Spike timing neural manifolds for learning, memory and computation in the brain

Authors: Simon R. Schultz

Presentation type: Talk at SNUFA 2026 online workshop (4-5 Nov 2026)

Abstract

Experience is reflected by the activity of very large numbers of neurons firing in patterns across the brain; these patterns tend to form relatively low dimensional topological subspaces within this neural activity space, which we refer to as “neural manifolds”. While it remains to be established if such manifolds are themselves important units of computation in the brain, they are at the very least an extremely valuable window into the dynamical operation of the underlying complex brain circuits. To date, however, neural manifold learning approaches have focused almost entirely on firing rate descriptions of neural dynamics, despite a mounting body of evidence that precise spike timing can be important for perception, memory and motor coordination. In this presentation I will introduce a theoretical formulation which allows low-dimensional spike timing neural manifolds to be extracted from spatiotemporal patterns of inter-spike interval sequences across populations of neurons. I will demonstrate that this produces useful descriptions of neural population dynamics during behaviour, by application to both simulated data and a number of datasets recorded from awake behaving animals. I posit that spike-timing neural manifolds may provide a useful framework for the analysis of important problems in the neuroscience of learning, memory, perception, thought and action.

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