And Heather says Seattle based midwife educator in clinician with a passion for midwifery and women's health. She strives to expand and diversify the midwifery and women's health nurse practitioner workforce address inequities within health care and improve perinatal outcomes. She currently serves as the assistant program director for the nurse midwifery women's health nurse practitioner and women's health midwifery programs at Georgetown University School of Nursing in Washington DC. And clinically she's been a midwife since 2002 and provided full scope midwifery care at Evergreen Health in Kirkland, Washington, where she's now per diem. She has attended almost 800 births and I'm going to skip a little bit here. She's currently pursuing a PhD in nursing science from the Vanderbilt University School of Nursing studying why birthing persons with higher body weights are more likely to birth via C section, cesarean sectioning, focusing on weight bias among nurse midwives. And her doctor on her doctoral committee is Dr. Jeremy Neal who is joining us on this call. So welcome to there both. And now I am going to turn it over to Heather. I can get my, I can get my, here we go. Make presenter. Great. All right. I'm going to mute myself. Take it away. Great. Thank you so much, Catherine. It's a real honor. What an exciting day for midwives. And I'm just tickled to be here. A lot of people in the room that are near and dear to me. So thank you so much for being with me this morning. It's kind of a gloomy morning here in Seattle. It's pretty gray, but this room is very bright. So thank you for that. I want to first just acknowledge funding for my PhD research by the ACNM Foundation, March of Dimes, Sigma Theta Tau, Sciat Large Chapter and Vanderbilt University. And also a big thanks to the American Midwifery Certification Board who agreed to send out my survey to all CMs and CNMs in the United States. And then of course, to my dissertation committee, Dr. Neal, who's with us this morning, Dr. Philippe Dietrich and Dr. Poole. And I just also want to take a pause. I've talked a lot, given a lot of podium presentations about weight bias. And I think it's important to just recognize sometimes this can be hard to hear when you're thinking about perhaps experiences yourself of experiencing harm in maybe the health care setting or in other settings, maybe in school or in your workplace. But also as practitioners, you know, we all have bias in some way. And I think it's important to just recognize sometimes we have caused harm. And I want to just start with everyone has bias. And our goal is really just to mitigate that and think about how we can do better. So that was really the impetus for my PhD studies and what I'll be talking about today. I want to first just start with some definitions. Weight bias can be thought about in two different ways, both in implicit and explicit ways. I'm going to be really focusing on weight bias as thinking about devaluation of someone who lives in a larger body, so someone with a higher body weight. You can have weight bias towards someone who lives in a smaller body, but the focus of my research has really been on what's more prevalent in U.S. culture of bias towards someone with a larger body. So as I mentioned, it can occur in the workplace, educational settings, media, interpersonal relationships. And I also want to just say that the sort of the assumption, the unconscious experience of weight bias is that you're assuming that someone who lives in a larger body, it's really a matter of responsibility and lack of self-care, that it's really willpower that has led to them looking at the way that they do and personal decisions. But what we really know a lot of has been studied and many by experts in this room that it's much more complex and multifactorial, that there's a lot of different factors that lead to what someone looks like in terms of their body size, metabolic, behavioral, cultural, familial, environmental, and socioeconomic factors that all play in there, including racism. So I want to just call that out. And that's a whole separate talk that we won't get into, but I just want to lay the groundwork there. OK, so I want to just start with some definitions. The World Health Organization and National Heart, Lung, and Blood Institute have categorized these particular categories on the screen here using the Body Mass Index, or BMI. And so the focus of my research has been on focusing on people with a BMI of 30 or greater, which is considered the obese category according to these organizations. And so I want to just first share that that was the focus of my studies, but you won't hear me use the word obesity at all or see that word in my content at all. The word obesity actually means to eat oneself fat. And it's considered in terms of people that live in larger bodies, the most offensive of terms. And so I'm really trying to move away from what is considered harmful in itself of using that word and using language that is preferred by the patients that we care for. So the words that I tend to use are higher body weight or someone who lives in a larger body. But I am focused on folks with a BMI greater than 30. I also want to just call attention to the use of BMI. BMI is not a good measure of health. It was really designed for measuring large populations of cis white men, and it's really not designed for measuring one person's individual health status. Unfortunately, we use it as really kind of the fifth vital sign when we walk in and see a patient, but it doesn't take into effect adiposity, bone structure. It doesn't really measure any of that. And so I just always suggest we give pause when we use BMI as a measure of health. But essentially, in the world of health care, it's what we have. Obviously, we need to find something better. But for now, that's sort of how it, you know, one of the measures that's used. But I just want to call attention to that. OK, so just giving some history, you can notice the red line over time. Essentially, in the US culture, our rates of BMI are going up. The black line is someone who is considered, quote, unquote, normal weight. And again, I'm very cautious of the use of the word normal. But you can see that number, that black line has come down. So not surprising statistics there. So just a quick overview of the harm of and what we know about weight bias in health care. It has been studied a fair amount in the health care setting, but not very much in perinatal care, which is really what my focus is. But in terms of broadly within the health care setting, we know it's been measured and found both implicit explicit levels in many health disciplines. It's been studied among nurses, physicians, dietitians, nutritionists, physical therapists. There's been studies of prevalence of weight bias among physicians who specialize in care of patients who live in larger bodies. So it's essentially everywhere in terms of health care, but has not been studied in the perinatal space. We know that weight bias is associated with negative communication patterns, less active listening, less rapport. Providers spend less time with patients that live in larger bodies without, you know, very unconsciously. Clinical decision making can be affected. So all sorts of what is offered, not offering a treatment because of based on someone's size, even though there's evidence that says that it would be of benefit. So overall quality of care has been studied that weight bias in the health care setting strongly impacts quality of care. Consequently, patients who live in larger bodies have developed a distrust of the health care system in many settings. We know we have higher rates of unplanned pregnancy in folks who live in larger bodies. Patient satisfaction rates way lower. I was reading a study about higher rates of patients who transfer care during their pregnancy because they feel harmed by their provider. So all sorts of problems of what we know about what's occurring in the health care setting related to discrimination towards people that live in higher body weights. And then there has been studies that look at lower rates of cervical and breast cancer screening again because someone does not want to pursue care. So they're forgoing a pelvic exam because of harm, like feeling mistreated, not wanting to have a mammogram. In terms of the adverse health outcomes, we know that when weight bias occurs in the health care setting, it contributes to patients who have increased food intake, less activity, poor body image, body dissatisfaction. All of those you can imagine are all associated. Low self-esteem, waking, depression, anxiety, stress, even suicidality and substance use. So we have a lot of work to do in improving the care that we're providing. Again, this is all in general health care setting, nothing in the perinatal space. What has been studied is we know that postpartum and pregnant persons are reporting that they are experiencing harm. This was a relatively small study, but 70% saying they experienced weight bias during their pregnancy or postpartum care. And we've talked about strain communication that has been studied in the perinatal space. Patients reporting, basically not feeling like they're getting the information that they need from their midwives and obstetricians about weight, weight gain, or nutrition. One really, really exciting report did come out. You're probably familiar with this birth settings in America. It was a huge, huge consensus study report by the National Academy of Medicine. And it had one little paragraph that talked about weight bias and how it can be associated with poor reproductive health outcomes. No studies have been done that have looked at that association. But for me, it was exciting that there was a call out to basically say, let's start to think about this. But as I said, it has not been measured. So here were the aims of my dissertation. I'm presenting preliminary findings today, not all of my findings. I plan to defend or hope to defend in June. So still working through some data analysis. I'm going to discuss today our goals were to develop an explicit weight bias among midwives in the United States. And to see if there was an association between those two types of bias, comparing them to other health professionals and the US public. Here is my conceptual model. This is really where I see the future of my research going. I'm really interested in decreasing C-section rates among patients that live in larger bodies. That's my ultimate goal. And really thinking about that statistic of, we know that women who live in larger bodies are three times more likely to have a C-section compared to someone who lives in a, quote unquote, normal with a normal BMI. So I'm interested in what is the phenomenon of going, what's going on during labor in terms of the care that we're providing, both unconsciously and consciously? What interventions are occurring that probably are not evidence-based? Are we allowing, excuse me, I shouldn't say the word allow, supporting physiologic birth, what's happening in terms of the care that's being provided? And my hypothesis is that we're contributing to unnecessary cesareans because we're not suggesting perhaps use of the tub as a form of pain management because we're not thinking, oh, I'm not sure this patient wants to get in the tub or I'm not sure I can help the patient get in and out of the tub or not supporting movement, ambulation perhaps, because in our minds we're subconsciously thinking, I'm not sure that this person has the stamina to walk around the labor unit. So I'm just gonna encourage an epidural. Again, these are things I'm really interested in studying but that's sort of what my conceptual model is getting at here. So in my PhD study, that's interesting, my table on the left did not show up, sorry about that. It was a retrospective cross-sectional study. I sent a survey through the American Midwifery Certification Board to all midwives in the United States, which is just under 13,000 midwives. And I solicited directly to midwives to fill out my survey last year at our ACNM annual meeting in Chicago. It was sent out via the listserv several times. And then I also used social media, specifically ACNM, our