Attractor dynamics underlie sequence working memory in data-constrained recurrent neural networks
Abstract
Sequences of stimuli can be maintained in working memory by encoding each item, together with its position, as a distinct pattern of neural population activity. Competing hypotheses about the underlying dynamics have been proposed: patterns of activity might be supported either by stable attractor states that persist over time, or by transient sequential dynamics. To investigate the dynamics underlying sequence memory, we developed a framework for fitting low-rank recurrent neural networks (RNNs) to multi-session recordings of spiking activity, and applied it to 2,488 units recorded across 30 sessions in two macaques. Our data-constrained RNNs generated population spiking activity which closely resembled neural data and recovered single-unit tuning properties across sessions. Consistent with prior work, fitted networks represented items presented at different sequence positions in separate low-dimensional subspaces in the neural activity, alongside a dominant temporal component. The RNNs enabled direct investigation of the underlying dynamics: By isolating the temporal and stimulus subspaces, we found a manifold of fixed points emerged during the memory retention period. Targeted perturbation analyses confirmed that activity within this manifold is consistent with attractor-like dynamics, and predict that brief optogenetic perturbations delivered along this attractive manifold can selectively induce persistent shifts in neural activity and decoded behavior. Together, our…
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