The structured ‘low temperature’ phase of the retinal population code

Mark L Ioffe and Michael J Berry

PLOS Computational Biology, 2017, vol. 13, issue 10, 1-31

Abstract: Recent advances in experimental techniques have allowed the simultaneous recordings of populations of hundreds of neurons, fostering a debate about the nature of the collective structure of population neural activity. Much of this debate has focused on the empirical findings of a phase transition in the parameter space of maximum entropy models describing the measured neural probability distributions, interpreting this phase transition to indicate a critical tuning of the neural code. Here, we instead focus on the possibility that this is a first-order phase transition which provides evidence that the real neural population is in a ‘structured’, collective state. We show that this collective state is robust to changes in stimulus ensemble and adaptive state. We find that the pattern of pairwise correlations between neurons has a strength that is well within the strongly correlated regime and does not require fine tuning, suggesting that this state is generic for populations of 100+ neurons. We find a clear correspondence between the emergence of a phase transition, and the emergence of attractor-like structure in the inferred energy landscape. A collective state in the neural population, in which neural activity patterns naturally form clusters, provides a consistent interpretation for our results.Author summary: Neurons encoding the natural world are correlated in their activities. The structure of this correlation fundamentally changes the population code, and these effects increase in larger neural populations. We experimentally recorded from populations of 100+ retinal ganglion cells and probed the structure of their joint probability distribution with a series of analytical tools inspired by statistical physics. We found a robust ‘collective state’ in the neural population that resembles the low temperature state of a disordered magnet. This state generically emerges at sufficient correlation strength, where the energy landscape develops an attractor-like structure that naturally clusters neural activity.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1005792

DOI: 10.1371/journal.pcbi.1005792

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