professional organization has a listserv called Connect. What was on the left here are basically the measures that I used. So I'll just describe those. I used for measuring implicit bias, I used the implicit association test or the IAT. If you're not familiar with the IAT, I would check it out. It's a free survey or free assessment that you can take. It's hosted by Harvard, it's called Project Implicit. So if you just put in IAT or Project Implicit, there's a bunch of different assessments that you can take, not just weight bias, but perhaps other forms of discrimination that you're interested in exploring about yourself. But I really encourage, it takes about eight minutes and it's free, so just encourage you to look at that. And then I used several explicit bias measures. One of them was called the fat phobia scale, one was called the anti-fat attitude questionnaire. I chose my explicit measures very specifically. I was interested in measures that used language that really got at willpower, endurance, self-esteem. So asking the question, when you think about someone who lives in a larger body, what do you think about their endurance? What do you think about their self-esteem? Things that I chose these measures because I'm thinking about treatment, how we're feeling about patients as we're caring for them in labor. So perhaps we're making some unconscious thoughts around, I'm not sure this person has the endurance to do this. So getting at that, those are how I chose my explicit measures. So here is my response rate, let me just make sure I didn't miss this slide there. Okay, great. So slide 26, here we are. Here is my response rate, 17%, just over 2,200 midwives filled out my survey. The median age was 46, median BMI 27, which according to World Health Organization CDC is considered in the overweight category, very geographically diverse and very representative of CNNs and CMs in the United States, mostly white, mostly female, almost 98% female. We do have just under 2% CMs in the country and that was representative here. Median number of years since certification 11 and then most of the respondents had an active license more than half, almost 70% were working full-time and then around 65% were attending births and of those who attend births, about 88% were attending births in the hospital setting. So I'll go through this slide relatively slowly, but I want you to start your, move your eyes to the top left under current study in total. So essentially this is one of my measures looking at implicit bias using that tool that I was discussing on Project Implicit and it is measured through what they call a D-score, which is from minus two to plus two and plus two indicates that you have implicit weight bias, minus two means you prefer, you have preference towards people who live in larger bodies. So I had again just over 2,200 respondents for this particular survey measure and the D-score among the midwives was 0.36. And I was interested in, how does that compare to other health professionals? So if you move to the right, there was a study of 2,200 physicians in 2012 done by Dr. Saban. And in that study, the D-score was 0.4. There's been ongoing collection of data through the IAT site of the US public and that very large study was just published in 2022 and reported a D-score of 0.48. So you can see that the midwives D-score, 0.36, was lower than health professionals and the US public. And in the statistics world, those were all statistically different, even though they seem quite close together, they were statistically different. What's interesting though, is if you just pulled out the physicians who were women, who identified as women, so now I'm in the lower middle box where it says 1,258. So of the 1,258 female physicians in Dr. Saban's study, the D-score went down a little bit of 0.37. And that is consistent with what we know that men tend to have more weight bias than women. So I thought that was interesting and I compared just the women in my survey. Again, it was mostly females who filled out my survey, who identified as female. And that was 0.36 still and then 0.37. So those were not statistically different. They're similar. And so that I think is interesting. But essentially what we now know is that female midwives have a similar level of implicit weight bias as female physicians, which I think is interesting. One might suspect, well, our midwives, maybe we have less weight bias because we're in the caring profession. It's our model of care thinking, perhaps that was the case, but what we learned is essentially, we're like other health professionals in that respect that we also have same levels of implicit bias. And looking at it in a different way, this graph shows midwives only of how much weight bias we had. And the D-score actually is put into three different, what they call cut scores, putting them into categories. So slight, moderate, or strong. And you can see the numbers at the bottom there. If you have a D-score of over 0.65, you're considered to have strong weight bias. And so if you group those slight, moderate, and strong together, it's around 70%. So what we can say is 70% of midwives in the United States have some level of implicit weight bias. And around 20% have strong levels of weight bias, which again, I think is interesting. Okay, now I'm gonna move on to explicit weight bias scores and what we learned. Again, I mentioned the fat phobia scale and the anti-fat attitude scale. So again, I'll start if you wanna draw your attention to that top left where it says current study.  And again, looks at things like endurance and willpower and self-esteem. And I wanted to compare that to other health professionals. And so if you compare that 3.24 to the 3.48, again, this was not Dr. Saban study. This was a different study among all groups of health professionals. So nutritionists and dietitians and physical therapists and physicians and nurses were lumped into those 878 people. And again, those numbers are very close, 3.24 to 3.48. You can also see US public 3.38. I suspect that number is maybe a little bit lower than the health professionals because it was different population, but essentially all very consistent, but statistically different. And then if you look at the anti-fat attitude scale, that scale went from one, I'm sorry, from zero to nine. And midwives measured on average 2.46 on that scale compared to health professionals 2.85. So again, what this tells me is that midwives have explicit weight bias, but essentially at weaker levels compared to other health professionals and the US public. And then a third explicit weight bias measure that I looked at was something called the preference for thin people. And essentially asked the question on a Likert scale of one to seven, do you strongly prefer fat people or do you strongly prefer thin people? And so you can see the current study. This is where midwives tallied in that column compared to the Dr. Saban study of US physicians from 2012. And essentially you can see that our rates are lower as compared to the US physicians. Specifically, I think it's interesting to look at the column where it says slightly prefer thin people, a little more consistent there, 26% compared to 28%. And then obviously bigger differences when you get into moderately and strongly prefer thin people. So interesting, I think. And then I looked at just females. So again, going with my earlier, if you've just looked at female physicians pulling out the male physicians that have higher weight bias than tend to have higher weight bias than women. Again, still statistically different, but a little more consistent there if you look at the slightly preferred thin people, 26%, 32%. So all similar, I didn't really glean out anything new there, just we are statistically different. But again, showing that midwives do have explicit weight bias at some level. So what we can say is a couple of broad statements. Midwives have moderate levels of implicit weight bias and it is similar to female physicians specifically, but lower than physicians or as a whole and lower than the US public. And we do have explicit weight bias, but again, our scores are lower than other health professionals in the US public. So moving forward, the next part of my data analysis that I'm interested in that I will be doing in the next bit of time here is really looking at how our weight bias among the midwives, how it varies by different demographic factors. So I collected race, ethnicity, age, BMI, years in practice and region should be on there. And I'm interested in looking at how does it vary? So this will, I think that's interesting because it will help us tailor perhaps our interventions. My goal is to help think about talking about weight bias with midwives across the country and how will that intervention be tailored? What particular types of midwives is it people who've been practicing for longer or is it our newer graduates who have higher levels of weight bias? Is it higher among white midwives versus black? Is it, it's interesting to think about someone's BMI. One might assume that someone who lives in a larger body would have less weight bias. So I'm really interested in diving into that data. And then moving forward after I defend my dissertation, my goal is to really move into clinical decision making and looking at what's going on in the labor process, what interventions are happening, specifically looking at some data set, a data set to see if there's an association and really hoping that if we can identify whether weight bias is a contributing factor to unnecessary C-sections in people who live in larger bodies. So that's where I'm headed in the future. So at this point, I am happy to take questions.  I can take questions in the chat or we can end early, whatever folks would like to do. No, I think people should have some questions and want to hear a little bit more about, I don't know how you untangled. I'm curious about how you untangled all this data. It just seems like an overwhelming amount of categorized and organized and all that. So especially the implicit bias part. Well, I have a great committee. I think that it's interesting to me, where I thought you were gonna go, Catherine, is the untangling around. There's so many things that contribute to, if you have a laboring patient with no risk factors who walks in to a birthing setting in spontaneous labor, what are the things that happen between when they walk in that space and when they end up on the OR table that could have contributed to that C-section and what happened that maybe they could have had a vaginal birth. And to me, the untangling that I think is gonna be challenging is how much can we say it was related to bias that occurred, specifically weight bias? I'm really interested in that because there's a lot of different factors, right? And the clinical management is the art, really, of the care that we provide, the clinical management decisions that are occurring. So that's really interesting to me, but I'm really hoping that I can have a small impact on perhaps elucidating information about ways that that might be impacted by weight bias. Absolutely. Well, I think you definitely identified a key topic, both for providers and researchers. Cecilia Jevitt says that she is not surprised that healthcare providers have biases that are close to the US population because we live in a culture long before we study health and this study begs for more education about weight and health for providers. Cecilia agrees and Megan Arbor says, well, can we brainstorm? What are your wild brainstorms about how to reduce implicit bias? What kind of interventions work for addressing them? Yeah. So, Megan, thanks for that question. I'm happy to share we wrote a team of folks and I wrote an article about mitigating weight bias in the clinical setting for general midwifery and women's health. It's not, hasn't been accepted. It's not off-depress or anything yet, but really what we wrote about was number one is starting with self-reflection. And I think that for a lot of people, when I talk about, hey, I'm studying weight bias, they're like, I never thought about that. Like, this came from when we were children, our parents inculturated us with, this