ELSC Seminar Series

We present a new class of highly accurate, scalable, and efficient models of the activity of large neural populations. Interestingly, these models have a biologically-plausible implementation by neural circuits that rely on random, sparse, and non-linear projections. We further show that homeostatic synaptic scaling makes the learning of such models for very large neural populations even more efficient and accurate. Finally, we will discuss how such models can allow the brain to perform Bayesian decoding and the learning of metrics on the space of neural codes and of external stimuli.