Topic: sparse nonlinear Random Projections

Studying and understanding the code of large neural populations hinges on learning accurate models of population activity. Statistical models that are based on sparse nonlinear Random Projections (RP) of the population proved to be highly accurate, efficient, and scalable. Interestingly, this class of models has a straightforward biologically-plausible implementation as a shallow neural circuit. Here we extend the RP models, and learn them by optimizing or ``reshaping" the random projections themselves. The reshaping of projections is akin to changing synaptic connections in the neural circuit