Stats for nerds
4recommend
6mentions
6people
2009–2024years
67% recommend33% neutral
Mentions per year
What Hacker News says
- Vision by David Marr
Heuristics by Judea Pearl
The Art of Doing Science and Engineering by Richard Hamming
- 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!
- - David Marr: "Vision"
The foundations of computational neroscience - and lots of practial demonstrations of how your eyes and brain work.
- Daniel Dennett "Consciousness Explained"
How the mind works?
- J.E Gordon: "The new Science of Strog Materials" & " Structures, or why things don't fall down."
How is 'stuff' strong/hard?
- Steve Grand: "Creation" & "Growing up with Lucy"
The programmer behind the game 'Creatures' and his adventures in artificial life.
- Joseph Campbell: "The Hero with a Thousand Faces"
Never look at Star Wars in the same way again.
- David Deutch: "The Fabric of Reality"
Many worlds or mad as a box of frogs?
- Richard Leakey & Roger Lewin: "The Sixth Extinction"
We're doomed - maybe
- Braitenberg: "Vehicles"
Emergent behaviour from simple rules
- Ian Wilmut, Keith Cambell & Colin Tudge: "The Second Creation"
How Dolly the Sheep was made. Takes you through the background such that you can read & understand their paper which appears at the end.
- John Brunner: "The Shockwaver Rider"
Snowcrash - hah! - 1975 and this dude got it already.
Other general authors:
Martin Gardner, Greg Egan (lots of free stuff on his site).
- David Marr is amazing. I remember being completely awed by Vision. I think more than any other author, Marr showed that cognition could be made computationally tractable (focus on vision, but before him it was just a mess).
I don't know that there's much here to discuss though. His work has been supersceded in just the way any researcher would want.
- Yeah the internal representation of organic neural networks are also weird - check out the signal processing that occurs between the retina and the various parts of the visual cortex before any decent information can emerge from the signal - David Marr's 1980s book Vision is a mathematically chewy treatise on this. This leads me to start thinking that human intuition may well caused by different neural network subsystems feeding processed data into other subsystems where consciousness and thus intuition and explanation emerges.
Organic neural networks are pretty energy efficient in comparison- although still decently inefficient compared to other body systems - so there is the capacity to build things out to the scale required, assuming my read on what's going on there is correct, that is. So it's not clear to me that the energy inefficiency of ANNs can be sufficiently resolved to enable these multiple quasi-independent subsystems to be built at the scale required. Not even if these interesting looking trinomial neural nets which are matrix addition based rather than multiplication come to dominate the ANN scene.
While I was thinking this comment through I realised there's a possible interpretation wherin human activity induced climate change is an emergent property of the relative energy inefficiency of neural architecture.
- I think the most interesting and relevant strand of philosophy of mind is the connectionism of Rumelhart, McClelland and the PDP Research Group (including "godfather of deep learning" Geoff Hinton), exemplified in the tome "Parallel Distributed Processing" released in 1986
It even has an accompanying manual on how to implement artificial neural networks.
http://www.cnbc.cmu.edu/~plaut/IntroPDP/index.html
More along this line is found in David Marr's Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
https://www.youtube.com/watch?v=9WcIiSCDqhE
There is a spiritual successor to the PDP books called "Rethinking Innateness: A connectionist perspective on development" lead by Jeffery Elman who did some of the first research on Recurrent Neural Networks (RNNs). It makes heavy reference to Chomsky's work on language and innateness
https://crl.ucsd.edu/~elman/Papers/book/index.shtml
This line of work even has heavyweight ancestors in Donald Hebb's work on the neural basis of learning in "The Organization of Behavior" (1949) and the noted austrian economist(?)/classical liberal and anti-communist Frederick Hayek covered similar lines in his book "The Sensory Order" (1952)
I think this tradition is carried on in the work of Yann Lecun, who recently released a talk titled "Deep Learning, Structure and Innate Priors"
http://www.abigailsee.com/2018/02/21/deep-learning-structure...
