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Edit: hmm not sure why this comment is getting downvoted.

Backprop isn't just a leaky abstraction. There's not really any evidence yet that biological neural networks use anything like backprop. So its important that students be taught the low-level aspects of the current state of the art so that better architectures can be invented in the future.

(Note: at the very end of this comment, I am leaving a link to one hypothesis about how something like backprop may happen at the dendritic level.)

I've been doing a deep dive on ML and bio neurons in the last week or so to try and get up to speed. The feedforward networks with backprop that are doing such awesome work right now share almost nothing in common with actual neurons.

Just some differences: (neuron == bio, node == artificial)

Terms: Neuron resting potential is about -70mV. Action potential (threshold) is about -55mV.

1) Neurons are spikey and don't have weights. A neuron sums all of its inputs to reach a threshold. When that threshold is reached, an electric current (action potential) is spread equally to all synaptic terminals. A node has a weight value for each input node that it multiplies by the value received from the input node. All of these values are then summed and passed to the connected nodes in the next layer.

2) There are multiple types of neurons, but for simplification there are 2 types that called "excitatory" and "inhibatory". A neuron can mostly only emit "positive" or "negative" signals via the synapse. There are different neurotransmitters that either add or subtract to the voltage potential for each dendritic input. The total sums work together to cross the threshold voltage. I say mostly because there is some evidence for a minority of neurons to be able to release neurotransmitters from both groups.

3) Artificial networks are generally summation based with no inclusion of time. Bio networks use summation and summation over time together to determine if they should fire or not. If I recall correctly, a neuron can repeatedly fire about 300 times per second. A dendritic input can last for up to 1.5 milliseconds. So if a neuron gets enough positive inputs at the same time, or collects enough over time, it will fire.

I haven't found any hypotheses or experiments that try to explain how reinforcement learning takes place in neurons yet in the absence of back propagation. Pretty sure that information is out there but I haven't run across it.

Overall I think that there will be multiple engineering approaches to AI, just like there are to construction and flight. We understand how birds and bees fly, but we don't build planes the same way.

Its important to remember than cognition is based in physics just like any other physical system. When the principles are well understood, there are multiple avenues to using them.

Here is a short collection of links that I've been finding helpful.

https://en.wikipedia.org/wiki/Neurotransmission

http://neuroscience.uth.tmc.edu/s1/introduction.html

https://www.quora.com/Can-a-synapse-change-from-being-inhibi...

https://en.wikipedia.org/wiki/Dale's_principle

https://en.wikipedia.org/wiki/Neural_backpropagation




In terms of big picture stuff, you're absolutely right that many DNNs are more "inspired by" the brain and less a faithful model of it. However, a lot of the things mentioned in your post are either overstated or outright wrong. For example:

1. Neurons, or more specifically, connections between neurons ("synapses"), absolutely do have weights, and the "strength" of synapses can be adjusted by a variety of properties that act on a scale of seconds to hours or days. At the "semi-permanent" end of the spectrum, the location of a synapse matters a lot: input arriving far from the cell body has much less influence the cell's spiking. The number (and location?) of receptors on the cell surface can also affect the relative impact of a given input. Receptors can be trafficked to/from the membrane (a fairly slow process), or switched on and off more rapidly by intracellular processes. You may want to read up on long-term potentiation/depression (LTP/LTD), which are activity-dependent changes in synaptic strength. There are a whole host of these processes, and even some (limited) evidence that the electric fields generated by nearby neurons can "ephaptically" affect nearby neurons, even without making direct contact, which would allow for millisecond-scale changes.

