Neural mechanisms of information processing and transmission

• Main Author: Leugering, Johannes
• Other Authors: Prof. Dr. Gordon Pipa
• Format: Doctoral Thesis
• Language: English
• Published: 2021
• Subjects: Theoretical Neuroscience; Neural Information Processing; Artificial Intelligence; 54.72 - Künstliche Intelligenz; 30.99 - Naturwissenschaften allgemein: Sonstiges; H.1.1 - Systems and Information Theory; G.3 - PROBABILITY AND STATISTICS; ddc:500
• Online Access: https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-202111055552

This (cumulative) dissertation is concerned with mechanisms and models of information processing and transmission by individual neurons and small neural assemblies. In this document, I first provide historical context for these ideas and highlight similarities and differences to related concepts from machine learning and neuromorphic engineering. With this background, I then discuss the four main themes of my work, namely dendritic filtering and delays, homeostatic plasticity and adaptation, rate-coding with spiking neurons, and spike-timing based alternatives to rate-coding. The content of this discussion is in large part derived from several of my own publications included in Appendix C, but it has been extended and revised to provide a more accessible and broad explanation of the main ideas, as well as to show their inherent connections. I conclude that fundamental differences remain between our understanding of information processing and transmission in machine learning on the one hand and theoretical neuroscience on the other, which should provide a strong incentive for further interdisciplinary work on the domain boundaries between neuroscience, machine learning and neuromorphic engineering.