All right, so welcome to the TSBVP seminar. Yeah, it's a pleasure to have Professor Timothy O'Reilly for this week's talk, right? So after studying mathematics, Tim did his PhD in biophysics and physiology. And then after doing a postdoc at the mother's lab at the Brandeis. So that is a lab that works on the gastric ganglion, small network of like around 30 neurons. And then he moved on to the Cambridge and then he's currently a professor at the Department of Engineering. So today he's going to talk about the fever control and variability in the nervous system. So, Tim, please. Thank you. And thanks for the informal demonstration of one of the downsides of feedback. We're going to be talking in this talk mostly about the upsides of feedback and how it actually reconciles a large amount of variability and flux in the nervous system. So let me begin with an old idea. And I'm very aware that I'm talking to an interdisciplinary audience here. So if anyone is lost, please put your hands up and I can clarify, hopefully online. So the old idea is that learning results from modifying connections in the brain. And this is probably familiar to you even through popular culture. But you may not have seen what a synapse looks like. This is a diagram of synapse. There's a release of chemical neurotransmitter from the pre-synaptic side and then the post-synaptic side senses that and transduces the signal. And these connections are responsible for maintaining the function of the nervous system. Now, when there's a modification to these connections, there's some kind of plasticity signal. I'm not going to go into detail in what that signal is. It might be an additional neuroactive substance, something like dopamine. It might be actually a particular pattern in the neuroactivity itself that causes this change. Whatever it is, it leads to some sort of change in the strength and often the size of the synapse. And there's a few other things that you need to know. One is that synapses can enlarge, this is called potentiation, they can shrink, this is called depression. New synapses can grow, existing ones can die away and there's a limit, fortunately, to the size of an individual synapse. So these are sort of the key facts that you need to know about neuroplasticity for this talk. So another idea that's supported by a lot of experimental evidence and also makes sense is that potentiation or any significant change to a synapse needs some kind of switch, a kind of threshold to engage the process. So what I mean by that is that in time, there's some kind of discrete plasticity signal, some event, something surprising happens in the external world. There's some kind of residual trace of this event at the relevant synapses in the brain. This trace is held for a period of time during which the strength or synaptic weight increases over time.  So all of these things occur because there's some kind of permissive signal that is triggered, okay? So here's a conceptual diagram that the plasticity signal causes some kind of switch and then we get growth. Now how is such switches formed? Synapse is a small biochemical compartments and therefore quite noisy. And there's been tremendous amount of research in the last decade trying to identify the biochemical pathways that need to be switches. One example of a candidate's molecule, molecular pathway is quite famous. It's called cankai names too. It's an enzyme that sits close to the receptors of the synapse and these enzymes sense heightened activity and then are able to undergo phosphorylation. They can phosphorylate themselves and others and this type of trigger causes changes at the synapse and can lead to differentiation. And the model for this is very old. So this goes back decades where we see that the net phosphorylation or activation rates of these enzymes becomes positive when calcium concentration exceeds the threshold and it becomes negative when the on-enzyme experience is a drop in calcium that is somewhat far away from the special showing hysteresis in the activity. So this model is based of a sort of conceptual model and some amount of biochemical data. Okay, so the basic idea is we've got these two states and on state and off state and some sort of mechanism that's switching between us. And it's even possible to see this happening nowadays. You can see this happening in an individual and drifting spine or synapse. So here this little globe is a synapse and at this point in time, the experimentalists have triggered a surge in calcium as well as a coincidence, a strong input to the synapse and then what we see in pseudo color is the enzyme becomes phosphorylated that the red color and subsequently the synapse physically grows in size of the life of the course of several minutes. So this is nice but seems like it's the end of the story. We've found these kinds of switches, these enzymes seem to work as advertised. Unfortunately, it's very hard to dissect out the bio molecular pathways. If one takes the isolated chemical system, so the chemical molecule purifies them and puts all of its friends in there together in a tube, this system doesn't appear to exhibit hysteresis. So what do I mean by that? If we take the enzyme in its inactive state and ramp up free calcium, then the enzyme activates as it should. However, if we start with active enzyme and then ramp down calcium, it deactivates but the proportion of active subunits overlays these curves completely showing no hysteresis. So this argues that this enzyme at least when it's isolated from the synapse doesn't really behave as a switch, but it's very fragile. And these issues have dogged many attempts to uncover the molecular pathways that controls natural plasticity. So we entered the story at this point in time. Myself and personally led this work, Monica Giosha, and we went back to basic and we asked, okay, what makes a reliable microscopic biochemical switch? We brought things right back to basics, forget specific molecules and so on. We're just going to think in generalities. So let's start with this conceptual idea that there are two states, an on state and an off state. And let's cook up some generic biochemical model of such a switch. So let's have two species. This could be inactive enzyme and active enzyme, or there could be two separate species. And let's cook a plausible biochemical reaction that controls the rate of change of the state of the system. So here, X is being inhibited by Y and Y is being mutually inhibited by X. And those of you who have some biochemistry will recognize the form of these are just ill equations. So very straightforward vanilla model. The classical theory, and I'll say what I mean by that in a moment, says that we need two stable states in the system in order to have a switch, okay? It's almost a topology. So if we plot the curves where the rates of these have a net zero, the so-called null finds, then if this exponent is two or higher, then we get several intersection points, okay? So we end up with, in this case, two stable states flanking this unstable equilibrium. So we have a nice candidate for a switch. If you start the system near this, it will fall into the stable state. If we push it past this point, it will fall into this stable state. But here's like a switch. If, however, we take the same system and we take this example and then set it to one, this very similar system now only has a single equilibrium. So this lets us play with a basic chemical, biochemical motif and play with the number of equilibria it has and ask what happens. So one thing to point out is that this situation where there's one stable equilibrium actually corresponds to the data I showed you earlier, where the equilibrium itself might move as a function of the amount of signal or calcium, but there is only ever one equilibrium that's known as the resistance system, okay? So I mentioned that this is a kind of classical model. What I really mean by that is that it's an approximation of what really goes on in a biochemical system. Synapse is a small, about one micron cubed or smaller and many molecular species at a synapse are present in low numbers, okay? In particular, Kankei 2, the one we talked about earlier is present in 80 to 250 copies, okay? And the reactions themselves are probabilistic. So what that means is that this nice smooth ordinary differential equation, mass action universe doesn't really apply, right? The dynamics instead are discrete and the states of the system hop according to various probabilities which are functions of the state of the system itself, okay? So this is the appropriate framework to model small system. So what we're going to do now is take the same system which has this one stable equilibrium and these mutual competing species. And just to remind you, when there's no cooperativity in this system, we just have this single stable equilibrium. So we're going to take this exact same system, the exact set of equations, we're going to transport this now to the stochastic discrete mode with the same parameters, the same regular ones, okay? So here's what this looks like in the case where there are thousands of molecules present. What I'm plotting here is a probability density of where the states of this system, if you can think of them as concentrations of X and Y where they sit in the X, Y plane, they kind of hover around in the vicinity of the stable equilibrium.  So nothing too surprising, nothing too exciting either. It's just a noisy version of this system that we have. Now, what if we take the same system and we constrain it so that it doesn't have thousands of molecules, it instead has tens or hundreds of molecules? Well, this is what happened. So now we have a mode here. So concentration high probability states here and concentration of high probability states here and very little going on at this deterministic equilibrium, okay? And just in case you fell asleep in the last few seconds, again, this is a large system has exactly the same dynamics that became the case. All we're going to do is constrain it so that there's fewer molecules and we're going to put this. So what's going on here, okay? What is going on? Well, the short answer is there isn't a good theory for this, okay? So in a strict sense, I can say I don't know, right? The mathematics for handling these discrete stochastic systems when they're non-linear, frankly, it doesn't really work. There's lots of approximate ways of studying these systems. One way to study them is to just simulate them and play around, okay? Another way is to get a bit of intuition and that's what I'm going to show you now. So the first thing to note this is that the world that this system lives in is no longer this smooth, continuous space, it's a lattice, right? I can't have half a phosphorylated molecule or half a molecule, I've got to have a whole million. So when all of the states sit on this discrete lattice, in particular, it's very unlikely that the stable equilibrium will sit on the lattice point but that's a detail. So what difference does this coarseness of this system or this discreteness of the system make? Well, here's one observation and a piece of intuition. I like for understanding why this system is fundamentally different. Let's zoom in on the region where we saw that mode or that concentration of states. What we notice is that the lines that they mark where the net flux or net flow changes sign or changes direction, these start hugging the axis and they start lining up so that they actually fit close to or between lattice points. So what that means is that if we were on any of the lattice points and I've drawn the flow field here in the yellow, say we start at this point, well, there's a high probability the net probability of flowing upwards and slightly out. But we could flow up several steps because the probability of flowing out is quite low, okay? Similarly, on this side of both milk lines with the net probability of flowing down and a small probability per step of the unit time of flowing across. So this means that the system can get kind of trapped here for some period of time, flowing up accidentally hopping across the milk line, flowing down then accidentally hopping across again, okay? Now stress that this is just an intuition but it seems to pan out quite well and it seems to generalize a little bit to