started so early. If you look at types of bullying that occur in elementary school, weight bias is number one over racism or gender bias, this starts really early. And so I think the first step is self-reflection. And then really the next steps are just all the things that we do well as midwives, thinking about language, thinking about shared decision-making and partnering. The number of times in this work that I've done, people tell me stories. I went in for a sinus infection to be treated and I left being told that I need to lose weight. So it's just so prevalent of like people think they're doing someone a favor by saying you need to lose weight, but we know that recommending weight loss does not really work. It's about a lot of different things. And we talk about health at every size is our framework. But I just think that self-reflection and then just the things that we do well as midwives, applying it to these principles of shared decision-making language and just reframing how we approach our care, I think is really important. Oh, thank you. Well, Seal points out that there are actually international guidelines for quote management of labor for people with obesity, but from the US it does seem to push people down the path towards intervention. Grace, I was just to comment, but Grace Daniel, it's following up on interventions in the future, which I think you addressed. I was just gonna answer that, Katherine, if I could. My first step is really just to look at the evidence. Interventions is maybe five to 10 years from now. I feel like we have a lot to study because as I said, it's been, this was the first step to really measure weight bias. We didn't even have, it really has not, it has not been measured among obstetricians specifically. There was one small study that looked at 27 obstetricians at one hospital in the Midwest, but essentially it has not been studied. So we have a lot of work to do, but my goal is, yes, to move towards interventions, but that's later in my research. Sorry to interrupt, Katherine. No problem, thank you for addressing. Jeremy is interested in what is known about the presence of implicit bias, how it influences clinical decision-making in other, i.e., non-paranatal settings. So primary care basically, is it similar, different in primary care settings? Yes, thank you, Dr. Neal, for that question. There have been studies that have basically shown, you're given a scenario, here's the patients that are presented and then what treatment options are being recommended and clinical decision-making was impacted by the patient's size, even though the treatment was completely unrelated to what someone looks like. So, yes, there is studies in that space. There's actually a lot that's coming out, of course, around racial bias and variations in clinical decision-making and treatments, but nothing in the weight bias. Just very few small studies on weight bias, I would say just a few. And with Margaret, who is in the UK, mentions that they also do automatic, she says, referral testing for gestational diabetes. So I guess, would you consider that one intervention that might be related to bias or is it clinically appropriate, regardless of that? Yeah, so I think it's, yeah, that's kind of a complicated answer. My focus has really been on labor, but my understanding is, there are different guidelines that you can use. I think Varney's Woodwifery does say, if you have a BMI over X amount, then it is recommended for early GDM testing. That's the evidence that, what our students are being taught based on the latest edition of Varney's. But I think that does vary based on what guidelines you're using. All right, well, Ciel also addresses the labor issues, up to 50% of bias towards giving up on labor early. And then Susan, we're gonna have to wrap up here pretty quickly, but Susan Egglebert says when she was a family nurse practitioner, our Ann, the provider just told the woman to lose weight and her back wouldn't hurt. But you know, everybody has back pain. People have all sides of back pain. Right, the stories are the story, it's been really, you know, oftentimes, so we do a weight bias talk in healthcare with our students at Georgetown. And it's quite common that a student needs to get up and leave in tears because they've been harmed or they, you know, tell stories. It's just, there are a lot of stories. And I think that as Megan asked earlier, like what can we do, you know, again, just starts with reflection. There's, I see a couple other stories here that are also just as appalling. So yeah, lots of work to be done. Right, and let's see. Oh, well, who, two people, I guess Megan and Seal both introduced their experiences of midwifery educators and how we can and should address this with our students and also our physician partners who, yeah, I see that story from Seal that's about a physician said you ate yourself into this C-section, sorry. Joy compliments you on your fantastic work. Just be tuned for when Heather publishes, right? Thank you, yes, working hard. Yes, really appreciate you all being here today. It's really been an honor. And I would love to collaborate. I appreciate insight collectively in this room. A lot of great experts in the field. So thank you very much. Great, and Andrea, I hope I'm pronouncing her name correctly, is a member of the Obesity Medicine Association, which, you know, does, well, she recognizes that biases are ingrained in stigmatizing, I guess within that association and without. We talk about textbooks. Let's see, Helen talks about the education again, what we've been taught about health issues associated with weight and what studies untangle bias from real issues. And that's gonna be our last question because we do have to finish the wrap up the session. So last question, what studies untangle bias from real health issues? Well, that's a very complicated question I probably can't answer in this short time. But I think that we have a lot of work to do and I welcome collaboration. I think that, you know, textbooks are really, really important. It's, you know, and textbooks take a long time from the moment you write it to the moment it's published. So I just encourage you all to think about language that you're using, you know, ways that we're talking around, you know, care of patients that live in larger bodies. Text, I'm happy that in the next edition of Varney, there's gonna be some therapeutic communication content in there and we've tried to incorporate some content around weight bias in this arena. So, but yeah, lots of work to do.


Now let's continue the lecture of yesterday. Today we want to move towards what I call massively parallel spike trains. I would say more than 10 is almost massively parallel, but 100 also. And as you can imagine, this is so to say not straightforward to extend the methods we were using before. And I would like to suggest you a few which we worked out and which you may want to use for the data challenge. So to remind you, this is what I would consider massively parallel data. And the tasks you will have then for the next three days is, as I said yesterday, the data, the so-called data challenge, that you will have six data sets. And we give you this afternoon the features of these data sets. And your goal is to find this out, which is which. And we will have then a report on this, I think on Friday morning. And then after successful successful identification, you will get the real, you will know which is the real data, and you can also get the local field potentials to work with this on a neuroscientific question. Yeah. So we had this in the last courses kind of mixed, and this was not so successful. So, and we would also like you to form groups, teams, also today for solving these questions. And we have basically more or less for places where the groups can meet separately and discuss and so on. I think this is important to exchange each other. And you can already have this a little bit in the back of your mind with whom and so on. Then the group could go in this room. Another group could go in this room. One group could be here and one in the back, or you only form three groups. But I would strongly suggest to work in groups. More brains are better. Okay. And so we will now go into this massively parallel data. And a question which comes immediately up when you start thinking about this is do we have, do we deal only with pairwise? So these are four neurons. These are sketches. This is time. This is the four neurons in parallel. And now you can think of different kind of correlations between these neurons. So for example, in this case, these neurons always all have pairwise correlations. They are all pairwise correlated. And this may happen, but you may have also only triple correlations. You see this in terms of, it's a sketch, a very, you see that all, there are constellations, different constellations of three neurons synchronized, synchronized activity. And basically you have different types of triple correlations. Or in this case, all these neurons are firing synchronously together. And why do you want to know that? Well, I would like to know that because I would like to know which neurons are forming an assembly. So here I would say all these four form an assembly, but here I would say it's different groups of three which form assemblies. And I would like to distinguish this from a mere pairwise correlation. Or in other words, what I also want to say is in case you look just as pairwise correlations and notice that you find as here a case of four neurons all pair to pair, pairwise correlated, this does not necessarily mean that a exhibit all together a higher order correlation. Now I'm a bit sloppy with my definition of higher order because the mathematics, there are some approaches to do this, but what I mean is sort of say correlations, let's say of four neurons, which is not explained by pairwise or triple wise correlation. There are mathematical frameworks which we tried. Cumulants, for example, which are so to say mathematical way. These are kind of higher order features of data, but this becomes immediately so complicated. Even for a correlation of four, you have that terms and it's very hard for the amount of data we have available typically in experimental data to approach this in the sort of say strict mathematical way. This is why I suggest different methods, now it's becoming a bit fresh, I'm sorry, which are not that, how should I say, correcting for lower order correlations, but indirectly. Okay, so what I also wanted to emphasize that when you want to generate or design a method and you want to build on the pairwise analysis method, for example, unitary events or so, or no, this is something else. I wanted to say that if this, but I said this before already, that if you want to conclude on higher order correlations, you cannot do this from only pairwise correlation, you will miss it. But what you need to do as I said, higher order correlation analysis, and the difficulty is that scaling up, for example, the unitary event analysis to a large number of neurons leads to a combinatorial explosion of parameters. So if you want to see all different constellations of neurons across this 100 parallel recordings, you have two to the power of N, N equals 100 neurons, this would be so, so many, many, many different constellations. And for this, you simply don't have the data available.  