Topic: higher order dependencies

Studying and understanding the code of large neural populations hinges on learning accurate models of population activity. While statistical models based on pairwise interactions are highly accurate in describing the code of tens of neurons, accurate models of >100 neurons require higher order dependencies. Statistical models based on sparse nonlinear Random Projections (RP) of the population proved to be highly accurate and efficient for 100-200 neurons. The scaling of these models to larger populations is challenging yet critical for mapping the semantic organization of population codes, the