systems that don't have this degree of symmetry. But in this case, the system is completely symmetric. So the same argument would apply here as well, right? So we have a possible explanation for why we get this multi modality in this case. Regardless, what we have is a robust form of find modality or stochastic possibility in these small systems and the interesting thing is that this by stability is annihilated as the system grows in size, okay? So here's for a small system, we have two modes and on and off we could call them and then as the system grows, it dissolves away so we end up with our own noisy equilibrium. And this has been known for some time but what was less appreciated was that this system could act as a switch and if it's coupled to growth, interesting things will happen, okay? What we went into in the paper and what I'm not going to talk about today for time reasons is that this does generalize to multiple species and to situations where the rates are not symmetric. And it turns out that this mutual repression, mutual repression motif is fundamentally important for this behavior to occur. In any case. In your self-examination, how long was that creating a pattern for each one? Right, that's a nice question. So we have these different modes but a very relevant question for this to be a reliable switch is how long will I hang out in a mode if I start that? And the answer is an arbitrary amount of time. I can tune the parameters of the system so the dwell time is arbitrarily long. You think there is only one vibration in the system and there isn't any indication that we can work from small to large even if we create a size bigger than what we want? In this case, no. For this system, because of the law of mass action, as I scale up the size of the system, that equilibrium is going to become more and more, what, the fluctuations around it are going to shrink in the relative sense. Any more questions? There's a helpful question, thank you. So going back to this discussion of synapses having the switch and then growing, we can ask, well, what will happen if we couple this to growth? And the answer is quite nice. Let's take the same system and let's label these states an unsafe and an off state. And let's suppose there's a threshold for initiating synapse growth, one in the on state. Well, as the synapse grows, it's now going to switch itself off at some point. So this will automatically prevent the system from potentially passing a certain point because growth itself is coupled to the physics of how the system actually works. So we can have synapse that starts to grow and then it kills its own switch once it gets past a certain point. So this we put forward just as a nice idea, what's nice about it is it works in spite of the fact there's a high amount of uncertainty and noise in the system that this exploits actually a transition in the physics of the system, not the quantitative properties of the system, it's the qualitative property of the system that really matters. So what we learned from this is that these mutually inhibiting species in a quiet stochastic switch like behavior in the microscopic limit. And if this is coupled to growth, this gives a novel type of feedback loop that can regulate synapse differentiation. So this is all speculative, but it might explain two-ish decades of contradictory findings that maybe when one tries to reproduce what's happening inside these microscopic systems, the size as well as the context and everything else might matter and they play a key role. Okay, so that was just kind of a warm-up story. I'm going to tell you three little stories today. That was the first one. And they are all connected, I promise you, but we're going to go from molecules all the way up to entire organisms and entire animals. That's my plan. Let's see how far we can go. So in the remainder of the talk, we're going to ask how stable these synapses are in the long run. This is a question you should all be concerned about because if you believe everything I've told you, these synapses store your identity, your memory, your ability to function as a human being. And what? Sorry. That's the question. Yes. I think it's something that I've had for a long time. Is there a thing for which it was more the individual experience, this kind of reaction? Completely in vitro. You mean outside of the cell, cell-free in vitro experiments? And the same is a test-beam experiment you mentioned, but with the monitor. Is there a way to do that for a vitro? In principle, I think so. I think speaking to a clever microfluid experts would be the way to go. So there's nice analogs of this. So the so-called BZ reactions, which are not organic reactions, they're inorganic reactions, but they're oscillators. There are examples of people confining those in bubble rafts in an oil and aqueous interface. So something like that you could imagine, you could do a clever biochemistry experiment and confine all of these ingredients and titrate them so you end up with low copy numbers. So yes, in principle, but I've never seen it done for these particular molecules, but that would be the thing to do. Yeah. So how stable are synapses in the long run? That's something we're gonna be concerned about. And are there any implications of all of this for neural circuits and behavior? Physicists like to point out a lot of the time, particularly statistical physicists, that once you zoom out and once you average out behavior, some of these fluctuations go away and they're not a problem. Unfortunately, biology doesn't always respect that. Okay, biology is not a ferroline or a piece of rock. Sometimes the molecular events or the microscopic events actually matter a lot. Okay. So in answer to that first question, it turns out that synaptic connections in the mature brain seem to continually remodel even when there is no apparent learning or change in day, right? So this might come as a surprise. Here's some data from a few years ago from Seymour Rumpelblad, and what they did was imaged the dendrites of an excited green neuron and they tracked these protrusions, which are the sites of the cytometry synapses on the dendrites over many days. And what they found is that some of the synapses hang around and change shape a little bit. Some of them disappeared and new synapses were born. Even though in this case, the animal wasn't learning anything. It was doing something very boring. It was listening to two different tones associated with two different stimuli and doing that same task for weeks on end. Here's a summary of this data when the spines are actually tracked and labelled over around two weeks, something like half of the original population remains. And this is continually replenished on each day by new dendritic spines, synaptic connections coming in and taking to place. So this is quite a lot of flux over the course of a few weeks, okay? Enough flux to be a problem. So what you might ask, and a very sensible thing to ask is that maybe there is something systematic going on here, right? The animal isn't frozen in time. It has a life possibly outside of the experiment that's being done to it and it might be thinking about that life. So perhaps the brain is changing over time in a systematic way. So there could be consolidation of existing memories of the types of metabolic housekeeping, et cetera. Regardless, one would expect then these changes to be the result of some kind of systematic plasticity signal of the type that we were discussing earlier. So an obvious thing to do is to try and block these plasticity signals and see if this has any effect on this frenetic spine terminal. It turns out that some experiments were done of this nature that attempted to arrest this continual change by blocking the component or by correlating the change to other physiological variables. And this is what we're seeing. So here's an older experiment. This is in culture, cultured neurons where the remodeling is tracked over time and then in control conditions, there's a large amount of remodeling over several hours. And when activity is blocked by applying to drug detox into the culture, the amount of remodeling slows down for sure, but it doesn't disappear in a completely significant amount of labs. Doing things a different way, trying to actually regress the changes in synapse with other processes in a statistical sense, only was able to account for a less than half of the total change attributing it to some kind of systematic signal. And this isn't just something that one group has seen in neural neural cultures. This has actually been seen in intact animals as well. Obviously it's very hard to interfere with these pathways and people have to come up with very clever ways of trying to block the plasticity but leave other things constant. So all of these things are subject to caveats. But what's interesting is just how consistent these estimates are. That the amount of turnover that seems to be due to a systematic source is always less than half of the total amount of turnover. Okay. So consistently less than 50% of ongoing static changes seem to be due to these systematic signals. This seems like a small proportion. Why so much of it is apparent fluctuation? Is it just measurement problem? Could be. But for now we're just going to take these results at face value. And what we're going to claim is that this proportion is actually optimal for maintaining a limb behavior. That seems a bit silly. The amount of systematic change is less than half of the total change. Meaning that the total change is largely unsystematic. Out of nowhere. Well, if this isn't obvious to you already, this seems mysterious.  That's what I hope. Okay. So what I'm going to show you next are some simulation results that Coralbred explained and some intuition of the mathematics behind the results. And this was work led by my former post-doctor Ubaraman who's their faculty at the University of Sussex. Okay. So what we want to do to tackle this question is cook up a general learning and memory model. So we would like a network of new funds. We'd like them to be attempting to learn some tasks via some type of form of plasticity, some type of learning. And in a very general setting, you might think, well, it's hard to say anything specific about the type of data we've just been looking at. What we really need is an extremely general modeling framework, okay? In particular, we'd like to know what type of tasks we consider. We don't want to be particularly strained. So we'd like to consider all tasks, right? So how am I going to do that for some generic neural network? Well, first of all, we want to consider all tasks that this network can actually solve, okay? So what we did was we borrowed a very clever idea that's used in the connectionist network community, these days known as the AI community, where we take two identical neural networks, identical in structure, we take, call one of them the teacher and we fix random connection weights in this network. We call another one the student and these are the connections that are plastic, they can change, they can be adapted and we just feed random inputs into the teacher and get input-output paths, okay? So this teacher just generates data that the student now attempts to learn. For any given input, this teacher gives an output and now the student, when given that same input, its goal is to try and produce the same output. Now we know in principle that there's a configuration of weights that will allow the student to do this because it's got exactly the same architecture, okay? Not only that, we have access to unlimited amount of data to train these networks. All we need to do is find an appropriate learning rule by which to train the student, okay? And here there's another problem and that is that we don't know which learning rules the brains actually uses, okay? Because after all, we want to relate this back to biology. So what kind of learning rules would we like to use? Well, again, we don't tend ourselves in so we'd like to consider all possible learning rules. How are we gonna do that? Well, for simulations, we'll consider all possible learning rules up to first order. What does that mean? What that means is that we will model learning on some time interval as imperfect gradient descent, okay? So the picture you should have in