If you want, for example, to test for the individual patterns, we touched this issue already yesterday. This means if you ask many questions to one data set, for example, if you would be interested in testing for each individual pattern constellation, but have a, for example, a significance level of 5% or so, due to the number of questions you are asking, you would get anyway, 5% significant. And this, and the more, of course, the more questions you ask, the more significant results you get. This, of course, you don't want. So first of all, you want to avoid massive significance testing on the same data. And second, there are correction methods which then, so to say, reduce the significance level in extreme Bonferroni that you divide the significance level by the number of questions you ask. So for example, if you ask 1,000 questions and you have a significance level of 5%, then the significance level is so low that nothing will become significant. But this would be the super strict method, but there are also other ones available. And so we started about 15 years ago with development of statistical methods to analyze such massively parallel spike trends. Now, the first approach actually suggested to me by Wolf-Singer was, he said, why don't you just look at the population activity? And I started to think about this. And actually, it's a quick way of looking at data to get a hint if there is higher order correlation available in some sense. And then you can later on dig into this and focus and try to find out which neurons and so on. So this is based now on a paper I had actually my only paper I have with Moshe Abeles. So I can also provide this to you. So what we did here, so this is only simulated data to make, to really know what we are doing. So these are, for example, 100 parallel spike trains simulated as Poisson processes with a given rate. And in addition, we inserted into 20 of them, not in addition. We inserted into 20 of them. You see here these highly synchronous events and compensated the rates of the background, correspondingly such that all of these neurons had the same fiber and probability. If you randomize the neuron IDs, you can hardly see these events. This is something you should realize because it's not something that you look at it and you see it immediately. And what we mean by population histogram is to bin the time axis, let's say in one or five millisecond bins and sum the number of spikes across the neurons. This is what I mean by a population histogram. And then what you can see here easily, you have when the mere independent background, you get relatively low entries here, but here and then when we actually meet this higher order event, we have a high entry. Super. And if you have independent data, which you could in a, if you have a stationary data, you could simply randomize the spike times, then this looks like this. Okay. Then we thought maybe we should use, we should then look at the distribution of these entries and we call these entries complexity and we form the distribution of all these counts and compare it then to the independent case. We learned yesterday you can make your data independent in one or the other smart way. So this you can then do always and it's a simple computation. No, I don't think so. So what you now see is this is the distribution normalized. This is sort of say the probability to find a certain complexity in there. And what you mainly see here is that you have a peak there on low complexities, but the inserted, the inserted synchrony of 20 is not, kind of does not appear in detail. If you look at the independent data, this looks more or less the same by I at least. But what we thought we could do is to take a bin wise difference, a bin wise difference and then this appears like this. So here is actually what we expected somehow to see. These are, so to say, these core desynchronized events of we inserted 20, but what you probably see is that the peak is a bit higher at a higher value. Why is this some idea? Maybe I show you the dot display again. So in this 20 we had inserted these events of 20, but why do we get maybe sometimes 21, 22, 23? So these inserted events may by chance meet background spikes. Yeah, exactly. So this is why this is a bit broader. Now why does this look like this? Why do we have here a negative peak? Exactly. Exactly. So because this is normalized, there is a probability distribution, the spikes that are here have to be taken from somewhere or are taken from somewhere and this leads to a negative dip. And what I can tell you from experience is that mainly this is often very hard to see, but if you see something like this, you know there is something going on. There is some higher order correlation going on. Now, yeah, it's an interesting idea. Yes, yeah. I see what you mean. Yeah, it's a good idea. We can think about whether this is helpful and how we can use this. Yeah, that's an interesting idea and you are welcome to implement this. So I, yeah, and show me what you found and so on. Yeah, of course. I just want to tell you for this simple case, Poisson and so on and insertion and so on, there you can have and you find this in this paper, analytical description. This is mainly dealing with, with binomial distributions and products of convolution of binomial distributions. If you do it just to get the idea. If you reorganize this, this is what we actually did. We gave these neurons the same firing rate and what we did here is that we reduce the background activity by the coincidence rate. Yeah, to have all these neurons also the same rate. And now to make a calculation to derive what we would expect, where you can reorder them that you deal these segments differently. This is, this is basically the idea because it's independent of when it occurs in time. Yeah, it's just to give you an idea how you can approach this. Okay, what can we do with it? So now we come to this, to this issue of Chittor and Ditter. So let's assume you have data and you want to know, for example, whether, whether there is higher order correlation involved than or off what, what order. What you can then do is, for example, vary the bin size. Yeah, perform a complex, this difference of the complexity distributions for a bin width of two and three and four. And this is here now shown in a, in a color plot. Yeah. So, and what you can see here is that this positive negative from the beginning is well separated from this feigned entry here. And at some point, they kind of run together. Now, so you can try to detect the order of the correlation from, from the second line. If you actually want to also identify the temporal Chittor of your data. So if there is an imprecision, let's focus on this. Otherwise, it's becoming too difficult. I did not explain all the rest.  Okay. So if the group, you have, you have inserted a group here of 20, as we had before. And the data had different temporal Chittors like being synchronized with, within one millisecond, two, three and four. So now if we vary here, the bin width for the analysis, we will notice somehow if the second of the second peak and a kink. And the reason for that is the following. Let's assume you increase more and more the bin width. You start to collect more and more of your, of your higher order events somehow and by chance. And then at some point you capture, you capture the inserted events. And by further increasing the bin size, you are only collecting additional background activity. And this is why here, the slope is changing considerably. Meaning, if you are able to identify this, kink here, you can tell what the temporal Chittor is because you know when you have captured all the events and also the group size or the complexity of your data. Give it a try and let me know how it looks like and how much you can derive from this. Very easy. It's not so time consuming for computation and for the tasks you will get this afternoon. It may be helpful. Okay. This is what I said. Simple measure, easy to compute. You surrogate for implementing the null hypothesis. It can give you an indication of existent correlation and group size. And you can use a variation of the temporal, of bin size to provide the temporal Chittor. Now, now we want to get a step further. We want to somehow scale this up and identify the neurons that are involved in significant patterns. And I learned at some point from a computer scientist. He was talking about a supermarket basket analysis. I don't know if you know about that. So people somehow, the supermarket seemed to be interested to know what people buy together. So that they buy typically wine and cheese and some bread together. And this they use to place the product in the supermarket. That is easier for you or that you buy more. And we were able, together with, his name is not here because he is one of the last authors, Christian Borgelt, to make use of this approach to products in a market basket analysis to a spike train analysis. Because then I said, I mean, we have a similar problem if I consider each neuron as another product, so to say, as another identity. And then the set of products would be the set of neurons which are active at a certain time bin. And so we were able to apply and translate this approach, which is called, where is it, frequent item set mining to the spike train analysis, the synchrony pattern analysis of parallel spike trains. So effectively, this frequent item set mining is an efficient way of counting different patterns. Mining is just counting. When we have counted them, we still don't know whether these are significant or not. So this is the pre-step. And what we do then, in order to perform our significance analysis, we do the following. So from such a data set again, this is again a simulated data set with 100 neurons and 20 neurons being synchronized. We perform, we apply the frequent item set mining to it. And then we get a list of a lot of counts. And this we enter now in this particular histogram. It's a bit difficult to explain. So what we do is we categorize each of our patterns we found into their size or formally called it complexity. So the number of spikes involved in a pattern and their number of occurrences. So you may have a pattern of neuron one, two, three occurring five times. And I don't have this number of spikes because my bin size is small enough. Or if I do my bin size, make it a bit bigger, then I clip it. Because if you start to have different counts, you can do this. But then your patterns become really more difficult. Then it's not only neuron one, two and three, but neuron one having two spikes and neuron three having five and so on. Then your space explodes much more than here. But if you are interested in something like this, we need to think about how you could do that. But here we typically have one spike per neuron. Yeah. Now what we do is, so for example, we said occurring five times, right? So what is going to enter in this bin? In this bin, we count how many patterns there are of size two, of two neurons involved occurring five times. So we don't count how often a certain pattern occurs. But if a pattern occurs with size two and occurring five times, this adds to this bin. And the reason why we build up such a matrix where we pool certain patterns is because of this multiple testing problem. Because in the next step, we want to know if an entry in this matrix is occurring significantly more often than expected by chance or just by chance. Yeah. And we need to ask less questions about 10 by 10 or 10. But still, I will come back to this. We will also correct for multiple testing. Okay. Now, how do we want to do the significance analysis? Well, we do it quite similarly as yesterday. We generate surrogates from our original data and do exactly the same analysis on these surrogates. Exactly the same. Multiple times. In order. So in here, in this case, we we apply a little again. And what we then get is a matrix, which gives you sort of say the probability to find patterns with occurring five times composed of two neurons with a certain probability. And you see the dark part here means, well, they anyway occur by chance. Yeah. But if you are here in the whitish part, the probability to find them by chance is extremely low. Yeah. That's the philosophy. So basically what you can see is by chance, you get anyway patterns which are of low size and relatively low either low size and may occur a bit more often or vice versa. Now we compare the entries of this empirical matrix to this and ask for each entry. Is it occurring more of significantly more often than giving significance level of 1% or so? And also correct the entries with, I forgot the name, not Bonferroni, but another correction due to the multiple testing and then we get a result like this. Yeah. So now here we have read entries which show these bins contain data which occur significantly more often than expected by chance. Now we are not completely done here because what we inserted here were patterns of a complexity of 10 occurring that many times. So what are these others here? Yeah. So these others