Neural mechanisms of information processing and transmission

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