your head is that the performance of the network can be quantified in terms of its error and the error as a function of all of the different weights forms a complicated landscape with hills and valleys in it and learning corresponds to dissembling one of these valleys to a low point, okay? How do you get down a hill? You can follow the path of steepest descent. It's not always the best idea actually. Turns out there's better words to do it. But locally over some time interval, anything that gets you from high error to low error can be approximated by gradient step plus some other stuff if it's not a very good limit. So this gives us a recipe for cooking up a family of learning rules all the way up to perfect gradient descent which some people argue the brain might be able to do. We're not sure, we're agnostic about that. All the way down to a learning rule that's not much better than random noise chickening the weights around and occasionally moving in the right direction, okay? Good, so we now have a way of decomposing changes in synaptic weights into a systematic component. So this is going to come from our approximate gradient descent rule and then a fluctuation which is just going to be a random disturbance to these weights. And we're going to be interested in how the steady state error, so that's the error that this network settles down to when it's finished learning on the relative magnitude of the systematic the fluctuating components because going back to the experimental data the cartoon version of what happened was that the experimentalist blocked this and then all the less with these fluctuations. So what we're able to do because we can control these things independently is look at how the steady state error depends on the relative strength of each of these. Any questions at this point? Because I've covered a lot of bits and pieces. Well, either everyone's on the same page or some people are so hopelessly lost they can't be bothered to ask questions, but that's okay. So for that, I'll think that you used an older new number, 60 new ones in the beginning. Yes, yes, yes. Yeah, the task error is always computed for the input output to the whole network. So there are some human new things that... Yes, yes, yes. Okay, so here's the result, the numerical result. This is the steady state error as a function of the strength of the plasticity relative to the fluctuations. So I'm just taking the magnitude of this relative to the magnitude of this plotting that on the x-axis. And what you see is when these things are roughly equal which is where the yellow and green regions meet the steady state error is approximately minimal, okay? If the systematic component gets stronger the error starts to creep up. If the random component gets stronger the error starts to creep up, yeah, okay? So this seems to agree in spirit with what we saw in the data. And what's nice about the setup is we can modulate the quality of the learning make it more and more accurate and we can change the overall magnitude of fluctuations. So changing the fluctuations doesn't really have much effect on this relationship. Interestingly, increasing the quality of the learning rule shifts the minimum to favor an even smaller proportion of systematic plasticity. Okay, so if you've got a good learning rule you don't want too much of it. You actually want to let the fluctuations dominate. Let the synapse to do their thing and it'll occasionally project and get them to go in the right direction. But why is that? Why, what's going on? So this is the part where you're gonna think this is all the action. And I'm just gonna give the intuition in a specific case. Let's imagine we only have one synapse and we have an error curve that looks like this. Errors low at the bottom. This is where we've learned things. And we're gonna consider the result of modifying our synapse according to a random component and then a systematic component that's gonna bring us down the gradient of this error surface. So let's suppose we start at the bottom. Flutuation comes along and it knocks us up the slope. Now I ask how big should our compensatory response be to fix the error that's been caused here? And the answer is it should be full and opposite because if it's any larger, we'll create up the other side of the slope. Now this actually holds more generally. But the picture changes slightly as we increase the number of synapses or the dimension of the problem. So I'm gonna give you now a little bit more intuition in a high-dimensional case. Let's suppose now we have three synapses constrained so that error is low in this kind of two-dimensional surface. So inside this surface, we have functional circuit configurations and outside it, things are not so good. Well, if we again perturb so that we're outside of this, the systematic plasticity is its goal is to take this back onto the surface. Now, what's the shortest path back to the surface? Well, the noise component pushed us in some random direction. An efficient systematic component is gonna take a B line straight back then to the surface. So it's going to take in general a shorter path to get back to that surface as determined by this right-angled triangle. And if you do the math from its highest level and you ask on average what the ratio is, about 61%. So this is the ratio of the magnitude of the fluctuation relative to the short part of this triangle, the systematic component. And that's quite complex because this ratio seems close to at least some of these estimates. Okay. I just wonder, so for the maintenance, maybe what you're saying, but how do we move the stationary point? Absolutely. So this all is a steady state analysis. So this is under conditions where we're assuming that there's no neck learning going on. And Professor Foucault is right to point out that if we're in a state where actually the animal is not learned, this is no longer true. And that is no longer the case. In an unlearned state, the systematic component should dominate. But all of the data that we're looking at here is at least nominally at some type of steady state. So that's a very important point. So conclusions for this part. So the experimental evidence seems to suggest less than 50% of systematic reconfiguration is driven by systematic changes at some type of baseline steady rate. And to optimally maintain the function, the systematic component should not outcomplete fluctuations. And this is independent of learning law. And this high amount of turnover in turn suggests that there's many spares of degrees of freedom in neural circuit connectivity. But the fact that these things can wander around, reconfigure, and then the circuit can find some way to fix them without sending everything back where it started suggests there's a huge amount of degrees of freedom, which again shouldn't be a big surprise. But this had interesting implications, okay? And in the final part of the talk, I'm going to talk about what these implications are. So let's go back to this project where we are hanging out inside this space, this curved space of circuit configurations where things work nicely. And some kind of ongoing learning or feedback or interaction with the world, internal or external, something is pulling us into this subspace, okay? Well, if there are many degrees of freedom, many spare degrees of freedom and room to move inside this surface, then generically, if there's any disturbance at all, any amount of noise in the system, for example, we're going to experience drift within this surface. So the circuit configuration itself is just going to change over time, okay? It can't not do that in a second. So this picture, if it's really true, if all that's happening is that the feedback is pushing in a general direction and not trying to restore a former state or keep a synapse sort of frozen, then this predicts a huge amount of variability and some drift in the circuit configuration over time. So the obvious question is, is there any experimental evidence of this? And if so, is it possible to actually manipulate this phenomenon, right? We've got a certain type of dynamics here, which is somewhat related to ongoing learning. That gives us a window into how the brain reconfigures and maintains information in the long run, okay? So for this, I'm going to turn to some experiments that were done a few years ago by our collaborators, in particular by Laura Driscoll, who was a PhD student with Chris Harvey. So what Laura did was she set up the following experiment where there's a mouse housed in a virtual reality environment with an optical window that allowed her to measure the activity of large populations of neurons in the brain using a calcium indicator. So she could literally watch neurons lighter as they were firing. And this virtual reality environment allowed her to design a task that the animal could solve. It's a fairly simple task. So the animal has to run down this virtual maze, turn left or right. And if the left versus right decision is associated with a wall pattern. So if the animal sees black dots on a white background, it needs to go left. If it sees white dots on a black background, it needs to turn right. If it gets the right answer, it gets the wrong one. Okay? Very boring. Animals can do it very well. And they learned this to expert level in a few weeks. So what Laura did was take those animals that had learned this task that were performing at steady state, no obvious behavioral change, and ask what was going on in the activity in a particular region of the brain. And the region of the brain she was interested in was called the posterior parietal cortex. Those of you who are not neuroscientists don't need to pay attention to this. All you need to do is think of it this way. You might be aware that there are parts of the brain that are involved in direct muscular activity or motor output. There are other parts of the brain that are sensory regions they receive signals coming in. PPC is neither of these, right? So it's a circuit that's sort of embedded somewhere in the middle of the brain and what it represents is therefore a little bit more abstract. It's not directly related to the sensory or motor signal. Okay? And it turns out that if you lesion PPC or block it, animals can't learn and perform this task. Okay? So it's an important chunk of the brain to solve in this time. So here's what she found. Importantly, the PPC activity actually represents the task in some sense. So there's a correspondence between where the neurons are maximally active and that's what's being plotted here in this color code. So yellow is where each of these cells, each cell is a row and the x-axis is where is the position of the animal in the mix. So there's a whole bunch of neurons that are lighting up when the animal's at the start. And then as the animal progresses through the task and through the maze, a different subset of neurons become active. Okay? This is nice. And incidentally, she designed this task because what she wanted to do was acquire a baseline and then train the animal to do something different and see all of this change. Unfortunately, that's not quite what happened. 