Role of connectivity anisotropies in the dynamics of cultured neuronal networks

TL;DR

This study presents a numerical model that replicates anisotropic connectivity in engineered neuronal networks, helping predict their collective behavior and improve understanding of structure-function relationships in vitro.

Contribution

The paper introduces a biologically-realistic, anisotropy-incorporating model that predicts neuronal network dynamics and enhances connectivity reconstruction from activity data.

Findings

Connectivity anisotropies improve structural connectivity inference.

Model captures diverse activity patterns in engineered neuronal cultures.

Network behavior depends on anisotropy strength and noise levels.

Abstract

Laboratory-grown, engineered living neuronal networks in vitro have emerged in the last years as an experimental technique to understand the collective behavior of neuronal assemblies in relation to their underlying connectivity. An inherent obstacle in the design of such engineered systems is the difficulty to predict the dynamic repertoire of the emerging network and its dependence on experimental variables. To fill this gap, and inspired on recent experimental studies, here we present a numerical model that aims at, first, replicating the anisotropies in connectivity imprinted through engineering, to next realize the collective behavior of the neuronal network and make predictions. We use experimentally measured, biologically-realistic data combined with the Izhikevich model to quantify the dynamics of the neuronal network in relation to tunable structural and dynamical parameters. These parameters include the synaptic noise, strength of the imprinted anisotropies, and average axon lengths. The latter are involved in the simulation of the development of neurons in vitro. We show that the model captures the behavior of engineered neuronal cultures, in which a rich repertoire of activity patterns emerge but whose details are strongly dependent on connectivity details and noise. Results also show that the presence of connectivity anisotropies substantially improves the capacity of reconstructing structural connectivity from activity data, an aspect that is important in the quest for understanding the structure-to-function relationship in neuronal networks. Our work provides the in silico basis to assist experimentalists in the design of laboratory in vitro networks and anticipate their outcome, an aspect that is particularly important in the effort to conceive reliable brain-on-a-chip circuits and explore key aspects such as input-output relationships or information coding.