Abstract
Neurons which fire in a fixed temporal pattern (i.e., “cell assemblies”) are hypothesized to be a fundamental unit of neural information processing. Several methods are available for the detection of cell assemblies without a time structure. However, the systematic detection of cell assemblies with time structure has been challenging, especially in large datasets, due to the lack of efficient methods for handling the time structure. Here, we show a method to detect a variety of cell-assembly activity patterns, recurring in noisy neural population activities at multiple timescales. The key innovation is the use of a computer science method to comparing strings (“edit similarity”), to group spikes into assemblies. We validated the method using artificial data and experimental data, which were previously recorded from the hippocampus of male Long-Evans rats and the prefrontal cortex of male Brown Norway/Fisher hybrid rats. From the hippocampus, we could simultaneously extract place-cell sequences occurring on different timescales during navigation and awake replay. From the prefrontal cortex, we could discover multiple spike sequences of neurons encoding different segments of a goal-directed task. Unlike conventional event-driven statistical approaches, our method detects cell assemblies without creating event-locked averages. Thus, the method offers a novel analytical tool for deciphering the neural code during arbitrary behavioral and mental processes.
Highlights
Uncovering neural codes is of fundamental importance in neuroscience
Edit similarity measures matching between two sequences with flexible temporal alignment, which is an essential feature for detecting noisy spatiotemporal patterns embedded in neural activity
We developed a method for robust sequence detection based on the edit similarity score known in computer science (Levenshtein, 1966)
Summary
Uncovering neural codes is of fundamental importance in neuroscience. Several experimental results suggest that synchronous or sequential firing of cortical neurons play active roles in primates (Abeles et al, 1993; Hatsopoulos et al, 1998; Steinmetz et al, 2000). Place cells exhibit precisely timed, repeating firing sequences representing the rat’s trajectory, subsections of which repeat during each theta cycle (O’Keefe, 1976; Mehta et al, 2002; Villette et al, 2015). These sequences are Unsupervised Detection of Cell-Assembly Sequences et al, 2013; Torre et al, 2016; Quaglio et al, 2017; Russo et al, 2017), and such data analysis remains a challenge. We extend the edit similarity score to a form applicable to neural activity data and develop a clustering method for blind cell-assembly detection. Robustness to noise and computational efficiency of our method will help the exhaustive search of repeated spatiotemporal patterns in large-scale neural data, which may lead to the elucidation of hidden neural codes
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