Ising models for networks of real neurons

By Gasper Tkacik, Elad Schneidman, Michael J. Berry, William Bialek. 2006

146 citations recorded by the source corpus.

Ising models with pairwise interactions are the least structured, or maximum-entropy, probability distributions that exactly reproduce measured pairwise correlations between spins. Here we use this equivalence to construct Ising models that describe the correlated spiking activity of populations of 40 neurons in the retina, and show that pairwise interactions account for observed higher-order correlations. By first finding a representative ensemble for observed networks we can create synthetic networks of 120 neurons, and find that with increasing size the networks operate closer to a critical point and start exhibiting collective behaviors reminiscent of spin glasses.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/1c511db77c701379dd7f2c383141de9164a1e0f6

Citations in the corpus

• Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design (2013, unknown): “…we follow the maximum entropy principle (Jaynes, 1957) to seek the least biased model capable of reproducing the observed probabilities of occurrence of every single and double mutation (Mora & Bialek, 2011; Tkacik et al., 2006; Tkacik et al., 2009), and use it to predict higher order mutations.”