are patterns which are actually composed of other pattern we included plus a chance background spike or sub patterns of it. And actually we wanted to get rid of this and we wanted really to get back the one which we inserted. And for this we added a particular step where we asked ourselves, well, this has the highest probability to occur. Can we explain the occurrence probability for this times if this pattern is explained by the occurrence probability of this times getting a chance background spike as well, then we say this is sort of say a false positive in a certain sense that it has a coincidence between the significant pattern plus a background spike. So it's a conditional filtering what we add in this case. And when we do this, we end up then with our inserted pattern. But we had a discussion with Moshe the other day and he said maybe you don't want to remove these. We can discuss about this and maybe I come back to this point why we discussed that after. You need to first know which are the patterns which have a high probability beyond chance to compare this mutual conditional. And you would have much too many. Okay. No. Only then we say this, so we look into this bin and then you will notice anyway that you because there will be only one type of pattern composed of yes because by chance they don't occur. Yeah. So this is our experience. Yes. That's true. That's true. That's true. Yeah. It could be. Yeah. What was your question? No. We would only extract here a pattern composed of 10. Well, this pre-step, this frequent item set mining is only extracting the biggest patterns. Yeah. This is the pre-step. Okay. Just to give you a little bit of feeling, so we did quite a lot of calibrations of the method which we use when we do, when we develop methods, we test them for different scenarios with ground truth data. And here, for example, you see the effect of pattern occurrence for different firing rates, just to give you a feeling. So this is of course then also considered in the p-value in the significance test. You see that the higher the firing rate, the larger is this area which of patterns to be generated by chance. Yes. So we have tested this as well in homogeneity across neurons. And our impression was, or what we tested was that this is not affecting the method. Yeah. But you need to make sure that your surrogates have the same in homogeneity. Yeah. Otherwise, you change more features of the data than the one you want to test for. Okay. Now, I can show you an application. Yes, please. Different patterns were redundant. You meant this one. It's basically like this. If you have found a pattern, which is how I don't remember the color coding, if you have a pattern with has the highest occurrence probability of the significant ones. So for example, this one occurred six times. And the other had a lower probability. Well, but this has a lower chance to occur because it has only three spikes. You compare any two of these patterns, whether the larger one, for example, can be explained by the probability of this ten neurons times the occurrence probability of a background spike of neuron 28. Yeah. And if this probability, this multiplication explains the occurrence for this, that this occurs two times, then we say this is sort of say a false positive composed actually by the other one. And you can do this also for sub patterns. Yes. I agree, but sometimes you only have the longest, but also shorter ones, which then survive. But the probability to occur if you count a certain pattern's length is then relatively high if you anyway have a pattern, a pattern, a significant pattern. You cannot only remove this. This is a matter of the counting of the frequent items at my language leads to this. Yes. Because I don't know. Good question. I don't know in the moment. So I wanted to show you an application of this method. The method is called spade and it was published basically mere. This was the mere method and here we, this is the application to the experimental data. So we used, I think in total, we used in total 10 sessions of two monkeys in the task you got already introduced. Again, actually the monkey was supposed to do a different grip with a different force. And the experimental protocol was that the monkey initiates the trial start himself when he's ready to go. Then he gets a cue with the information given by the constellation of the LEDs lighted on either precision grip or side grip. Then he has to wait for a thousand milliseconds. And then he gets in with the go signal also the information of the with what force he has to pull then the object. Then he releases a center key. This is basically the reaction time and then moves its arm to the object. This is the touch of the object and then he pulls it and holds it in a certain position. We wanted to know whether, whether there are sort of say patterns occurring synchronous patterns occurring. Are they specific to different behaviors and so on. And what we did here is because the spade method because of this many surrogates is quite computationally expensive. So we could not do directly as for the unit event analysis, a sliding window approach, but we segmented the data into relevant what we thought behaviorally relevant periods. So around the start around the cue early in the delay late in the delay during the movement aligned on switch release and during the hold of the object. And these segments are all 500 milliseconds long and these are sort of say separately applied for the different behavioral conditions. Side grip high force, side grip low force, precision grip high force, precision grip low force. And for each of these conditions we have about 30 trials. So when we analyze the data we take for example the period around switch release of PG low force from all the trials and analyze this together as one data set. Meaning we have in total of one experiment of 15 minutes we have four times six data sets which we analyze according to the segmentation and extract for each of them the patterns if we find some. This is how we do that. Now this I told you already. Now here you see an example of one session what kind of patterns we found. This is from the whole session and what you see here now it's also you notice already patterns are you need to deal with visualizing them and so on. But what we see here already is that for example the pattern zero is composed of this neuron and that neuron. Pattern one of this neuron and that neuron. And pattern two is composed of that neuron and that neuron and so on. And what you realize already is that these patterns are much smaller than I assumed in my simulations. They have maybe two at most this one three spikes involved. And I will come back to this why this may be the case. But these are in general the patterns we found for example in one session. Yes. First of all you cannot necessarily conclude whether this is a monosynaptic. But to be honest I didn't look at across Wait a second. Why is this relevant? Why would this be relevant for you? So I think they I still hope that they are coming from a sinfile chain which is so to say being a cell assembly but that our sampling is not so good. I come back to this we had a calculation on that. Let me continue a little bit. So and when you look at when these individual patterns occur in the experiment you have now you have now so to say how should I explain the different epochs the different trial types and when the particular pattern occurred and was significant. So what you can see here already that these patterns typically were significant and occurred at different epochs and typically also in different in different trial types. We then we did a lot of statistics on these patterns which occurred for example this is monkey L monkey N. So what we did here is as a function of these epochs and here you see for example the number of patterns occurring in different colors for the different behaviors occurring in the different epochs and one thing which is quite obvious here already and this holds for both monkeys that they mostly occurred during the movement period and it's the amount of pattern seems to be more or less equally distributed across the different trial types. Also the size of the patterns does not vary so much this is now statistics across 10 sessions of each of the monkey. The size does not vary so much so it's also not particular. The number of occurrences of these patterns may be relatively high but they are also not specific and then we also looked here is a better display at the occurrence probability of these patterns as a function of the distance on the array. We come back to this. But no. No. I was thinking and so okay so just to give you an idea how probably it is to find significant patterns as a function of distance on the array and you see that for both monkeys the occurrence probability decays very much with distance and basically on a distance of about above 4.4 millimeters this is more or less diagonal you don't find any anymore. So it's a matter of distance which you can imagine that this is natural because of the connection probability in space also decays however faster actually and single unit single unit. Okay now we were also interested is there a certain how do they occur on cortex so to say. So to put them back onto onto the array and see when do where do these patterns occur and with which orientation and so on and here again for two monkeys here is the general probability of finding patterns across the array here we put for each pair of neurons within a pattern the orientation where they occurred yeah on the array and if you and what we found is that there is actually for the patterns a preferred orientation on the array yeah so for example they are preferably maybe lateral occurring almost identical almost very similar for both monkeys and what we thought but could not test yet that this may be related to what Hadzopoulos was showing and others with these propagating waves in motor cortex yeah because we know I showed you yesterday a slide we knew that or we know that significant patterns seem to ride on a face of an oscillation and so this may be the reflection of spikes riding on this of this wave of the oscillations sorry I forgot to tell three milliseconds no I the idea was I think I had a figure which was so showing this this hypothesis sorry I forgot to put it here um no the idea the idea would rather be this that if you have waves going like this ah sorry if you have waves going in this direction on the LFB that we see so to say spikes riding together on this that they are synchronous on on a so to say a high peak of this oscillation that's so to say the guests but let me go a little bit further we were interested this was a question before we were interested whether a pattern is specific to the behavior is a if if there were groups of neurons um particularly processing a certain behavior we would expect that they exhibit these patterns which are specific to behavior that's the philosophy and so we made um we calculated the specificity and it gets a value of one if a pattern occurred only in one epoch and in a particular behavioral condition or if they are distributed in different epochs behavioral epochs we give a specificity of zero and this is the analysis of it again the different epochs um the number uh the probability the the specificity of a particular of the particular patterns and for example this is pretty clear that that these neurons seem to be very specific to the precision grip uh to the grip type not so much on the um on the force level this is why when we look at the combination of of uh grip type and force type that this is going lower because this is reducing the specificity but during um during the uh the trial type we noticed that they are highly specific in particular in the later part of the experiment so it seems the different assemblies are active for different behavior okay that's our conclusion um however you could also ask well some of these patterns were overlapping quite strongly yeah for example there were comments by common neurons in these patterns um and we applied a kind of a cluster uh on cluster those and then looked at their specificity yeah so we were asking is this actually a bigger pattern do they