2. While you can start by dividing neurons in excitatory and inhibitory populations, there's a lot more going on. On the glutamate (excitatory) side, AMPA receptors let glutamate rapidly excite a cell and make it more likely to fire. However, it also controls NMDA channels that, under certain circumstances, allow calcium into a cell. These calcium ions are involved in all sorts of signaling cascades (and are involved--we think--in tuning synaptic weights). GABA typically hyperpolarizes cells (i.e., makes them less likely to fire) and is secreted by cells called interneurons . However, there's a huge diversity of interneurons. Some seem to "subtract" from excitatory activity, others can affect it more strongly in a divisive sort of way or even cancel it completely. Furthermore, there's a whole host of other neurotransmitters. Dopamine, which is heavily involved in reward, can have excitatory or inhibitory effects, depending on whether it activates D1 or D2 receptors

3. While the textbook feed-forward neural networks certainly have "instant" signal propagation, there are lots of other computational models that do include time. Time-delay neural networks are essentially convnets extended over time instead of space. Reservoir computing methods like liquid state machines also handle time, but in a much more complicated way.

4. I chuckled at the idea of finding a biological correlate analog for reinforcement learning, since reinforcement learning was initially inspired by the idea of reinforcement in psychology/animal behavior. People have shown that brain areas--and individual neurons within them--encode action values, state estimates, and other building blocks of reinforcement learning. Clearly, we have a lot to discover still, but the general idea isn't at all implausible.

Finally, some people are fairly skeptical that the fields have much to learn from each other; Jürgen Schmidhuber said this a lot at NIPS last year. However, other, equally-smart people (e.g., Geoff Hinton) seem to think that there may be a common mechanism, or at least a useful source of inspiration there. But, if you want to work on something like this (and it is awesomely interesting), it really helps to have a solid grounding in both.


This is exactly the kind of response I was hoping for. Thanks Matt! If it's not an inconvenience, could you drop any links to the topics you referenced, especially the ones that differed from what I've been studying, to charles@geuis.com? I'm going a bit deeper now and reading some studies from the early 90's and some that are more recent. It's kind of a crapshoot of what I can google for, so a guided search would be immensely helpful.


Hmmm...it's hard to do entire fields justice, but here's an attempt.

There are a couple of standard neurobiology textbooks, like Kandel, Jessel, and Schwartz's Principles of Neural Science, Purve et al.'s Neuroscience and Squire et al.'s Fundamental Neuroscience. These are huge books that cover a bit of everything, and you should know that they exist, but I wouldn't necessarily start there.

If you're specifically interested in computation, I would start with David Marr's Vision. It's quite old, but worth reading for the general approach he takes to problem-solving. He proposes attacking a problem along three lines: at the computational level ("what operations are performed?"), the algorithmic level ("how do we do those operations?"), and the implementation level ("how is the algorithm implemented").

From there, it depends on what you're interested in. At the single-cell level, Cristof Koch has a book called The Biophysics of Computation that "explains the repetoire of computational functions available to single neurons, showing how individual nerve cells can multiply, integrate, and delay synaptic input" (among other things). Michael London and Michael Häusser have a 2005 Annual Reviews in Neuroscience article about dendritic computation that hits on some similar themes (here: https://www.researchgate.net/publication/7712549_Dendritic_c... ), along with this short review (http://www.nature.com/neuro/journal/v3/n11s/full/nn1100_1171...) by Koch and Sergev, and a 2014 review by Brunel, Hakim, and Richardson (http://www.sciencedirect.com/science/article/pii/S0959438814...). Larry Abbott has also done interesting work in this space, as have Haim Sompolinsky and many others. Gordon Shepard and his colleagues maintain NEURON (a simulation package/platform) and a database of associated models (ModelDB) here: https://senselab.med.yale.edu/ if you want something to download and play with (they also do good original work themselves!)

Moving up a bit, the keywords for "weight adjustment" are something like synaptic plasticity, long-term potentiation/depression (LTP/LTD), and perhaps spike-timing dependent plasticity. The scholarpedia article on spike-timing dependant plasticity is pretty good (http://www.scholarpedia.org/article/Spike-timing_dependent_p... Scholarpedia is actually a pretty good resource for most of these topics. The intro books above will have pretty good treatments of this, though maybe not explicitly computational ones.

More to come, however I also just found this class from a bunch of heavy-hitters at NYU: http://www.cns.nyu.edu/~rinzel/CMNSF07/ Those papers are a good place to start!


