Thermodynamics and signatures of criticality in a network of neurons. Author Gašper Tkačik, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer, Michael Berry, William Bialek Publication Year 2015 Type Journal Article Abstract The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. Keywords Animals, Brain, Models, Neurological, Neurons, Algorithms, Nerve Net, Entropy, Hot Temperature, Models, Statistical, Monte Carlo Method, Probability, Reproducibility of Results, Retina, Thermodynamics, Urodela Journal Proc Natl Acad Sci U S A Volume 112 Issue 37 Pages 11508-13 Date Published 2015 Sep 15 ISSN Number 1091-6490 DOI 10.1073/pnas.1514188112 Alternate Journal Proc Natl Acad Sci U S A PMCID PMC4577210 PMID 26330611 PubMedPubMed CentralGoogle ScholarBibTeXEndNote X3 XML