10 days later, keeping the cells sorted in the same order, this is what she sees. So the activity is somewhat disorganized. And this disorganization continues showing a drift in the activity pattern over day. Now, it's not just the case that the neurons have become less active because on any given day, if she goes back in and reorders the cells, she's able to find this nice representation of the task again. So somehow this association between neural activity and what the animal is doing is evolving gradually over a period of several days. So we don't know why this is. The one thing that I'd like to ask you to think about is how neural circuits might cope with this. This is something that we looked into in a very sort of simple way. I'll quickly mention that. You could imagine that there are many possible configurations with so many degrees of freedom. It might be possible to extract or read out the information from this in spite of the fact that things are changing. And the answer is that's almost true, okay? So what we found, and this is work by Michael Rue and Adrienne Lovac, is that it's possible to decode what the animal is doing simply by taking the neural activity in this population and decoding it through a model. Well, what type of model we use, the simplest one, thinker, just linear model. So plain old linear regression can actually reconstruct the trajectory of the animal by taking enough samples. And if you look at the structure of this activity, a bit of intuition should say why. The neurons behave almost like little basis functions that encode what the animal is doing at any point in time. So a linear weighted sum of these basis functions can give you a decent estimate of what's going on. Another motivation for a linear decoder is that that's kind of what neurons and circuits do. So you could imagine another neuron that's connected to the cell in this population taking weighted sums of what's going on and then it's able to reconstruct some estimate and use the information that's present in the circuit. So anything that's a problem for a linear decoder might also be a problem for another part of the break. Conversely, if we can easily read out the information using a linear decoder, there's no reason why the rest of the brain shouldn't be able to do it either. And we can. So this should be a position. Yeah. About the data, can we go back to the slide? Where to here? A one thing, and a one thing data. Yes. So the one is the beginning of the training. No, this is several weeks after training for a month when they've reached a criteria in a performance, a performance is flat throughout this time. And he makes the everything during the course of the learning. They have looked at this. Yeah, I didn't do this work. This is worked by Laura first. You know, I think we have the great things from here to our potential. We don't know. Okay. The people are looking at that and that's an important question. Naively, we'd expect a lot more reconfiguration to go on during the learning time issue. Yeah. And if we just say what they're going through. Yes. Yeah. So the behavior is at quite hearing here. Basically they're performing at 95% all the time. Nothing interesting going on. Like this thing, they've been looking at the products. How much is there? Is there a difference in this? There's a smaller amount in a single day. Yeah. But the largest amount occurs between days, which is interesting. So it could suggest that sleep is involved and a bit of time. And in fact, it seems that both time and experience is jointly determined how much the code evolves. And that's been seen in other parts of the brain as well. So this is not the only part of the brain where this type of phenomenon is observed. Yeah, this question is related to this idea of looking for the brain. Do you think the thoughts have to seem like a cognitive trick? No, they don't. And that's interesting as well. So that's something we could pick up at the end. OK, so where was I? Right, we have this linear decoder. This allows us to reconstruct what the animal's doing. We can pull out its location, its velocity, and its heading in the maze. So this is the one. And unsurprisingly, if we build this linear decoder and then we fix the parameters and then test that same decoder on subsequent or previous days, it doesn't do so well because there's drift going. But this raises an obvious question. There's a huge amount of redundancy in this code. We're only reading out a small amount of information for a large number of samples. Can we find a drift invariant set of weights? How might we do this? Well, an easy way is to take data for multiple days and try to build a model that can predict as well on any given day. And the answer is it kind of works. So a concatenated decoder does not perform. It's statistically always worse than a decoder that's trained and used on that same day. Worse still, even when you do this, and this is challenging statistically because of the limited data, but our best estimate was that you will get degradation of a concatenated decoder no matter how much of a subset of data you actually use. So what this tells us is that the drift is going on in a way that doesn't completely destroy the decoder. It's not as destructive as it could be. So for example, if we have completely random drift, the decoding performance would degrade far more rapidly. So there's some sort of constraint, but it's not constrained into a perfectly linear subspace. So the drift is not occurring in the kernel of some limited decoder. It's not as nice as that. However, it is far more systematic and constrained than you'd expect by chance. And an obvious question then is what's constraining it? Is it the behavior and the continual learning that the animal's doing that's constraining where this neural activity resides? We'd expect it to be, but an obvious thing now to do is to try and manipulate the behavioral feedback. So in order to do that, we asked if we could actually decode the behavior from the PPC activity in real time. We were working with offline data where we fit a linear model and this is a big model, right? There's hundreds of neurons and there's many minutes of data sampled with a fairly high resolution. So it can be challenging to actually get a piece and fit in a certain amount of time, but because it's a linear model, there are nice methods for doing this. Moreover, because it's a linear model, we don't need to isolate individual cells in an imaging frame. If the imaging frame is steady enough, then it contains the signal plus some other nuisance component as well. So by linearity, we can train directly on pixels or super pixels of this imaging frame, build a decoder and then again recover what the animal's doing. Now, why would we wanna do this? The answer is that we can now potentially manipulate the feedback so that the neural activity itself drives the behavior. So in summary, what we're attempting to do is we have this situation here where the PPC is involved in controlling behavior of this virtual environment. We want to shortcut this for the eliminate the motor output to disconnect the control, the physical control of this VR. You go directly from this part of the brain and use that to drive the VR. Why? Because this should now operate as a different constraint for the activity in PPC and it should evolve differently than in the physical case. In particular, our conjecture is that this will maintain the performance of a linear decoder far greater than chance. So here's where we've got with us. And this was working with PhD students. You can saw all of working in postdocs in physics lab, Dan Wilson. So they set up the same thing except this time they have a real time decoder. And without the mouse knowing it, they can switch between physical control of the VR or brain machine interface control of the VR. And this is what it looks like. So here's a comparison of the physical interface and the brain controlled interface. The animal is running down the linear portion of this maze. It reaches a turn and then turns left or right. And if you look at trajectories of these animals, it's very hard to tell the difference between the brain control case and the physical case. Now, the brain control case is not quite as good. The animal definitely figures that something's a bit strange here, the performance is not quite the same, but it's good enough to study what happens in the long run. And so far what we found, and the broken ones show that is that even with a fixed decoder over several days, we don't get the drop in performance that we'd expect from offline data, which is interesting. I haven't done this long enough or across as many animals as we need to to make strong conclusion that this is promising. And another interesting thing is that the activation of the neurons and what's being shown here as a function of location in the maze is where individual cells are most active, that there's a difference between the BMI case that's shown in blue and the physical case that's shown in black, okay? So there's certainly some contextual difference that the animal detects when it's operating in this BMI case. But that contextual difference is not enough to completely destroy the decoding of the task, okay? So on a short time scale with a big change in activity in PPC, but it's compatible with this linear decoder. On a longer time scale, what we're interested to see is does this actually shape the evolution of the neural code as we predict it would? Well, we're bringing up like the BMI. So, do you call the motor intention rather than the current state? It's current state, yes. So there's no history. It's a static decoder actually. Okay. So now let's come to the transaction. So how can you operate the control? So you will use the current test before the system and the data feedback. Yes. Okay, because that doesn't mean that neural activity in some of our cases, we see the current actual data. Yes. Okay. So there's something probably a bit unnatural about this, that the change in the virtual reality environment somewhat anticipates a little bit what the animal is actually doing. So we don't know what the appropriate lag would be because when we looked in the data, there wasn't a detectable optimal lag between the activity and the behavior. If you go back in time, you can always reconstruct from history what's going on, but that's not a surprise. But it wasn't obvious that there was a sort of obvious peak in predictive power between TPC and behavior. Yeah. And some of that could be a limitation of calcium indicator as well because they do have the sort of temple smoothing effect on neural activity. And actually, I thought of those both working on the... Yes, we didn't freeze movement. What's interesting is the animal's physical movement starts to deviate from what it should do. So that there's a gradual mismatch covering between the animal stereotype movement and then it's actual motion in the maze. So they start to decouple a little bit. So one of the things we're trying to do now is to slow down the animal's movement by physically breaking the ball. We'll see if we can decouple the two things. Do you know if the TPC activity has something like two various influences in four canvases? Yeah, I don't know. I don't know. Like a kind of theory. Yeah. Yes, I don't know actually. I don't know whether you get these little episodes that predict the animal's intentions or be interesting if you do. So we're actually working with Yulia Kruppek who's been doing the same thing with hippocampus. Possibly, yeah, at those things. But it's very hard with optical methods. Okay, so let me quickly summarize. I can take a few more questions after that. So in this part, I talked about decoding signals directly from TPC and this actually allows these animals to control movement in virtual environments. Interestingly, the new activity differs slightly from the BMI in the physical case. This is something that's seen in traditional BMI as well, where you use the motor cortex as well. So that's actually consistent. Interestingly, in fixed decoder seems to perform safely over several days, at least so far, despite possible changes in the new activity. So that brings me to the end and hopefully you've seen that the thread here has been that there is a large amount of ongoing change at every level in the nervous system. And that observation by itself is very hard to reconcile with anything coherent going on. But when you put feedback into the picture, you start to realize that a huge amount of reconfiguration can happen as long as there is a minimal amount of feedback keeping things going. So I'd like to thank all the wonderful people that I've met and worked with over the years. It's a real privilege to be among young creative smart people as my nine to five job. The people here, so Monica, Druva, Michael, Charlie, Ethan, and Adriana were directly involved in the work that I talked about today as were these collaborators. I'd also like to thank Jonas and Lynn for hosting me while I'm here and making sure that everything's worked so smoothly. The TSVP committee for allowing me to come here in the first place and of course, OIST and yourselves for supporting this visit and for listening to me talk for a minute. So I can take more questions now. Thank you. Good, thank you very much. So, thank you. Has there been any experimental work? So this is about the spine vertebrate. Is there any experimental work that has been able to look at how much of the 500 or