Great info. Definitely have plenty to read over the next couple weeks now.


(cont)

I probably should have lead with this, but there's been a lot of interest in backprop-like algorithms in the brain

* Geoff Hinton has a talk (and slide deck) about how back-propagation might be implemented in the brain. (Slide deck: https://www.cs.toronto.edu/~hinton/backpropincortex2014.pdf Video: http://sms.cam.ac.uk/media/2017973?format=mpeg4&quality=720p )

* As always, the French part of Canada has its own, slightly different version of things, care of Yoshua Bengio (slide deck from NIPS 2015: https://www.iro.umontreal.ca/~bengioy/talks/NIPS2015_NeuralS... preprint: https://arxiv.org/abs/1502.04156 )

* Here is another late 2015 take on back-prop in the brain by Whittington and Bogacz (http://biorxiv.org/content/early/2015/12/28/035451) This one is interesting because they view the brain as a predictive coding device which is continuously estimating the future state of the world and then updating its predictions. (I think the general predictive coding idea is cool and probably under-explored).

* There's a much older paper by Pietro Mazzioni, Richard Andersen, and Michael I. Jordon attempting to derive a more biologically plausible learning rule (here: http://www.pnas.org/content/88/10/4433.full.pdf) This work is particularly neat because it builds on earlier work by Zipser and Andersen (https://www.vis.caltech.edu/documents/54-v331_88.pdf), who trained a three-layer network (via back-prop) to transform data from gaze-centered ('retinotopic') to head-centered coordinates, and noticed that the hidden units performed transforms that look a lot like the work done by individual neurons in Area 7A. The Mazzioni paper then replaces the backprop with a learning procedure that is more biologically plausible.

For backprop, you need some sort of error signal. Wolfram Schultz's group has done a lot of work demonstrating that dopamine neurons encode something like "reward prediction error." (e.g., this: http://jn.physiology.org/content/80/1/1, but they have lots of similar papers: http://www.neuroscience.cam.ac.uk/directory/profile.php?Schu...). For reinforcement learning, you might also want to maintain some sort of value estimate. There are tons of studies looking at value representation in orbitofrontal cortex (OFC), using mostly humans and monkeys, but occasionally rats. Here's a review from Daeyeol Lee and his postdoc Hyojung Seo describing neural mechanisms for reinforcement learning (http://onlinelibrary.wiley.com/doi/10.1196/annals.1390.007/f... ) The Lee lab has done a lot of interesting value-related things too.

Switching gears slightly, there is also considerable interest around unsupervised learning and related methods for finding "good" representations for things. This is potentially interesting because it would allow for improvements within individuals and even across individuals (e.g., by evolution).

Olshausen and Fields kicked this off by demonstrating that maximizing the sparseness of a linear code for natural images produces a filter bank that resembles the way neurons in primary visual cortex process images. (http://courses.cs.washington.edu/courses/cse528/11sp/Olshaus...)

Michael Lewicki has done similar things in a variety of sensory modalities. Here's a recent paper from him looking at coding in the retina (http://journals.plos.org/ploscompbiol/article?id=10.1371/jou...) but he has similar work in the auditory system and building on the Olshausen and Fields paper linked above to explain complex cells (and more!) in visual cortex. Bill Geisler has also done a lot of work looking at the statistics of natural scenes and how the brain (and behavior) appears to be adapted for them.


@geouis asked you to email him some reading material on this in a sibling comment. if you do send them something, could you post it here too?


One hypothesis is that the neural units in a CNN model hundreds or thousands of individual neurons, including some neurons used to produce/transmit error signals.

This seems reasonable, but I doubt there is any single simple and general abstraction that can describe the learning algorithms used by physical neurons. It seems more likely that the brain uses many highly specialized algorithms for different regions of the brain, each shaped by a ~billion years of evolution.


If you're interested in Reinforcement Learning and Spiking Neural Network, you should look into Izhikevich work on dopamine-modulated STDP:

http://www.izhikevich.org/publications/dastdp.pdf




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