Revealing cell assemblies at multiple levels of granularity

TL;DR

This paper introduces a versatile, multi-scale framework for detecting neuronal cell assemblies from spiking data, using a biophysically-inspired connectivity measure and community detection, applicable to synthetic and real neural recordings.

Contribution

The authors present a novel integrated method combining directed functional connectivity and Markov Stability community detection to identify neural assemblies at multiple scales without prior assumptions.

Findings

Successfully identifies hierarchical structures in synthetic data

Detects known functional types in retinal ganglion cells

Accurately finds place cells in hippocampal recordings

Abstract

Background: Current neuronal monitoring techniques, such as calcium imaging and multi-electrode arrays, enable recordings of spiking activity from hundreds of neurons simultaneously. Of primary importance in systems neuroscience is the identification of cell assemblies: groups of neurons that cooperate in some form within the recorded population. New Method: We introduce a simple, integrated framework for the detection of cell-assemblies from spiking data without a priori assumptions about the size or number of groups present. We define a biophysically-inspired measure to extract a directed functional connectivity matrix between both excitatory and inhibitory neurons based on their spiking history. The resulting network representation is analyzed using the Markov Stability framework, a graph theoretical method for community detection across scales, to reveal groups of neurons that are significantly related in the recorded time-series at different levels of granularity. Results and comparison with existing methods: Using synthetic spike-trains, including simulated data from leaky-integrate-and-fire networks, our method is able to identify important patterns in the data such as hierarchical structure that are missed by other standard methods. We further apply the method to experimental data from retinal ganglion cells of mouse and salamander, in which we identify cell-groups that correspond to known functional types, and to hippocampal recordings from rats exploring a linear track, where we detect place cells with high fidelity. Conclusions: We present a versatile method to detect neural assemblies in spiking data applicable across a spectrum of relevant scales that contributes to understanding spatio-temporal information gathered from systems neuroscience experiments.