Spin glass models for a network of real neurons

By Gasper Tkacik, Elad Schneidman, Michael J. Berry, William Bialek. 2009

99 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 salamander retina responding to natural movies. We show that pairwise interactions between neurons account for observed higher-order correlations, and that for groups of 10 or more neurons pairwise interactions can no longer be regarded as small perturbations in an independent system. We then construct network ensembles that generalize the network instances observed in the experiment, and study their thermodynamic behavior and coding capacity. Based on this construction, we can also 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. We examine closely two such behaviors that could be relevant for neural code: tuning of the network to the critical point to maximize the ability to encode diverse stimuli, and using the metastable states of the Ising Hamiltonian as neural code words.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/20c20f0aa4d608b24aa6dc30be69b136806f9417

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Citations in the corpus

Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “Sur-prisingly, also in neural data (not generated by an equilibrium model), three-point correlations are predicted well by a model with pairwise interactions [206, 210].”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “19 And since the inferred interactions among “spins” have both signs, they are a sort of spin glasses (Tkačik et al., 2013; Tkacik et al., 2009).”
Hierarchical model of natural images and the origin of scale invariance (2013, background): “3B) may also explain the critical structure of spikes from retinal ganglion cells responding to natural images (21).”
Being Critical of Criticality in the Brain (2012, methodology): “…to neuronal activity patterns found in actual data (Schneidman et al., 2006; Shlens et al., 2006, 2009; Tang et al., 2008; Yu et al., 2008, 2011; Tkacik et al., 2009; Yeh et al., 2010) you are entirely right to say that the Ising model is far too simple to completely capture all neural…”