belong to the same assembly but are different expressions and when you do this you notice that the specificity is going down considerably meaning uh these uh they are not coming from the same assemblies that's our conclusion here and why is the patterns size approximately only two for synchronous patterns well um we assumed a sunfire chain one sunfire chain in the volume below the array which are about how many one million neurons and sampled about one hundred neurons randomly from there in as these were the situation when we um when we sampled with a uter array and we assumed um we assumed a sunfire chain one layer of a sample from synchronous activity one layer of a sunfire chain so we assume that there are different that there is a sunfire chain and if you find synchronous activity you would expect that you sample them from one layer yeah uh or one group and um we assumed to have that many and um and uh that many per group so the then you can calculate that the probability of sampling three neurons from one group versus two uh is 300 times smaller so the probability to to randomly detect uh from such a group two neurons is much higher than uh larger ones on the other hand uh the statistics of spade is relatively conservative so um and another point is that um I wanted to do not want to express this so strongly but and I could give another talk on artifacts um and uh we don't know where they actually come from but what we did and I don't tend to have a figure now for this but I noticed um at some point different strange phenomena in the correlation analysis and then I asked my people to go back and look at the original time resolution of a 30th of a millisecond and make a population histogram for that and then you see high peaks in there but this can't be neuronal spikes have a duration of one millisecond at least so what we did is we could not find out what is happening on the setup and could not remove them so to say in terms of the setup probably it's some crosstalk but what we did is we first form did this um population histogram and removed all spikes which are in a synchrony of more than two of two and more in case to remove to remove these artifacts yeah and this may have contributed also to low complexity because we may have also thrown away good spikes but I felt better like this okay where are we okay I wanted to let you know but don't want to go too much into this but I wanted to let you know that we also extended the spade method to um two spatial temporal patterns meaning that you cannot only detect synchronous patterns but also patterns with a certain delay between the individual contributing spikes um I hesitated for a long time to do this because the problem explodes but we somehow after having developed spade we had some somehow the mindset changed and uh we were actually able to extend spade to this I just want to give you again I should have brought this figure before much earlier um so why would I expect to get spatial temporal patterns if you think that um at that uh cell assembly is somehow organized in this as a synthfire chain so Moshe Abelis uh invented this concept 89 uh 1982 and 91 there are two books which we have here as well the idea is the following given the connectivity we find in anatomy of the cortex you have as I said you have a high divergence from individual neurons to other ones and each neuron is receiving a lot of convergence from different neurons and if you now do this for groups of neurons in different layers you can define so to say groups with um strong divergence convergence between them and it was known and shown in in studies by Moshe and also others that if you stimulate the first group for example synchronously that this very stably propagates as synchronous activity through this network very stable um a synthfire chain is a very so to say a very strict model of that kind you can relax this also a little bit some people call that synthfire braid which is you don't require to have all these neurons firing synchronously to that uh that a receiving neuron is getting synchronous input but they could be sent out at different points in time but have compensating run times so you could assume different delays between the neurons which are then compensated by different initiation of the spike but still arrive synchronously at a receiving neuron and this concept was for example put forward by bean in stock or much more known unfortunately and he also didn't cite bean in stock by it's a giveage um they are of course you would also find spatial temporal pattern and preferably spatial temporal pattern because you have a delayed distribution here and you would not see any synchrony in that sense anymore yeah okay and um we were lucky um that we were able to extend uh the spade analysis to also detect spatial temporal patterns this took us about three papers that we realized that the approach we first took is actually equivalent to frequent item set mining I don't explain all this now but formal concept analysis in our application is basically equivalent to frequent item set mining and so we we could use also our whole machinery more or less identically but just feed in the data differently so instead of looking for synchronous events we first search for for some patterns and then reformat um these data by concatenating these of one window search window together and then we are back to search for synchronous events that's the rough idea and um however what we had to do in order to detect um spatial temporal patterns correctly um is that we had to extend this spade matrix to a 3D version because um patterns of different durations temporal durations have different null distributions and so when we when we pooled patterns of um let's say the same signature like three spikes occurring five times however of different durations this led to uh false positives but we did this we inserted this and um then uh we for when we only used the 2D spectrum we had problems of finding really these long patterns because they are more rare for a certain allowed duration than shorter ones and of course you have to build this in your um um your you have to consider this and then when we adjust to a 3D spectrum which is then number of spikes um their occurrence and the duration of the pattern if we then have these cubos and test for the individual um pick what is this now uh huh voxels then we exactly find back the patterns what we inserted i just wanted to let you notice that this extension exists i can already tell you that for the data challenge you probably don't need that but you can of course play with that um we also applied this in the same way to the data and indeed we found spatial temporal patterns you see here the result for one session of 15 minutes and we find the same phenomena that we find most patterns during the movement period here is their uh their complexity or the number of neurons involved they are now going up to six or so um um but a phenomenon and and i want to show you here one of these patterns which i like very much to see so this is for example a pattern one two three four of composed of five neurons and these patterns occurrences are aligned here on the first spike this is why they are on top of each other and then you see the second and the third and the fourth and so on and they have some temporal chitter because we allowed for three millisecond chitter of of such a spike in the pattern um i'm not sure if i understand so we apply for the search a window which moves it attaches to a spike and and then extracts whatever there is yeah then we do the the statistical analysis as before and the patterns we find are within the temporal precision identical neuron one is firing at a certain time so as i showed you here for an example oops so neuron one is firing then neuron two within the certain temporal chitter and so on yeah they repeat identically and they and what what you see here as colors is showing different trials because we wanted to see are they distributed more or less homogeneously across the trials they do yeah so this is one type of pattern and in this particular data set we even found we found 39 different patterns two of two spikes fifteen of three eleven of four eight of five and four of six these are occurring during a particular uh epoch in a particular behavior yes no i didn't i didn't but this method is available freely available open source an elephant so people could do this um i am a bit hesitating to apply to look at hippocampus data because i i and i'm in general hesitating of downloading data and applying it because i know you need to know much more about the data than just throwing a method onto it but i agree people found their these patterns and also are different um also slowing down and this type of things it would be this method would be extremely well suited for such analysis yeah one more thing i wanted to show you a puzzle we did not solve yet um now in for these patterns for all the patterns here in total were distributed uh or here on 10 neurons so only a small fraction of neurons actually contributed to this to these patterns and even more surprising and i really don't know yet what this means that this is now a plot like the unitary events this is one um one epoch aligned on in this case which release one neuron across multiple trials and this for the different 10 neurons and we inserted a square around the spikes which are involved in a particular pattern at a particular time so each color means a different pattern yeah so here for example you see there are only pinkish squares there somewhere there needs to be problems for this or here are only red spikes maybe with this one maybe another one but these many of these spikes here have multiple squares uh meaning an individual spike must be involved in different patterns and this is still uh kind of confusing for me and we need to find out what this actually means maybe we are actually dealing with much larger patterns and maybe we need to merge these patterns on top of each other and see maybe clearer an expression of sinfire or whatever assembly activity yeah um in principle i agree but um so this what however what i kind of had in mind is well i have an assembly and then this set of neurons are is composing the assembly and so i would see only patterns from those and then another set of neurons which is somehow i intuitively thought they need to be um separate yeah but obviously it more happens what i thought is that um they seem to multiplex just a second go ahead well you could try to and this is what people do reduce calcium imaging to kind of point processes time scale is different you need to be aware of but i think it should be worth to apply it there are more questions so what what's your point i think so which paper is it interesting okay that's interesting paulina i think i said this didn't i ah okay yeah thanks yeah they are both phenomena that individual neurons are for example this one this has not this has many different color spikes this seems to like like a kind of a half neuron being involved in different patterns but not the individual spikes but this and this has spikes involved in different patterns yeah i i i agree but this all does not i mean if you come i'm i'm long enough in this field to have experience this fight between rate and time temporal coding yeah and people the rate fill rate people are still very strong and this would not fit into their philosophy at all yeah yes this is what we think is happening and this is what we are going to do next to to merge them and to see whether these are actually composing a bigger pattern yeah can we go on a little bit i want to show you one more method can you still concentrate it's hard for me but okay then um yeah it's a good question we don't know yet it's a very good question because somehow you lose this perspective here because you need to um but yes you're right that's a good question whether there are neurons which are always sort of say starting a pattern or yeah yeah something like this okay so i i realize you are tired i want to then i show you just a picture that you get the idea what we also have in our package and um which you may have applications for and i will not explain this in detail to you um so we