Scalable and accurate method for neuronal ensemble detection in spiking neural networks.

Jonathan David Touboul,Rubén Herzog,Rodrigo Cofré,Arturo Morales,Joaquín Araya,Soraya Mora,Adrian G Palacios,María-José Escobar

doi:10.1371/journal.pone.0251647

Abstract

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.

Highlights

Donald Hebb predicted more than 70 years ago that ensembles would naturally arise from synaptic learning rules, where neurons that fire together would wire together [1]
Principal Component Analysis (PCA) extracts a set of orthogonal directions capturing the most significant variability observed on the population vectors
We computed the Euclidean distance between all the vectors projected on the first six principal components

Summary

Introduction

Donald Hebb predicted more than 70 years ago that ensembles would naturally arise from synaptic learning rules, where neurons that fire together would wire together [1]. Despite the long history of this idea, only recently the simultaneous recordings and computational analysis from hundreds of cells have turned out to be possible [2]. Recent advances in recording technology of neuronal activity combined with sophisticated methods of data analysis have revealed significant synchronous activity between neurons at several spatial and temporal scales [3,4,5]. Theses groups of neurons that have the tendency to fire together known as neuronal ensembles ( called cell assemblies) are hypothesized to be a fundamental unit of neural processes and form the basis of coherent collective brain dynamics [1, 6,7,8].

Methods

Results

Conclusion