Neural networks of different species, brain areas and states can be characterized by the probability polling state
Neural spiking data often reside in a linear probability polling state. In such state, the distribution of neural firing patterns can be accurately captured by the second‐order maximum entropy model, since high‐order effective interaction strengths are small.
Cortical networks are complex systems of a great many interconnected neurons that operate from collective dynamical states. To understand how cortical neural networks function, it is important to identify their common dynamical operating states from the probabilistic viewpoint. Probabilistic characteristics of these operating states often underlie network functions. Here, using multi‐electrode data from three separate experiments, we identify and characterize a cortical operating state (the “probability polling” or “p‐polling” state), common across mouse and monkey with different behaviors. If the interaction among neurons is weak, the p‐polling state provides a quantitative understanding of how the high dimensional probability distribution of firing patterns can be obtained by the low‐order maximum entropy formulation, effectively utilizing a low dimensional stimulus‐coding structure. These results show evidence for generality of the p‐polling state and in certain situations its advantage of providing a mathematical validation for the low‐order maximum entropy principle as a coding strategy.