Abstract
ABSTRACT
Cell assemblies–groups of neurons that fire synchronously–are central to neuronal computation. Technological advances now permit the simultaneous recording of large neuronal populations; however, many existing cell assembly detection algorithms rely on simplified measures such as pairwise correlations, failing to capture higher-order synchrony and thereby missing a rich repertoire of assemblies present in the data. To address these limitations, we developed Identification of Synchronously Active Cells (ISAC), which performs a rigorous, iterative search across all recorded units, leveraging the full dimensionality of the dataset to detect groups that fire together above chance. Using simulated recordings with ground-truth assemblies of increasing complexity, we demonstrate that ISAC considerably outperforms currently used algorithms in identifying synchronously active cells. Finally, we show that applying ISAC to experimental recordings uncovers novel features of assembly formation and dynamics. The power and sensitivity of ISAC can thus enable insights that remain inaccessible with existing methods.
SIGNIFICANCE STATEMENT
Neurons rarely act in isolation. Instead, groups of neurons can become active together, forming “cell assemblies” thought to represent and process information in the brain. Identifying which neurons belong to these assemblies is therefore important for understanding how neuronal populations encode information. However, commonly used methods can incorrectly merge distinct groups of neurons or identify neurons that do not consistently participate in synchronous activity. Here, we introduce Identification of Synchronously Active Cells (ISAC), a method designed to systematically identify groups of neurons that fire together more often than expected by chance. ISAC directly examines the timing of neuronal activity and applies statistical criteria to determine both whether individual neurons belong to a group and whether the group behaves as a coherent assembly. Across extensive simulations, including conditions reproducing the complex activity patterns observed in experimental recordings, ISAC identified cell assemblies more accurately than commonly used approaches. Applications to real recordings further illustrate how ISAC can reveal the formation, composition, spatial coding, and reactivation of neuronal assemblies. ISAC provides a framework for studying how coordinated groups of neurons organize and process information across brain states and behaviors.