over talks to a vertebrate inside and out thing in both? Yeah, I don't know. That number, and for your model, if that number wasn't particularly low, what do you think that would be for your model? Yeah, I think what, yeah, so what you're asking is, is there essentially a persistent available input and then the spines that are turning over are just passing the baton between each other and maintaining connection with the same input? I suspect that's actually not the case for the simple reason that when you look at the turnover and on a single dendrite, these spines are appearing and disappearing in very distal parts of that denco. So it could be the case that an axon snakes along and makes contact in several places, as does happen quite a lot. But it doesn't seem to be the case that the turnover is just a zero sum game at individual synapses. But I don't have a systematic data set that speaks to that. I'm not aware of anyone having gathered one. The last bit, I think it was super interesting to articulate the drift, the online situation. But I'm not sure how that addressed the failure of the linear motor in the actual situation. Right, so I think what happens, and this again, subject to it being preliminary data, we haven't run this for a whole month. So we don't know what will happen in one rush. But I think that when the animal is actually in closed loop, it's able to correct the minor deviations from what's happening, right? So it intends to move forward. It kind of moves forward, but with a bit of a deviation, well, it will correct that. And as long as those corrections don't destabilize the overall loop, okay? In other words, as long as they're modest and the controller doesn't amplify them, which a linear controller could do in certain circumstances, then you'll get graceful if slightly suboptimal control of the BMI interface. It's a little bit like if I was to mess with your mouse pointer, and each day I slightly rotate the X, Y, because without you know it. On that day you'll come in and you know what's going on, but you'll very quickly adapt. That you can't mimic that situation in offline data, okay? So I think what's going on, if there is the substantial change in this case, right? Is that the animals continually adapting? And then the question is, does that adaptation show up in the trajectory of the drift? So it's almost like forcing a form of ongoing latent learning and then looking at how the population activity evolves in its presence. I have a question about some sort of biological underpaying for drivers for all business time, for removal of lap grating. Of course, some sort of like maintenance for lap grating to the different place, but at the same time, especially in the case of the campus where higher quality brain energy is more of an automatic encoding system. They're incorporating new information with opportunities. And in your final experiment where I know it's going to go through everything and have imagine the animal is kind of living for a life outside of the past. What do you think would be another task? So I would introduce you to the proper reality task and to produce other transfer changes, which is quite different in nature from maintenance purpose. We expect the representational drift being very different from what you have observed and recorded in your environment. Yeah. And I should mention I'm not physically doing these experiments. It's down in Chris's lab. There's very kindly, and often it's time to do this. What you'd expect from the theoretical point of view is, so let me repeat what you've suggested. Instead of doing the fairly mundane task day in, day out that doesn't really use up resources, neural resources, what if suddenly the animal had more of a challenge? Okay. And had to do an auxiliary task or a variant of that task or something more complex each day. How might that affect drift? Yeah. So the purely theoretical answer to that is drift is occurring in spare degrees of freedom. So if you reduce the number of spare degrees of freedom, as you should, if you've increased the complexity of the task or other additional tasks, the drift can now only occur in a lower dimensional space. Okay. So that should actually be detectable. If the drift resembles something like a Brownian motion in this subspace, if you change the dimension of the subspace, if you change the mean squared deviation of that, of that random walk as a function of time, so you should actually be able to detect it. And what you should see is the mean squared deviation drop as the complexity of the task increases. So that's the naive. Yes. Vanilla answer to that. I think it's an interesting variation. Yes. Yes. Well, Chris, Chris is lab has, has considered this and we've sort of considered this. It's, it's something it's quite hard to do. Because it's not obvious that the animal, one of the problems I foresee with this is you really get a limited amount of time where an animal is engaged and motivated to actually do anything. And that tends to equal the amount of time that you need to gather the data. So you have to design this in a very clever way, possibly so that the animal being monitored in its own case with something that's mobile to actually do that. But yeah, that's a good idea. Yeah. Yeah. Yeah. And it's, it's again something that's, it's on this ever growing list of things to try out is now that we have this decoder, we can mess with it and we can apply transformations to that decoder to see does it have any effect on the PPC activity on the long or the short term. Yeah. Maybe. Yes. Maybe, maybe. And there's probably a very clever transformations that we could do that are cast dependent as well that maximally use the neural activity that's available. So yeah, it opens really this is a proof of principle, but it opens the way to doing all these types of, of manipulations. Yeah. Yeah. Yeah.


Hi, I'm Clint Finley with Silicon Angle. We're here live on theCUBE, which is our streaming broadcast internet TV show. We cover all the latest and greatest in technology. Today, we're in San Francisco at the first Node Summit, which is dedicated to Node.js, which is an emerging development platform. I'm joined today by Arnaud Kazimier. He's the founder of Observit. Arnaud, can you tell us a little bit about Observit? Observit is a real-time analytics service that it's not analyzing your website, but your users.  So it's just like watching over the shoulder of your user. And so would you compile a lot of statistics from this or is this just, you record a lot of the interactions? Sort of, how does the end user who's analyzing the traffic, how do they use it? We're sending all the events to our backend and then you can replay it, just like separate session or it gets aggregated as heat maps. Okay. And so you were one of the winners of Node Knockout, the most recent Node.js development competition. Which categories did you win in? We won overall, solo and utility. So you developed the original one, solo, all in your own? Yeah, solo. Okay. What's your background before starting this project? I'm originally a designer and then I moved from designing to the front end and then when Node started getting extraction, I moved to Node. So you're solving a sort of problem that you would face then as a web developer wanting to know how people are interacting with sites and applications. Yeah, how they're doing on his site and what is working and what is not working. How users are converting on a website as well. How does, how do you use Node.js in this? Our complete services runs on Node.js. We're using Socket.io for the real-time connection between the user and our server. And we're using Node.canvas to generate heat maps and we're using the queue service from LearnBoost to process all the information in real-time. Is this something that the end user has to opt into or is this something that it just, it just starts getting recorded? It just starts getting recorded. So you have to put a little privacy notes in your page that the user might be monitoring. And so this is all done in JavaScript. So there's nothing that the end user has to even download. It's just part of. There's one line of coding in your page and you're done. How old does it perform then? Does it slow the site down? It doesn't slow down the site because we're using WebSockets to send over the information. But of course we have a little performance in because you have to download one access script on your page. Well, what about on the users and does it? You won't feel any latency when you're moving around on a page. Will it increase the CPU usage or anything like that? Nope. Not a bit. Or maybe like one, two percent. It's not noticeable. Where are you going with this application now? I know you're part of the node competition here. Yeah. So are you looking for funding? Do you have funding? We're definitely looking for funding. We're currently just bootstrapping the application. And we're just hoping to build it out as a complete service. And by looking for investors, we can just push out the public release sooner. What's the business model going to look like? Will this be sort of a freemium service that people can, you know, you have an entry level tier? Yeah, we have an entry level that's like 100 recordings and then you have to pay for a monthly fee. Okay. What's on your roadmap for the development? Is there any, do you have any longer term vision for what else you want to do? Yeah, we definitely want to continue aggregating the data and getting more useful data out of it. Like where your users are dropping, so you don't have to follow one session to find out, but just an aggregated view of possible interesting sessions for you. And we want to go mobile. It's the new hot thing, so following touches and relating that as well. Okay, so I want to go back a little bit to your background, because you said you started out as a designer and then you started doing front-end development and now you're doing back-end development with Node. How hard were each of those transitions? From designing to front-end was quite easy because I just always wanted to have my designs pixel perfect. So you just have to do it yourself if you want the pixel perfect web pages. And then, yeah, Node came and I just wanted to move on, keep on learning and was just a smooth transition from front-end to back-end. It's just the same language everywhere which makes it really easy to work with. Is the whole callback model, is that hard to learn? I don't think so. It's just something to get used to, but once you know how it works and what kind of issues you're going to run into, like these funky coal stack trees with nested coalbacks everywhere, because if you just know how to avoid it, it's just simple. Were there, as you were developing, observe it, did you run into any big stumbling blocks with Node or did everything just work pretty seamlessly? The only problem that we had was with hosting that are not all supporting WebSockets. So that's something we were bumping against because the WebSocket spec kept changing and changing and not every hosting company can keep updating their stack to support the current WebSockets. Right, and not all browsers. Well, all the major browsers supporting WebSockets now, I know Mozilla took it out for a while but they put it back in. Opera is supporting it now. Opera is supporting it, Chrome, Safari. Has the development of it stabilized then of the spec? The spec is finalized, so that's great news. But there are always some issues with cross-bousers support, even with WebSockets. They're bugs everywhere. So you just gotta be aware of those issues. So that seems more like it's more of a front-end issue rather than a Node.js issue. It's not a front-end issue. Yeah, but you also make sure that your back-end of Node is up-to-date with the different WebSocket protocols. Of course, we've got really old versions of Safari. It's using like Draft75 and the newer one using the latest WebSocket specifications and you want to support them all to have a great browser support range. Okay, well, that's all the questions I have. Is there anything else you wanted to let our viewers know about? No, they can sign up for a beta on beta.observe.it. And we're rolling out beta next week, slowly. Great, well, good luck with the beta. Yeah, thanks. We're gonna take a break.


