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I think that (speaking as a neuroscientist) one important physiological aspect this intro glosses over, that would be relevant to any of you machine learning gu

I think that (speaking as a neuroscientist) one important physiological aspect this intro glosses over, that would be relevant to any of you machine learning gurus, is how neurons manage their synaptic weights. Most artificial neural network models I've seen utilize a partial derivative-based backprop algorithm to update 'synaptic' weights. In neurons these weights (in the case of fast excitatory transmission) are proportional to the number of glutamate receptors currently in a given synapse. During learning (let's say associative learning, where I associate the sound of your voice to the visual info about your face) something happens to increase the number of these receptors at key synapses. That way, the next time the upstream neuron fires, it more easily elicits activation from the downstream neuron, because it has more receptors at those synapses. So the same quanta of neurotransmitter release by the neurons activated by the unique auditory signature of your voice will be released at all its terminals, but since the downstream terminals for the visual info of your face have upregulated their receptor number, those neurons are going to be easily activated. (thus when you call me on the phone, your voice will readily activate those visual neurons for your face, and I remember what it looks like). If you are interested in seeing how the postsynaptic neuron manages its synaptic receptor numbers (and the intricate process the backprop algo is cheating), you might like to check out a 3D simulation I've made to model this process. Animation: https://www.youtube.com/embed/6ZNnBGgea0Y https://www.youtube.com/embed/6ZNnBGgea0Y Code on github: https://github.com/subroutines/plasticity https://github.com/subroutines/plasticity (note the animation is not too exciting, so you might want to skip forward a few times) But basically you will see that neurons rely on stochastic surface diffusion of receptors to deliver new receptors to synapses. The takeaway is that during rest (or whatever you want to call it... baseline, non-learning, etc) synapses will reach some steady-state number of receptors. If the synapse undergoes some learning event, it needs to 'capture' more receptors to increase the steady-state amount. It does this by modifications to proteins just below the surface of the postsynaptic membrane that act as velcro to surface receptors floating by. Thus synapses take control of their synaptic weights by managing the surface diffusion rate of excitatory receptors.

DanielleMolloy 9y ago
I am working between machine learning and neuroscience, and find such explanations really interesting, so thank you. I would agree that there is currently not a lot of evidence that non-local learning such as backpropagation happens in the brain (event though there are some papers trying to find this evidence). But what you describe still falls into the Hebbian principle ("fire together - wire together") right? And this is still one idea originally engrained in perceptrons. While the brain's local learning algorithm is unknown and would be important to know for machine learning, e.g. to get ahead with unsupervised learning, there is quite some recent evidence that machine learning people are on the right track, at least with what deep convolutional neural networks learn in their hierarchy.
- subroutine 9y ago
  The example I gave was indeed Hebbian. I was merely using it to facilitate understanding of the general physiological mechanism used by neurons to manage their synaptic weights. The same general mechanism is also used in (mono)synaptic efficacy management for non-associative learning (e.g. sensitization, habituation, etc). But I agree there is much we neuroscientists can learn from the machine learning research that provide insight into the biological system. Trust me- we are paying close attention.
nabla9 9y ago
Thank you for interesting comment. Can you give me rough time scale for how quick neurons learn (synaptic plasticity)? I have a recollection that the time scale is in order of tens of milliseconds but I might be wrong.
- radicalOH 9y ago
  There are multiple time scales synaptic plasticity happens on. For instance, ion channels can be moved in or out of the membrane to modulate excitability. On a longer time scale, gene expression can change. Synaptic plasticity can get really complicated, but Kandel's book has some great chapters on mechanism.
- subroutine 9y ago
  As @radicalOH mentions, there are several timescales depending on whether you are talking about immediate, short-term, or long-term synaptic potentiation, each coordinated by a cascade of events (that have been extensively documented in you're interested in knowing more). To specifically address your question about how quickly learning happens in neurons... it's fast. Is it ~10ms? mmm, that seems incredibly fast, and I'm not sure I can provide any definitive answer. I think it heavily depends on what you're willing to interpret as 'learning' when examining events on the molecular level. There are events that engender near-immediate alterations to ongoing transmission; and other events that support lasting electrical changes (necessarily?) develop across longer timescales. You might think of this distinction as the difference between being able to hold a phone number in consciousness via repeating it over and over, and then actually being able to recall that phone number 5 minutes later. In the former certainly something internally must have changed because a moment ago you weren't repeating this number in your head, and now you are. On the other hand, can we really say it was learning if the number is immediately lost the moment it leaves consciousness. Anyway, I"m not going to wax philosophical on that. I'll just show you some data for timescales we think are sufficient to produce lasting increases in synaptic efficacies... Here is a video showing a single spine just after 2-photon 'glutamate uncaging': https://goo.gl/rfgjrW https://goo.gl/rfgjrW (as I mentioned above, glutamate receptors are the primary receptors involved in fast-excitatory transmission; so uncaging tons of glutamate right next to a spine is going to evoke immediate plasticity changes - particularly under these experimental conditions) Note that in just a few seconds the spine has doubled in size. Morphological change in the spine are probably not the first events that alter synaptic transmission, but morphological changes to the spine are a reliable sign of a lasting memory trace. Here is a whole cell recording of the electrical response in a neuron to incoming signals, before and after stimulation like that above ('EPSC' stands for 'excitatory post synaptic currents'): https://i.imgur.com/0OTMwH5.png https://i.imgur.com/0OTMwH5.png

