Rewrite the textbooks: Findings challenge conventional wisdom of how neurons operate

Feb 17, 2011
Brain diagram. Credit:




The findings are published in the February issue of the journal .














More information: The title of the paper is “Slow Integration Leads to Persistent Action Potential Firing in Distal Axons of Coupled Interneurons.”… n2/full/nn.2728.html

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User comments : 15

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3.6 / 5 (7) Feb 17, 2011
Once again rewriting the textbooks on neuroscience. Looks like they might have to redesign neural nets.
2.3 / 5 (3) Feb 17, 2011
This is how computers become intelligent
3.9 / 5 (7) Feb 18, 2011

Is this similar to how memristors work? maybe you could use them to simulate a physical neural net.
3.8 / 5 (5) Feb 18, 2011
Time to update the Blue Brain project
3 / 5 (2) Feb 18, 2011
Does this explain different cycles of action & thought?

Sometimes parts of the body react quickly to external stimuli, e.g. before we are consciously aware of the stimuli.

Is there a connection between slower axon communication and conciousness, or am I dreaming ?
2.5 / 5 (4) Feb 18, 2011
Sounds like there is still a lot we don't know about how a brain works. Which means our attempts to recreate it electronically, like electronic neurons and neural nets, are still not recreating what the brain does. That would explain why they don't simulate a brain very well yet. I would think that once we know how the brain works we can simulate it electronically, I also still don't think even that will become self aware. Even if it did how would we know it wasn't just simulating self awareness?

We may need a new term for it like apparent self awareness. If it happens there will be 2 camps that would argue about it forever, one will say it is and one will say it isn't. Just like a lot of other things.
3.8 / 5 (4) Feb 18, 2011
. Looks like they might have to redesign neural nets.

Not really since the neural nets in computer science are highly abstract.

Neural nets only model connections from node A to node B. The connections are parametrized by signal strengths and transmission times (in the simplest cases). In order to incorporate 'slow' axon action you just need to model each biological axon as two connections: one slow and one fast.
To model the 'axon computation' mentioned in the article you insert a virtual node in each connection (i.e. you treat each connection also like a node).


Neural nets are modelled in software, not hardware (there used to be some dedicated hardware but it has long been superceded by software modeling). Hardware has the disadvantage that it's tricky to make/break connections to other nodes or insert/delete nodes dynamically.
3 / 5 (4) Feb 18, 2011
Neural nets are modelled in software, not hardware

Yes, but software simulation is much slower and so the more complex the simulation, the slower it becomes.

Also true, which is why it's difficult to make progress. FPGA's have the reconfigurability, but simply can't approach the complexity required.
3.8 / 5 (4) Feb 18, 2011
Another problem with FPGAs is that you can't easily model signal strength nor signal time very well. Both are pretty much determined by the the hardware. In software the simulation is much easier - and with parallel processing not noticeably slower. Also you are only limited by the size of your memory as to the amount of neurons/connections you can have. with swapping (which slows stuff down, though) you're not even limited to that.
4 / 5 (4) Feb 18, 2011

Actually, it was thought for long that the backpropagation algorithm wasn't biologically plausible because it needs backwards connections. This study shows it's indeed the case and brings more biological plausibility to the Multi Layer Perceptron (just one type of artificial neural network among many).

antialias already gave excelent explanations about how artificial neural networks are abstracted from natural ones and how to simulate some behaviors. I just want to add that the slow connections could be (and already are) modeled by introducing unitary delays.

Another interesting thing is that there is a model of artificial neuron that uses "leaky integration". In other words, it reacts to stimuli history using a local decaying memory, exactly as the study has found.
4.3 / 5 (6) Feb 18, 2011

Simulating self awareness and being self aware are equal; as long as the simulation is flawless. You can only ever be sure of your own consciousness anyway.
2 / 5 (1) Feb 18, 2011


They are no more equal than an orange is to one made of antimatter. Looks the same, acts the same, but isn't the same. As for the last statement, I've said that more than once.

Evidently considering the marks I got for my comment I was wrong about us not knowing how the brain works and the mark givers must have already known what was in this article. What's more you must already know more than researchers now know so why don't you enlighten us?
not rated yet Feb 19, 2011
They probably should have figured this out when they noticed that chemicals being processed by the brain don't travel one way through the synapse, but instead bounce back and forth until they're used up.
1 / 5 (2) Feb 22, 2011
This article is fascinating! Is it perhaps a 'mirror of neural pathways' that are yet to be revealed? I believe that this will ultimately be proven. The human mind is quantum! We are in the universe.. and the universe is inside us. We are the 'dark matter' and the 'super novas'. We are the 'black holes' of intense gravity.. and yet lighter than photons. Logic, reason, and creativity. All three are hardwired in the human mind. A quantum leap is now in order. It only requires truth and spirit to carry it forward.
1 / 5 (1) Feb 22, 2011
I'm thinking that this could lead to the discovery of where our consciousness resides.


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