Seminar · Computational Neuroscience
The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks
Brian DePasquale · Princeton
Wed, May 3, 2023 · 15:00 UTC
Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, computations are performed not by continuously valued factors but by interactions among neurons that spike discretely and variably. Models provide a means of bridging these levels of description. We developed a general method for training model networks of spiking neurons by leveraging factors extracted from either data or firing-rate-based networks. In