Welcome back to Tennis Talk, my name's Cam Williams and Alexander Zverev has pulled out of the Davis Cup this week. After suffering some pain in his foot, the same one that he heard at the French Open a couple of months ago, let's go check out the press conference that he actually announced the injury. So this is what Zverev had to say over at the Davis Cup, I have a bone edema issue which causes me quite a lot of pain. I don't know if it happened yesterday or not, but yesterday in practice with Oscar Otter it got to the point where I couldn't run or walk anymore. I subsequently found out that it was a bone edema which means I won't be able to practice here and it's not a question of days, it's more of a question of weeks or most likely even months. There have it, Sashasverev not playing the Davis Cup this week and we might not see him for maybe even the rest of the year because remember we've only got a couple of months left of the season. October, November is sort of the end of the season, Davis Cup is obviously in December and there's a couple of other tournaments floating around at the end of November but he might not play for the rest of the season at this point and that's because of this bone edema. Let me know down in the comments below. Do you miss Vera? Do you want to see him back? I feel like we missed him at the US Open, you know he was one of the big names that wasn't there alongside Djokovic. I'm not saying that it wasn't a great tournament, it still was but to not have Vera there a former finalist at this stage he's probably not going to be qualified for the ATP finals so expect him to lose a lot of points because he's the defending champion there but Alexander Vera not playing Davis Cup this week and we don't know when he's coming back.


This is a picture I took from a corrupted SpongeBob SquarePants bootleg tape.  None of that is important compared to the part I took this picture from though. I found this tape in a library section at Goodwill, as I like to look around for various treasures every once in a while. I don't normally buy VHS tapes, but this one in particular cocked my eye with how mysterious it came off to me. On the side was a label written with pen, and it read, to Georgia Jeffries. It made me wonder. Was this a personal belonging that someone brought here either out of anger or on accident? I have heard cases of people taking their wedding videos or family photos to this place, so it was no different. I thought that maybe I could return it to its owner, so I bought it with my debit and headed over to my dad's place. After all, he was the only guy I knew who had a cassette player. I'm sure as hell not buying one. After a rough setup process, I injected the tape in and pressed the play button. Imagine my surprise when the SpongeBob intro showed up. The damn video slid so much that I could barely make out the first few frames, but it eventually stopped and played as normal. One small thing I noticed was that the Nickelodeon screen bug wasn't in the corner of the screen, as if this was recorded from an actual copy from the company themselves. Then after the first three episodes were done and dusted, dumped played. It went on as normal, with Gary staying with Patrick at his rock, SpongeBob getting jealous, and so forth. It was fine, up until it reached the scene with Patrick showing up to SpongeBob's house to get soap for Gary. Remember that one scene where SpongeBob asks if Gary wants fresh scent or heavy duty, but then he stutters halfway through duty due to his sad state? Well, the part where he stutters three times rewinds twice in the video, and the audio spiked, to the point where he sounded like he was saying too. After the stuttering bug, SpongeBob's eyes suddenly distend to a swollen degree, and his mouth shrinks to where it can't be seen. He then drops the soap from his hands and turns to his left. Patrick and Gary have now disappeared, leaving behind what seems to be an actual transparent image of something on SpongeBob's sandy floor.  SpongeBob delves, and is now heavily breathing, but at this point, he doesn't sound like how he usually would. He sounded like someone from a slasher film trying to escape from the killer, if that's a good enough way to describe it.  Looking behind him, and to his right, he'd make his way over to the door, and turn the knob, taking a peek outside. What followed was actual footage of a specific area suddenly showing up, with the time and date of it being recorded on the bottom left. It looked like a grassy field, blowing away in the windy setting, with a road in the front, and many many trees in the back. The video was completely still, with no sign of the holder in front of camera. No hands, no body, nothing. This went on for a few seconds before jittering and cutting back to SpongeBob peeking out of his door. His eyes weren't circular anymore. They were structurally correct, from the eyelash to the pupil. He would make his way back inside, positioning himself back to where he was when his strange scene started. He'd shake in place, his fists clenching until they widened, and then it happened. SpongeBob had no holes in his face, nor did he have a nose, but along with the natural-looking eyes, he had a human mouth with misaligned teeth. The shot looked heavily posterized, and a specific line from SpongeBob during the original scene would repeat. Just with the line war sounds of SpongeBob loudly sobbing. Various random words would flash on screen, spaced out between sets of ellipses. Words such as television and Yulbert. This continued on for a few seconds before the tape ejected from the slot. On seeing what I had just seen, and all the bizarre shit that came from it, I had to know who this Georgia Jeffries woman was and why this was being sent to her. I just had to. I needed all the answers I could get. I tried a good few methods. Making a Facebook page, asking friends in my area if they know someone by that name, encouraging them to spread the find, and even checking online phone books. By sheer luck, I managed to find one person. A 47-year-old woman, living in the same town as me. Her phone number, address and previous information were listed. I ultimately decided to visit her house to hand over the tape, as I felt like calling her over the phone and not knowing what I looked like seemed less friendly. To put it simply, yes, it was the right person. For reasons I honestly didn't expect at all. She asked me what was on the tape, and I informed her of everything that I saw. When I got to the part involving the grassy area and the random words, she looked like she was panicking out of her mind, while trying to be quiet. I asked her what's wrong, and she told me to repeat what I mentioned to her. I told her about the exact details I saw, and just for added measure, what words I remembered seeing. Here's what she said to me. On February 2, 2012, a finger belonging to her 6-year-old son was found near a field about 3 miles from her home. Shortly after, a DNA test was made and the results were given back to her. The location was similar to what I had described. Her husband didn't take him to school at the time he went missing. He was sick with an upper respiratory infection at the time, so his son had to take the bus, or at least, they thought he had taken the bus. A missing person's report was filed when he ended up not coming home. Elf, elephant, yogurt, red and yoyo were some of the words I saw on screen. She told me that he dressed as an elf for Halloween a year before the case. Yogurt was one of his favorite snacks to eat. Red was his favorite color, the elephant was his favorite animal, and his favorite toy was a yoyo that he'd carry to recess when he got bored. She still had that yoyo. When her son was found, it was a revolting sight. In the woods, he was found dead in an underground cellar by a few teenagers, drenched in rubbing alcohol. His limbs were bound with duct tape, and so was his mouth. They also found a small safe against the steel wall. Not only was everything touching the safe covered with rubbing alcohol, including the money inside, but a lone pair of rubber gloves were also found on top of it. When locals that lived near the area were asked by police if they had witnessed anyone go near the woods, many replied saying that they didn't remember seeing anyone go near there, let alone see anyone outside. The ones that did see someone reclaimed witnessing a person in winter attire. The case went cold. Disgusted and hugging her tightly, I asked her if she wanted me to take this tape to the police. She begged me to do it, and rightfully so. What else would she ask for? I found a tape linking to her son's murder, in a Goodwill library of all places. Then I took the tape to the police, and they brought it over to the interrogation room to watch. They asked me the usual questions, like how she knew this was linked to the death of her child, for starters.  That's when I noticed something odd. The words that were shown on screen weren't just this poor kid's favorite things. Upon taking a closer look, the first letter of each word formed something when put together. It was a hidden message of some sort, with only two words. They are lying.


