Modelling Neuronal Behaviour with Time Series Regression: Recurrent Neural Networks on C. Elegans Data

TL;DR

This paper demonstrates that recurrent neural networks, especially GRUs with small hidden layers, can effectively model and simulate the neuronal responses of C. Elegans, providing a data-driven approach to understanding simple nervous systems.

Contribution

The study compares different RNN architectures for modeling C. Elegans neuronal behavior, highlighting the effectiveness of small-GRU models in capturing complex neural responses.

Findings

GRU models with 4 hidden units accurately reproduce neuronal responses

Recurrent neural networks outperform classical models in capturing nonlinearities

Small neural network architectures are sufficient for effective modeling

Abstract

Given the inner complexity of the human nervous system, insight into the dynamics of brain activity can be gained from understanding smaller and simpler organisms, such as the nematode C. Elegans. The behavioural and structural biology of these organisms is well-known, making them prime candidates for benchmarking modelling and simulation techniques. In these complex neuronal collections, classical, white-box modelling techniques based on intrinsic structural or behavioural information are either unable to capture the profound nonlinearities of the neuronal response to different stimuli or generate extremely complex models, which are computationally intractable. In this paper we show how the nervous system of C. Elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, we target the use of state of the art recurrent neural networks architectures such as LSTMs and GRUs and compare these architectures in terms of their properties and their accuracy as well as the complexity of the resulting models. We show that GRU models with a hidden layer size of 4 units are able to accurately reproduce with high accuracy the system's response to very different stimuli.