  Recent advances in experimental techniques have allowed the simultaneous
recording of populations of hundreds of neurons, allowing more comprehensive
investigation into the nature of the collective structure of population neural
activity. While recent studies have reported a phase transition in the
parameter space of maximum entropy models describing the neural probability
distributions, the interpretation of these findings has been debated. Here, we
suggest that this phase transition may be evidence of a `structured',
collective state in the neural population. We show that this phase transition
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.