It's time for the Lawn Jean Chronoscope, a television journal of the important issues of the hour, brought to you every Monday, Wednesday and Friday. A presentation of the Lawn Jean Wittner Watch Company, maker of Lawn Jean, the world's most honored watch, and Wittner, distinguished companion to the world-honored Lawn Jean. Good evening, this is Frank Knight. May I introduce our co-editors for this edition of the Lawn Jean Chronoscope? Mr. William Bradford Huey, author and analyst, and Dr. William H. Peterson, writer and economist. Our distinguished guest for this evening is the Honorable Homer E. Capehart, United States Senator from Indiana. Senator Capehart, it's a great pleasure to have you with us tonight, sir, on the Chronoscope. Thank you, sir. And now, sir, how do you feel about Stalin's death? Do you think that the free world stands to gain anything from this, from his passing? I'm fearful not. No, I do not think so. I think the condition, the situation there will be just as it has been understood. Well, sir, the news tonight is that our own government is hoping to exploit this uncertainty in Russia at the moment. Are you in favor of our government? Well, I'm in favor of our government doing anything that they can do that is necessary to win the Cold War and win the Korean War. And I have absolutely no objections to anything that they do. I think they ought to do it. I think it's our responsibility, the people to do it. Our viewers recall that you have taken a rather firm stand even back in the McCarthy controversy in favor of more aggressive action on our part. In the Far East.  Do you still favor doing whatever is necessary to win that war? I certainly do. It seems to me that if we're unable to win the Korean War, that we certainly are going to show weakness and come near having a big war with Russia We're unable to win a little war and if we show weakness at the moment, I think we're less likely to have a big war with Russia if we win the Korean War and show strength. Senator, what do you think of MacArthur's policy? Would you say that history has vindicated MacArthur? Well, I think it has because the Korean War will soon be three years old and we haven't won it. We haven't made any progress. And Lord knows that we've got to someday win it. Well, General Van Fleet has been testifying in Washington this week. Do you interpret his testimony to mean that he holds very much the same views that General MacArthur held? I think they're exactly the same views that MacArthur has had all along. And as a student of this situation, you feel that we have to take the moves that are necessary in order to achieve a military victory in Korea. Well, I know only one way to win and that's to win and that's to use every conceivable advantage of everything that we have and use every bit of our material. Well, Senator, you of course are chairman of the very important Banking and Currency Committee and as such, that makes you a sort of guardian of the country's economy. Now, I believe that you've been criticized in some circles because some of your critics say that you're hedging on this matter of controls. You're getting rid of control, as I understand it, but you want some standby laws left there. Now, how do you defend your position there, sir? There's no relationship whatsoever between a continuation of controls at the moment which are not needed and what I'm advocating. I'm advocating a standby control law which in case of a big emergency would permit the President of the United States to immediately freeze all prices, wages and rents for a period of 60 to 90 days. Do I understand that you are opposed to price, wage and rent controls? Well, I'm opposed to them at the moment, but only because they're not needed. I'm not opposed to price, wage and rent controls. For example, if during a big emergency, during a big war, our government is taking anywhere from 40 to 50% of our national products for war use, leaving only 50 to 60% for civilian use because under those circumstances, you certainly must ration the goods and you certainly must have price control. And I'm only thinking in terms of a big emergency where our government would be taking, say, anywhere from 40 to 50% of our goods. And under those circumstances, I think we must have price, wage and rent control and rice. All controls will go off on April 30th. All controls will go off on April 30th? And you're in favor of that going on? I certainly am. I voted against a continuation of controls last July and had the Congress followed my vote, we would have gotten rid of controls as far as back as last July. And this standby legislation, which I believe some businesses are saying you're holding a sword over their heads, that you simply want to use in the event of emergency. That's all and only in an event of emergency and then only for a period of 60 to 90 days until such time as the Congress can decide whether they ought to be continued. And in the wisdom of the Congress, during that period, they might well decide that they were not needed and I might come to that conclusion myself. Well, sir, now moving on to the other big question, you come from a big farm state, Indiana. How do you feel, sir, about this decline in farm prices? Are you for low farm prices or high ones? Well, I'm certainly for high farm prices. That is, I'm for a prosperous farmer purely on the grounds that the farm income or the nice little income is always seven times the farm income. And if you permit the farm income to decline, then your nice little income will decline in proportion, which means less jobs in the city and less business for our business concerns in America. What about this New Zealand beef, which we consumers have been enjoying, some nice tasty steak and so on? How do you feel about New Zealand and foreign foods coming into our country? I have no objections to their coming in unless the production in the United States is such that under the law our government is subsidizing our farmers, then I certainly am opposed to it. I'd much prefer to buy the beef from New Zealand or the eggs and cheese or butter from other countries and give it to them or give it to some other people throughout the world and save transportation charges. Senator, let's take this little situation. You are a member, I believe, of the tapped wing of the Republican Party. Now, are you going to be uncomfortable with the Republican administration buying all these millions of pounds of butter down there? Please keep this in mind that we Republicans have inherited a lot of things and some of many of them are not good. Now, we inherited this butter situation. We inherited a law that does not expire until 1954 in which we guaranteed to the farmers that we would, the 90% parity. Did you vote for that? You're in favor. Yes, I'm in favor of 90% parity. Yes, indeed. You're in favor of the federal government guaranteeing a full market or a good price to American farmers. Well, I'm in favor of maintaining farm prosperity and doing whatever is necessary to maintain farm prosperity because I know just as certain as I'm sitting here that if you do not do that and if you do not have a prosperous farmer, then you're going to have a depression in this nation and none of us want that. Do you remember that the beginning of the depression, the 32th depression, of course, started with the farmers? Well, now as a practical politician, which way do you think the knife is going to cut? Do you think that opposition is building up to the Republican administration in the farm areas now? Well, I think opposition is building up against the Republican party at the moment, possibly, on those things that we inherited, which the people do not quite understand. In other words, we've got to deal with situations as they exist in Washington and we had nothing to do with their making, and at the moment I don't think the people quite understand that. Now, as soon as we can work out from under many of the evils of the New Deal administration, why then we'll start straightening things out as they should be. Well, one of the things Republicans inherited, starting to get the cave part, was very high taxes. Now, sir, would you be in favor of reducing taxes before we balance the budget? Well, my position, I want to make my position perfectly clear, and that is that I'm in favor of reducing expenditures, balancing the budget, and likewise reducing taxes. And I think all three can be done, should be done, and must be done, and I think the Republican party will do it. At one place, you're willing, however, to spend more money if necessary, as in the Far East. I'm willing to spend whatever money is necessary to win the Korean War and to win the Cold War, but I'm against wasting that money, and I'm fearful that it's going to be a long-drawn out affair. Therefore, I think we should keep our house in order at home by maintaining a sound economy and not discouraging our people too much with taxes high over a period of years. Well, which do you put first in those three desires? I certainly would reduce expenditures and balance the budget, yes. One possible expenditure, Senator Capehart, is further foreign aid. Now, the British are here in Washington, and the West Germans and the French are on the way over. Do you favor continued financial aid to the Europeans? Well, I've always favored aid, and my quarrel has always been with the way they did it. I'm now in favor of helping them and aiding them, providing it is on a good sound business basis, meaning that we get value received. Well, that's a final question, Senator Capehart. Since you are so vitally concerned with the nation's economy, I'm sure that our viewers would appreciate a prediction from you, sir, as to what you think is in the economic future for the country. Do you expect a good year or a bad one? Well, I'm very optimistic about the future. I can see nothing but full employment, high wages, and good business for the businessman and for the farmer. I see nothing that anyone should be fearful of, at least for the next 12 months. Well, I'm sure that our viewers very much appreciate this rather hopeful presentation of yours, and thank you for being with us. The opinions you've heard our speakers express tonight have been entirely their own. The editorial board for this edition of the Long Gene Chronoscope was Mr. William Bradford Huey and Dr. William A. Peterson. Our distinguished guest was the Honorable Homer E. Capehart, United States Senator from Indiana.  Soon it'll be spring and then Easter, and then glorious days ahead. In countless homes, this is the season of planning for great activities. Easter, graduation, an anniversary, a June wedding. And every year, more and more people are giving Long Gene watches as gifts. For Easter, for any important gift occasion, nothing can be worn with greater pride, and nothing that can be worn speaks more eloquently of your affection. 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We invite you to join us every Monday, Wednesday, and Friday evening at this same time for the Long Gene Chronoscope, the television journal of the important issues of the hour, broadcast on behalf of Long Gene, the world's most honored watch, and Witner, distinguished companion to the world's honored Long Gene. This is Frank Knight, reminding you that Long Gene and Witner watches are sold and serviced from coast to coast by more than four thousand leading jurors who proudly display this emblem, agency for Long Gene Witner watches. This Sunday, Ken Murray proves it's time to smile on the CBS television network.


