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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, especia

• mattkrause 10y ago
  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_computation https://www.researchgate.net/publication/7712549_Dendritic_c... ), along with this short review (http://www.nature.com/neuro/journal/v3/n11s/full/nn1100_1171.html 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/S0959438814000130 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/ 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_plasticity); 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/ http://www.cns.nyu.edu/~rinzel/CMNSF07/ Those papers are a good place to start!
  • mattkrause 10y ago
    (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 https://www.cs.toronto.edu/~hinton/backpropincortex2014.pdf Video: http://sms.cam.ac.uk/media/2017973?format=mpeg4&quality=720p 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_NeuralSystemsWorkshop_11Dec2015.pdf https://www.iro.umontreal.ca/~bengioy/talks/NIPS2015_NeuralS... preprint: https://arxiv.org/abs/1502.04156 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 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 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 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 http://jn.physiology.org/content/80/1/1, but they have lots of similar papers: http://www.neuroscience.cam.ac.uk/directory/profile.php?Schultz 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/full 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/Olshausen-nature-paper.pdf 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/journal.pcbi.1003761 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.