DanielleMolloy 9y ago

I am working between machine learning and neuroscience, and find such explanations really interesting, so thank you. I would agree that there is currently not a lot of evidence that non-local learning such as backpropagation happens in the brain (event though there are some papers trying to find this evidence). But what you describe still falls into the Hebbian principle ("fire together - wire together") right? And this is still one idea originally engrained in perceptrons. While the brain's local learning algorithm is unknown and would be important to know for machine learning, e.g. to get ahead with unsupervised learning, there is quite some recent evidence that machine learning people are on the right track, at least with what deep convolutional neural networks learn in their hierarchy.

subroutine 9y ago
The example I gave was indeed Hebbian. I was merely using it to facilitate understanding of the general physiological mechanism used by neurons to manage their synaptic weights. The same general mechanism is also used in (mono)synaptic efficacy management for non-associative learning (e.g. sensitization, habituation, etc). But I agree there is much we neuroscientists can learn from the machine learning research that provide insight into the biological system. Trust me- we are paying close attention.

nabla9 9y ago

Thank you for interesting comment. Can you give me rough time scale for how quick neurons learn (synaptic plasticity)? I have a recollection that the time scale is in order of tens of milliseconds but I might be wrong.

radicalOH 9y ago
There are multiple time scales synaptic plasticity happens on. For instance, ion channels can be moved in or out of the membrane to modulate excitability. On a longer time scale, gene expression can change. Synaptic plasticity can get really complicated, but Kandel's book has some great chapters on mechanism.
subroutine 9y ago
As @radicalOH mentions, there are several timescales depending on whether you are talking about immediate, short-term, or long-term synaptic potentiation, each coordinated by a cascade of events (that have been extensively documented in you're interested in knowing more). To specifically address your question about how quickly learning happens in neurons... it's fast. Is it ~10ms? mmm, that seems incredibly fast, and I'm not sure I can provide any definitive answer. I think it heavily depends on what you're willing to interpret as 'learning' when examining events on the molecular level. There are events that engender near-immediate alterations to ongoing transmission; and other events that support lasting electrical changes (necessarily?) develop across longer timescales. You might think of this distinction as the difference between being able to hold a phone number in consciousness via repeating it over and over, and then actually being able to recall that phone number 5 minutes later. In the former certainly something internally must have changed because a moment ago you weren't repeating this number in your head, and now you are. On the other hand, can we really say it was learning if the number is immediately lost the moment it leaves consciousness. Anyway, I"m not going to wax philosophical on that. I'll just show you some data for timescales we think are sufficient to produce lasting increases in synaptic efficacies... Here is a video showing a single spine just after 2-photon 'glutamate uncaging': https://goo.gl/rfgjrW https://goo.gl/rfgjrW (as I mentioned above, glutamate receptors are the primary receptors involved in fast-excitatory transmission; so uncaging tons of glutamate right next to a spine is going to evoke immediate plasticity changes - particularly under these experimental conditions) Note that in just a few seconds the spine has doubled in size. Morphological change in the spine are probably not the first events that alter synaptic transmission, but morphological changes to the spine are a reliable sign of a lasting memory trace. Here is a whole cell recording of the electrical response in a neuron to incoming signals, before and after stimulation like that above ('EPSC' stands for 'excitatory post synaptic currents'): https://i.imgur.com/0OTMwH5.png https://i.imgur.com/0OTMwH5.png