were having i have always in mind the sinfire model and if you were able to record all neurons from the sinfire model then you would probably see something like this so different neurons time and if such a sinfire chain like this is active and you record many neurons per group then you would probably see sequences of synchronous activity so here assuming you have a a more complete sampling yeah and if the sinfire train is activated again you would see this this activation of of these groups of neurons one after the other again and um um this is actually a simpler task in a way as looking for spatial temporal pattern spatial temporal pattern would be so to say a sparse sampling of this but here you have better chances to detect this and um a simple approach which we started to develop already 2008 um is that what we could do is the following really easy to understand um we take this time axis and again bend it in the potential width of these synchronous groups let's say three milliseconds and now we compare any any of these the contents of these bins whether we have overlapping neuron IDs yeah and in particular if i would have a bin here and a bin here comparing then the overlap of the active neurons is pretty high right and what you can do is insert this neuron overlap in matrix which compares any two times bins uh bins in time yeah so for example um um for example this here would be the comparison of this bin at t2 and this bin at t1 and we would find a whole uh strong overlap and if we had such a feature that we have propagating synchronous activity then also the next bin keeping these uh detecting these neurons and these neurons neurons would uh have a high overlap resulting effectively in diagonal structures within such a matrix with a high entry yeah that's the old this that's the idea behind and what we did in in this method um published in which we called asset um uh published as torret al is basically al is basically finding a way to automatically identify such diagonal structures in in such matrices this involves the number of steps but i um i this is just the idea i wanted to give you and you are welcome to apply this method as well but i will now skip the details okay um um discussion i showed you different ways of approaching such massively parallel data what one was the simplest using a population histogram looking at the complexity distribution and compare this distribution to the one of independent data it's easy to compute and it provides at least an indication of a presence of correlation uh a method i didn't show to you is cubic um which is um which can infer a lower bound of higher order correlations it just tells you seven then you know you have some correlation of at least seven then uh i showed you um uh the the method spade where we can detect higher order synchrony or spatial temporal patterns where you have the neuron ids resolved which are participa which are these patterns and also their time of occurrence and i hint hinted to you to the method uh for detection of sequences of synchronous events by asset for which holds the more neurons you observe the better is the detection and this is more or less what i wanted to tell you here is um um a kind of a review paper where you can so to say look at the comparison of the different methods and here i want to thank the people of my group thank you very much


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So, given the estimate time and you can build the perfect product, so a little about me. Right now, I'm working in the Goal School and I'm the founder of Goal School. We teach Ruby on Rails and I'm also the organizer of Rails Taiwan and the last Pacific, I'm also the organizer. Right now, my full-time job is the kind of software development coach and also my primary language is also Ruby on Rails. I started using Ruby on Rails since 2007 and I am also a hacker. Speaking to a hacker, so you may think, why can I call myself a hacker? I may not like a hacker, but I used to work at Hexon in 2012 and I am the world champion. This is real, this is Mark Zuckerberg, not a Photoshop picture. This is tech in the Hex square. So, that makes me kind of qualified to talk about this topic, how to build a product in very limited time. So, first, speaking to Rails, what can cross your mind? So, what would you want to enter the Rails world? I think the main reason is like, you think Rails is fast, so you want to run Rails so you can work all over time. But when you finally enter the Rails world, you find out you still work overtime. So, too many jobs and burnouts, so why? Because Rails only guarantee you can build things very fast, but it didn't guarantee you can get the right project direction. So, today's topic, so it's not about like how exactly about building Rails with agile approach. Instead, I will talk about how to win a Hexon, for example, to teach you how to do the right project management and how to make you a better Rails developer to deliver the project on time. So, speaking of how to win a Hexon, so how do we think how to win a Hexon? So, the answer is probably very, the answer probably is you build the great PowerPoint and then you can win. So, many people are very angry about this, but this is true. So, typically, a Hexon winner will come out with following results. The number one is like you have the great pitch deck. The second one is you have a right product and the product's purpose is good for press release because like if a bank throw out a Hexon, you may probably the first place will probably be like a mobile Hexon and can pay something easily, so the product must make their expectation. And also, you should finish the product, even with the prototype. So, it's kind of like when you want to build a product, what is the great product? It's like you also need to like a great pitch take, so you can tell your customer, your scenario is your value and the project must meet MVP definition, minimal viable product. So, this product must like can quickly success and deliver the future on time where the market needs it. So, fighting a Hexon is kind of like you're building a great product in less than 10 hours. So, that's why I will choose today's topic, like building how to win a Hexon. For example, technical example to explain how to exactly manage your project. So, how do we win in 2012? So, the product we built in 2012 is we built a service called paperclip.io. This is the FBE share links bookmark service. So, we usually save a lot of links on our Facebook, we would like. But in the old Facebook, so after two weeks and three weeks, you will forget how, which amazing article you browse in several weeks ago. So, I think this is terrible. So, that day I built this topic to become our top project. So, this feature later becomes the built-in feature in 2014. So, if you use the Facebook now, they already have this feature. So, why we will win in four years ago? Because in late day, we have the perfect pitch deck and with perfect product and we built it and perfect in seven hours. So, how do we manage to do this? So, what we use rest of course, because if you want to win a Hexon, you need to build things first, things very fast. But I think why we will win is more about time management. Before this Hexon, I like bigger stuff. So, I tend a lot Hexon, but I always lose. Why I lose? Because number one, I didn't have the perfect deck. I built a perfect deck. So, every time I lose. Second is, every time I run out of time, so I have a crappy demo. And the final reason is like I have a crappy idea. I always build something. I think it's very cool, but the strategy is it sucks. So, how will we do in 2000 at Hexon? So, this time I really want to win because I think at that time I'm building the consultancy. I think if I win the price, then I can get more business. So, I must win this year.  So, I think what can we build this time? So, I find out the number one reason for me to lose is I didn't have time to build everything at all in the given timetable. So, I find out. So, why should I check how many resources before I start? So, the timetable is, this is a seven-hour Hexon. So, start at night and end at 6 p.m. So, we only have seven hours to build this product. This time, the seven hours also includes lunchtime and bathroom time. But this is not really we have. So, I want to win this time. So, the important thing is I need to give a very great presentation. So, in order to do this, I must finish my pitch deck. This takes one hours and the practice. And before that, I need to build a great product. So, I must take some screenshot. So, it only left me five hours. So, you might think this is not possible. Five hours. How many things you can build in five hours? I will think probably I can build five features in five hours. But, turn out, we only build one major feature. Because, like in Hexon, if you are too greedy, it will be more low cost. When you think you only have one buck, actually you will get 10 bucks.  Like, buck song. So, because I always, like, run out of time in the previous Hexon. So, this time I run something smart. We should only focus on one feature and the graffiti is perfect. So, I start thinking, what topic should I build? So, I think I want this feature very bad in Facebook. So, probably I should build this. And in order to build this idea, I need to build several infrastructure. Like, we need to save our links. So, the features we want to build, we need to build is like URL feature and background job to crawl out of all of the links and with a simple design. So, we finally build this service. And the second is, we start to plan our power schedule. And at the beginning, we spend 30 minutes to discuss this idea. We decide to only build one idea and one feature. Then we spend the next 30 minutes to set up the developer workflow like Git and the Capstrano deployment for the continuous deployment. The reason we do this is because you always fail at the final moment. You cannot deploy your website. So, this time we take smarter approach. So, I set up the continuous deployment at the first place. So, then we will like fail at the final moment. Then third is like build a major feature. So, I split the time. Like before lunch, we build the major feature because our core feature is too simple. So, we can finish all the feature before the 12 o'clock. And the reason we need to finish it all before lunch is because I will do something very evil. So, at lunch, I do something very evil. At lunch, we already have this demo. So, at lunchtime, instead I am not eating lunch. I take my laptop to show off people and make them demonstrate. So, you will lose. I win. Okay. And the fourth stage, after lunch, I started to do this because I always fail at the last moment. When the judge plays the website, you always fail. So, I spend a lot of time giving this demo to my Facebook friends, to help me to test. Then I find a lot of bugs. For example, we need to save a lot of links on the Facebook, but our background job is not fast enough. So, I build a parallel algorithm to make it faster. So, when you import your links, the result will show instantly. Instead of, like, dun, dun, dun, dun. And we also take some time to, like, have built some several minor details and building the landing page. And the step five is, like, finalize the product. And after I implement the minor features, my Facebook friends tell me, I don't know what stuff you are building. So, I realize we not only need to do a presentation deck, we also build our landing page. So, I spend some time to build our landing page then make our presentation and pitch, and then pitch at the final. So, why we want eventually? Because, like, we spend a lot of time practicing our pitch deck and the demo. In the hack zone, it's not what you build is not the first reason. It's like, if you want to want, like, programmers head to list, but the presentation, the pitch, the demo is the mostly important. And the second level, we only build one product, one product, one feature. And this product must be meaningful. What I picked this topic is because I think this I want to leave this feature. And also, I think Facebook needed this feature so I built it. And we spend three hours, like I say, we spend three hours to