Brains

Neural networks made of real neurons, in functioning brains

2014-11-03 – 2017-08-15

How do brains work?

Dr. Greg Dunn and Dr. Brian Edwards, Self Reflected

I mean, how do brains work at the level slightly higher than a synapse, but much lower than, e.g. psychology. “How is thought done?” etc.

Notes pertaining to large, artificial networks are filed under artificial neural networks. The messy, biological end of the stick is here. Since brains seem to be the seat of the most flashy and important bit of the computing taking place in our bodies, we understandably want to know how they works, in order to

  • fix Alzheimers disease
  • steal cool learning tricks
  • endow the children of elites with superhuman mental prowess to cement their places as Übermenschen fit to rule the thousand year Reich
  • …or whatever.

Real brains are different to the “neuron-inspired” computation of the simulacrum in very many ways, not just the usual difference between model and reality. The similitude between “neural networks” and neurons is intentionally weak for reasons of convenience.

For one example, most simulated neural networks are based on a continuous activation potential and discrete time, unlike spiking biological ones which are driven by discrete events in continuous time.

Also real brains support heterogeneous types of neuron, have messier layer organisation, use less power, don’t have well-defined backpropagation (or not in the same way), and many other things that I as a non-specialist do not know.

To learn more about:

  • Just saw a talk by Dan Cireșan in which he mentioned “Foveation” - blurring the edge of an image when training classifiers on it. The allure of ignoring stuff to learn it. What is, rigorously speaking, happening there? Nice actual crossover with deep learning.
  • Algorithmic statistics of neurons sounds interesting.

Fun data

Allen Institute Brain Observatory.

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Kass, Robert E., Shun-Ichi Amari, Kensuke Arai, Emery N. Brown, Casey O. Diekman, Markus Diesmann, Brent Doiron, et al. 2018. “Computational Neuroscience: Mathematical and Statistical Perspectives.” Annual Review of Statistics and Its Application 5 (1): 183–214. https://doi.org/10.1146/annurev-statistics-041715-033733.

Katahira, Kentaro, Kenta Suzuki, Kazuo Okanoya, and Masato Okada. 2011. “Complex Sequencing Rules of Birdsong Can Be Explained by Simple Hidden Markov Processes.” PLoS ONE 6 (9): –24516. https://doi.org/10.1371/journal.pone.0024516.

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