Canal, G. P.; Ferraro, N. M.; Evans, T. E.; Osborne, T. H.; Menard, J. E.; Ahn, J. -W.; Maingi, R.; Wingen, A.; Ciro, D.; Frerichs, H.; Schmitz, O.; Soukhanovskii, V.; Waters, I.; Sabbagh, S. A.
Fredrickson, E. D.; Belova, E. V.; Battaglia, D. J.; Bell, R. E.; Crocker, N. A.; Darrow, D. S.; Diallo, A.; Gerhardt, S. P.; Gorelenkov, N. N.; LeBlanc, B. P.; Podesta, M.
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.
