Topic: low-dimensional subspaces

Over the past decade, neural representations have been studied from the lens of low-dimensional subspaces defined by the co-activation of neurons. However, this view has overlooked other forms of covarying structure in neural activity, including i) condition-specific high-dimensional neural sequences, and ii) representations that change over time due to learning or drift. In this talk, I will present a new framework that extends the classic view towards additional types of covariability that are not constrained to a fixed, low-dimensional subspace. In addition, I will present sliceTCA, a new t

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