practice this project. So, by the presentation, presentation time, our product is already 100 production ready. But what others do? This is typical, programmers will do. If you want other, what people will do in hack zone? First, when they arrive the menu, they spend one hour discussion. Then they will see, and then we'll spend another hour to discuss for 10 ideas. Then they will decide to nail down to six major features. And then they will start coding. Then after discussion, they will spend all the time to build all the stuff. Then after like five o'clock or like the end, they will realize they can build and all. So they decide to drop to only two major features because they cannot complete all. If they want to complete all, they will run out of time. So I just pick the two major features. So they will, after coding, they also will save time for the pitch take. But since they don't finish all the features, they also didn't finish most of the takes. So when they come out of the stage, the first thing they will say is, sorry, I run out of time. Apology for their unfinished work. Then when the judge presses their website, pun 500 zero. So that's what typical people do. The difference between me and them is I spend only half of the time to develop a feature. But they spend most of the time to develop a feature but also include unnecessary features. But I save a lot of time to practice and craft my product to the perfect state. So by the end, my progress is 100, but their progress probably like 60. So let's find people, other people didn't win. So they spend the last time of unnecessary features. So that's not the only technique, that's not the only take, I only use the Hexel. Usually I manage all my projects in the same method. The way I manage my projects is like typical pitfall. I always do the same method. Like first, I will count how many resources do I have. For example, if a client dedicated a project to me like say I have like three months to build a project. So I will estimate how many resources do I have. Then the first thing I will do is like save the next month of assessment testing because if we didn't have save this time for practice or assessment testing finally, as the final, I probably will be get sued. Something like this. Because like the project fail is not like we run outside, it's probably like because you build something completely wrong or like over time, over project. So I think the last month for assessment testing is extremely important. And then I will split the other eight weeks for two, three milestone. The first milestone is like do the groundwork and find out what the risk in my project and set up the continuous deployment. And in the meantime, I can like go through the user story and to prioritize the user story. Then the second phase, I will build implement the major function and the project's major workflow to find out what the risks we have and what features we need to dump to complete the major goal of this project. And the third phase, then I will implement the major functions like improve usability and style the project to make it like much beautiful or much useful. In this week, I can continue to figure out where the risks and where the architecture we can improve all have. So then by the last month, we can make the product to do the perfect. So you will not, over time, you have plenty of time to make the project succeed and meet the expectation. So what do I do? Hexon is simply adapt these techniques. So I list my timetable. If I want to present in six o'clock, so I must finish the presentation at five o'clock. So if I want to give a pitch taker, if I want to do a pitch taker, I must finalize my product in four o'clock. So then before that, I don't need one hour to debug. So I must finish all my product before the three o'clock. So I use 12 o'clock to become the middle line. So I must complete most of the major feature at 12 o'clock. Then I can have a major demo to my stakeholder to make sure it looks good and gives them confidence. And after that I can go through the user story detail to make things right. So if you want to build a successful story, I think this talk I want to tell is instead of you building your product in waterfall way, first is you should define what is the success of your project. For example, if this is a Hexon, you must find out what is the success for the judge. For example, judge don't have time to go through your project or your presentation. So you must make them feel good. So you can have a very beautiful pitch taker, just make sure your project didn't fail at the demo time. So this is the success. It's not matter how your project is cool or something. The judge of Hexon won't give the first place to cool but useless or cool but not good for put on the newspaper. So you must want to win the Hexon or complete the project goal. You must find out what stakeholders care about and based on the definition to prioritize your story's priority. And the second is find out what is your major features. The major feature means the features you must have. Did it all could have and nice to have. Typically, if you do overtime in your project, probably the number one reason is you do too much could have and nice to have project in the first place, then you run out of time. And for my approach is like I will save the first one-third for the September for the final assembly testing and split the rest of the two-third to another three milestones. The first milestone is for ground works. The second one is for major feature and the third one is for minor features. Then I can make my available and the rehearsal so this approach can this approach can make sure you have plenty of time to think about and do the detail without over promise or like over two opponents. So the reason I'm doing this because the project fail is normal developers will do. If you have 90 days to do 90 days product, you probably won't build the product at all because if you have like 90 days or like 180 days it probably will spend all the time for planning. The typical internet company always like this kind of tragedy. So we have six months and the PMs spend five months for planning. Then after five months they give a proposal to the designer. They say we only have one month and the designer spends three weeks to build all the mock-up. Tell engineers you only have one week to implement all the features. So why I'm doing this because there are too much times so people are too optimized about the milestone. The way I'm doing this because I can use these techniques, I can cut down I can make the available looks very little. In this method I only have 60 days to build the features. Then I will think the time is very urgent. So I will dump all the features that are unnecessary. So if you do this your timetable and the quality will change it very significantly. So you will delete all nice to have and could have features to focus on actually MVP. When you finish the MVP you have plenty of time to implement that instead of spending too much time on wrong direction. So people always say Rails is fast but in my opinion the truth is if you didn't deliver something right nobody will think Rails is fast so that's the thing I learned in my software career. This is my talk thanks everyone. This is a little sale promotion. I have a new book called Crosshaker. Now it's the top seller. So if you want to buy this book we also have like sweet 30 copies in the front desk. You can grab one and give me to sign. Thank you.


From Mountain View, California, it's theCUBE, covering the 15th Annual Grow Awards, brought to you by ACGSV. Hi, Lisa Varn on the ground with theCUBE at the Computer History Museum in Mountain View, California for the 15th Annual ACGSV Grow Awards. This is an event with nearly 300 attendees, about 100 plus C levels, and I'm excited to welcome to theCUBE the first time, Hannah Kane, the CEO of Alam. Hannah, welcome to theCUBE. Thank you, I'm so glad to be here. And here you are and I are in the lobby where there is a lot of innovation and collaboration going on right here. So thank you for joining me in this energetic time at the event. Oh, I absolutely love it. You can feel the energy of Silicon Valley here. Yeah, you're right, you can.  Tell me about your company, what you guys do, what makes you different. So we do supply chain excellence. We execute and plant supply chains for very large corporations out of 19 locations globally. We are headquartered right here in Silicon Valley using technology to help our customers be agile and get the products to the right place exactly when their customers need it and protect their brand, do their risk management and make sure they do everything right in the supply chain. It's super exciting and no other place is technology use better than in the supply chain. So you have founded Alam in the 90s. You have seen a tremendous amount of technology innovation. I mean, things change faster than we can even keep track sometimes. Tell me a little bit about what has been a facilitator of you as the CEO being headquartered in Silicon Valley and being able to take advantage of technology to grow and scale your business. I think back in the 90s, nobody really realized the potential of technology in supply chain. I mean, supply chain wasn't even a word. And so I always thought that supply chain could be done much differently than it was done in the old days and that technology would be the big facilitator of it. So right now we have a lot of more, much more visibility in supply chain. We can see where products are, et cetera, but we've also increased the complexity of the supply chains. Driven down the cost of product, but also at the same point of time, driven up the complexity with components being shipped all over the world and assembled in one place and distributed to another place. So there's a lot of complexity that only technology can resolve. So being in Silicon Valley, which is the birthplace of technology, is just fantastic when you're in supply chain. It really leverages innovation that's taking place. And you can, like we said in the one we started, you can feel the energy of the innovation going on here. I read on your LinkedIn profile that you are passionate about excellence, technology, collaboration and community. The last two collaboration and community really underscore the Association for Corporate Goes Silicon Valley. Tell me about your involvement in ACGSB and what makes this event worthy of your time? So I do believe in collaboration and I think collaboration is that core value on Silicon Valley. I believe that collaborative companies and collaborative people are going to win in the marketplace and also have more fun while doing it, creating much more value. And so in ACGS, Silicon Valley, there's just a lot of collaboration, lots of different points of view, but also a lot of very focused, dedicated business people. And so we get together and get ideas from each other but also send business to and from each other and use each other as resources. And I also believe apart from collaboration, being resourceful is a real winner. You need to be resourceful and be able to make things happen and figure out a way to navigate new landscapes. And that's what having these great contacts in ACG and other associations in Silicon Valley really do for me. So last question, Hannah, for you is as a female CEO, a leader in technology, what advice would you give to the subsequent generations of women in technology who aspire to be leaders like yourself? I think they should be leaders like themselves, not like me, but like themselves. I think you need to be authentic, bring your own strength to every situation. And I think that's what I really wish for the new generation that many of the women have paved the way such that the new generation can really be themselves and contribute. And I would say focus on what you can contribute and what you can do for the greater community and for business as such. I love that advice, Hannah. Authenticity is such a valuable. Thank you so much for spending some time here with us on this energetic 15th Annual Grow Awards. We appreciate your time. Absolutely my pleasure, thank you. You're watching theCUBE, I'm Lisa Martin. Thanks for your time.