ePoster · Neuroscience
Shaping Low-Rank Recurrent Neural Networks with Biological Learning Rules
Pablo Crespo, Dimitra Maoutsa, Matthew Getz, Julijana Gjorgjieva · Bernstein Conference 2024
Extensive experimental evidence shows that task-relevant neural population dynamics often evolve along trajectories constrained to low-dimensional subspaces [1, 2]. However, how these low-dimensional task representations emerge through learning, and how the neural activity interacts with synaptic plasticity is still an unresolved question. The recent theoretical framework of low-rank recurrent neural networks (lr-RNNs) provides a direct link between connectivity and dynamics by relating structured patterns embedded in the network connectivity to the resulting low-dimensional dynamics [3]. We