What's up, everyone? It's your girl, Kenzie here from Layton Sports Cards, and I'm ripping a personal here for Jeffrey S. We've got a 21-22 Panini Obsidian basketball hobby box. All right, best of luck, Jeffrey. Here we go. Let's see again. Another new release. We got a big release this week. How about some select football on Wednesday? Group Break spots are listed. Again, LaytonSportsCards.com. For those of you on TikTok, you just click the link in our bio. Again, we've got group breaks listed for Wednesday. Take your team and random team styles, so definitely be sure to check those out as those spots are going super fast. All right, we've got a Keon Johnson Ricky Base for the Blazers. Jimmy Buckets for the E and A Halliburne Pacers. It's always interesting to see what Jersey is going to be in, because like half of the product this year, he's in the Kingsdale, the other half, he's in the Pacers, Jersey, so it's always interesting to see. We've got out of 35. How about a Kendrick Perkins on the Orange Volcanic? Big Perk. Big Perk. A little orange off-color match there for OKC. Cool one there, Jeffrey. Yeah, I know. That was a great box. Congrats again on those. Really cool looking cards. Then out of 149 for the Spurs Aurora. How about a little artist, Gilmore? Nice. How about a throwback box here? Some really cool names. There you go, Jeffrey. Gilmore. Not 2Ks, maybe. Hey, Kendrick Perkins. We have Purple Rookie to 100 of your 7 for the Heats. And Orange to 50, Hakim the Dream. A little Elijah on there. Nice throwback box here for you, Jeffrey. Some really nice names. All right, that'll do for your box. Congrats on those, and we'll be getting them right out to you.


Hello, thank you for joining me. I'm on a train heading through to London Suburban, which just departed London, Victoria. This train goes to Ashford International. We're going to Swansea to visit the Swansea New Barn Railway. It's another episode of Miniature Railway Britain. Never been to this railway before, so I'm really looking forward to visiting. I understand they are running some steam there, so that would be good. I'm on the train. One thing I would say is I do like trains where they have a nice sit-in-the-mail station, but it's good for a tunnel. I do like it when they have a nice four-plus-four seat. It's just so much more civilised than seats that don't line up with windows and all that kind of things. I feel I can sit here. I can really enjoy the journey. I've got my OS map on the table and my little London Rail Atlas, all the way where we're going to, is just outside London. So I can sit down and just enjoy the journey.  But this is to me how if all train journeys were like this, it'd be perfect. It'd be awesome. The tunnel in a minute. Somewhere above us, there's the old railway to Crystal Palace, passes over this tunnel. I'll have to go and explore that line one day. So I'm going to be going through this tunnel for a while, so I'm just going to sit back, enjoy the journey, and soon we shall be at Swanley. Here we are. I've arrived at Swanley. I've never been to this railway station before. I've been through it many times. We'll watch our train go out, and then there's a freight due quite soon, so I'm going to hang around to see that pass through, and then we're going to go and find the Swanley New Barmanage Railway. That was good to see a freight train pass through. We're now going to make our way out the station. This is something I wish they wouldn't do, putting frosted glass in, because it just means, you know, people like me like to be able to see the trains, not see them. And it seems pointless. I don't know why they do that. Anyway, come out the station. They don't have ticket barriers here. There is a ticket office, but that's at the other entrance, and that's not the entrance we want. So we come out here, and we appear to be in the housing estate. That's what I'm going to do. We are at Swanley Station. I'm going to make my way through this housing estate, and let's go and find the Swanley New Barmanage Railway. So, here we are. I've arrived at Swanley Park. However, I think it's just starting to improve, and my J is generally improving, because we've gone from less rain and steam. Here we have a steam locomotive. And I've seen that locomotive before. She was at Barnard from Hitcher Railway last year. About a year ago it was, so you'll see that video. I'm fairly sure I'll travel behind that locomotive. There's another one coming this way, and there's this one here. It's what's going across. It's actually stopping for water, I think. There's another one coming up the bank, so we've got quite a lot going on. There's a tinkerbell coming up the bank, so it's like an out-and-back line.  This is what's going to go past. So here we have a busy railway station with four locomotives in steam. This is perfect. It's a really nice way to spend an autumn Sunday afternoon. So I'm not sure which one's going to go out next, but we'll ride behind them all. Is that one going? So we'll have a ride, try and ride behind them all, and we'll also do some line driving. Let's watch this one go. Looks great. Let's go and have a ride. So here we are. I've got my ticket. We're about to go for a ride. There's a steam loco here. Let's just have a look and see what we've got. There's about four locomotives out today. Old Rosie, I think, if you're going to take us for a ride. And there's another steam loco here, just reversing onto the other train. So what we'll do, we'll jump on this one and we'll go for a ride. We'll go to the intermediate station and we'll get off there and then we'll come back on another train. That was a really fun trip. I was thinking of getting off at the Parkway station, but I thought we'd do. I was enjoying it so much I just decided I'd stay on. So what we'll do now is we'll go for a walk and we can get some line side shots and then when we get to the Parkway, I'll either get a train back or a walk. I haven't quite decided. So I've just been looking at what happens. When the locos have worked the train, they come up here to the headshunt and then as the thinker bell is now, they will reverse and work the next train. If you have a look here, there's another set of carriages there. It's not particularly busy today because of the weather, but I've been told when the weather is nice, it's very, very busy. Also, the loco there, Kestrel, now that's a nice loco. The real one got exported to Russia and it's always hoped that she'll be found eventually, but it's probably likely that she was scrapped. This is a Mardike loco and I've got a few of these here, but today it seems to be an all steam passenger. So I'm going to walk off down there and we'll see what sort of line side shots we can get. So here we are out in the park. It's been nice watching the trains at the line side. Interestingly, the line, as we've seen from when we rode on it earlier, is a giant balloon loop. It opened in 1986. Now this was the original line. It was a there and back and there would have been a parting loop somewhere along here. What was the first part of our ride? That is actually newer. That was added a year later in 1987. And the idea was that down here is the car park. The people who drive, obviously I came by train but if I was to have driven, I probably would have parked down here. And you can catch a train from the car park up to the other end of the park. There was a boat in Lake. I didn't really have a look at it because it was raining so much. But I noticed there is a boat in Lake and various other activities. So this railway performs a full service. If you arrive by car, you take the train to the other side of the park. Or if you just come by car today, you have a nice ride on the train. So we're just coming up to the station now. And I'm just here, looks like there's a train departing. Yes, it's Old Rosie which we had a ride behind. So let's watch her go past and then we'll go and have a look at the station. Something else running behind Old Rosie. Looks like they're having a bit of fun with them taking their skates and for a ride. That's quite amusing. Anyway, what we're going to do, let's have a look at the station up here. And the reason I want to show you this station is it's something unique. As in it's a former station we're quite used to on the national network. Whether we use them or we're just on the train and our train happens to stop at one. Let's just check. So crossing, yeah, no trains coming. We probably all use or pass through these stations. But have we ever seen this station at a miniature railway? Walking up here, I think this might be unique this station. The reason why I'll show you what the name is. The station is called Swanly Parkway. Is this the only parkway station on a miniature railway? I certainly can't think of any others. There might be others that perform a similar function but to actually have the station with a parkway name, I think is unique. Of course, if you don't know of any others, please do comment and tell me. Here comes a tinkerbell just arriving. If you know of any other parkway stations on a miniature railway, then do let me know. But I think this may well be unique. I think that train's stopping. So, yeah, that's exciting. Last time I visited a parkway station, or one of the newest ones, it wasn't actually the last one, but it was in Kent, was a fanate parkway. If you want to see that, have a look at the link on screen now. I went to a fanate parkway station earlier in the year. Here you can see it's quite a gradient coming down here. So when you're on the trains, they're not really working particularly hard here, but it's down. And then when I get round the corner, the gradient begins because obviously the other station is at the top of the hill. So the second part of the ride I found more exciting because the logo was really having to work hard to get up that hill. What I think we'll do now, we'll perhaps just, I'm either going to wait for a train or walk back and just watch trains really and then maybe have another ride. Well, having had fun riding the trains, I've just been in this little cafe here, got myself a coffee. The railway station is literally there next door. So when you come to this park, the other things you can do here, if you've got young children over there, there are some children's paddling pools. And there's a lake, as you can see, there's these swambo, so you can have a bit of fun on the lake. And if you've come by car, you would have taken the train from the parkway station to the railway station here. You can just see this steam rising up from there. So that's how, you know, this railway is great and it really does perform an actual function. If we have a look here at the station, there's a diesel train arriving. What I really like about this junction though as you go out is it's possibly one of the busiest miniature railway junctions there is. Look, I mean what we've got going on now, we've got this double departure now. I think, yeah, one steam train is going into the head shut and the other is going off with a passenger train. So what happened is when the trains get to the end of the line, the loco goes off on the turn there.  There's a loco up there now and then they reverse down onto one of the trains and then they depart with passenger trains. You can just see trains going off over there. So it's really, really quite exciting. It just seems there's constant activity, don't they? Diesel train, you can see the passengers look like they're really enjoying themselves. So I've had a really good afternoon here. If you'd like to come and visit this railway, then, you know, they run, I think, pretty much every weekend, probably from Easter to the end of October. So do come and visit them. I've really enjoyed it here. So thank you very much for watching. Please do feel free to like, subscribe and comment in those little tinkerbell reverses onto a train. Goodbye.