Speaker
Description
Identifying cell assemblies from multi-unit recordings is a fundamental challenge in systems neuroscience. Most existing methods focus on detecting co-excited neurons, but neural circuits also involve inhibitory interactions, where the activation of an assembly can systematically suppress the firing of some of its members. Here we present a cell assembly detection algorithm that identifies assemblies of both excited and inhibited neurons from continuous neural signals, identify assemblies across multiple timescales, and correctly separates assemblies with partially overlapping membership.
The algorithm band-pass filters each unit's signal at a logarithmically spaced set of timescales, then tests pairwise correlations via a Fisher Z-statistic with an effective sample size correction for temporal autocorrelation. Significant pairs are grown into higher-order assemblies through an agglomerative procedure requiring each candidate unit to be significantly correlated with all current assembly members, enabling separation of assemblies with shared neurons that would otherwise be merged into a single spurious detection.
We validate the algorithm on three synthetic datasets of increasing complexity, demonstrating reliable detection of synchronous and sequential assemblies across timescales from 1ms to 1s, correct excited/inhibithed classification, and successful separation of overlapping assemblies. We further apply the algorithm to in-vivo recordings from mouse olfactory tubercle and anterior piriform cortex, recovering a cross-regional assembly consistent with known inhibitory projection between the two areas.
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