Reconstruction scheme for excitatory and inhibitory dynamics with quenched disorder: application to zebrafish imaging

TL;DR

This paper presents an inverse method to reconstruct neural network connectivity and neuron properties from brain activity data, validated on synthetic and zebrafish imaging data, revealing a power-law distribution of excitatory connections.

Contribution

It introduces a novel inverse reconstruction scheme for excitatory and inhibitory neural dynamics using a heterogenous mean-field approach, applied to real zebrafish brain data.

Findings

Power law distribution of excitatory in-degree connections.

Successful reconstruction of neuron degree distributions from zebrafish imaging.

Validation of the method on synthetic data confirms its accuracy.

Abstract

An inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.