Mimicking Collective Firing Patterns of Hundreds of Connected Neurons using a Single-Neuron Experiment

TL;DR

This paper introduces a novel experimental technique that uses a single neuron to mimic the collective firing patterns of hundreds of interconnected neurons, enabling the study of large neural networks despite technological limitations.

Contribution

A versatile method is presented that allows the simulation of recurrent neural network activity through single-neuron experiments, overcoming measurement constraints.

Findings

Demonstrated emergence of cooperative synchronous oscillations

Observed coexistence of Gamma and Delta oscillations

Enabled study of large-scale neural phenomena with a single neuron

Abstract

The experimental study of neural networks requires simultaneous measurements of a massive number of neurons, while monitoring properties of the connectivity, synaptic strengths and delays. Current technological barriers make such a mission unachievable. In addition, as a result of the enormous number of required measurements, the estimated network parameters would differ from the original ones. Here we present a versatile experimental technique, which enables the study of recurrent neural networks activity while being capable of dictating the network connectivity and synaptic strengths. This method is based on the observation that the response of neurons depends solely on their recent stimulations, a short-term memory. It allows a long-term scheme of stimulation and recording of a single neuron, to mimic simultaneous activity measurements of neurons in a recurrent network. Utilization of this technique demonstrates the spontaneous emergence of cooperative synchronous oscillations, in particular the coexistence of fast Gamma and slow Delta oscillations, and opens the horizon for the experimental study of other cooperative phenomena within large-scale neural networks.