Master's Thesis
Modeling C. Elegans Nervous System’s Behavior using Machine Learning Techniques
2021
—Key information
Authors:
Supervisors:
Published in
11/30/2021
Abstract
Given its inner complexity and potential for human advancement resulting from a deeper understanding of its structure and functioning, the study of the human brain and nervous system is one of the greatest challenges in computational neuroscience. In order to understand its dynamics, insight may be gained from deeper knowledge of simpler and smaller organisms like the Caenorhabditis elegans, a common benchmark in computational neuroscience. This organism has a small nervous system with a connectome composed of less than 400 neurons and 15000 connections, which was already studied and reconstructed in multiple ways. One of these reconstructions is available in the NEURON simulator used in this work to generate data for common locomotion and movement behaviors of this worm. In this work five different artificial neural network architectures are implemented and compared in their ability to model the behavior of this small nervous system by predicting the output voltage on different neurons using only knowledge from the electric currents given as inputs to the system. Here it is shown that small models based on the Long Short-Term Memory Unit and the Gated Recurrent Unit architectures are able to replicate with high fidelity the mentioned behaviors of the system. These architectures are also able to produce a single model that can replicate the behavior of the system for both studied behaviors. This indicates that these architectures are appropriate for creating small black box models of nervous systems for different behaviors and should be tested for more complex nervous systems.
Publication details
Authors in the community:
Gonçalo Leote Cardoso Mestre
ist187005
Supervisors of this institution:
Ruxandra Georgeta Barbulescu
ist428600
Fields of Science and Technology (FOS)
electrical-engineering-electronic-engineering-information-engineering - Electrical engineering, electronic engineering, information engineering
Publication language (ISO code)
eng - English
Rights type:
Embargo lifted
Date available:
10/04/2022
Institution name
Instituto Superior Técnico