A computationally-assisted approach to extracellular neural electrophysiology with multi-electrode arrays

With the advent of high-density multi-electrode arrays we are now able to measure the activity of hundreds of neurons simultaneously, even at the sub-cellular level. However, next-generation devices introduce novel grand challenges and the need for appropriate tools to handle the rich information that can be recorded. The work presented in this thesis has therefore focused on developing and benchmarking new tools and methods for using such devices at their full potential. Main research findings Neurons use tiny electrical signals to communicate with each other. By inserting electrodes in the brain, we can read from neurons (record electrical activity) and even write to them (induce activity by electrical stimulation). In recent years there has been a huge development in neural devices: neuroscientists can now use probes with several hundreds of very closely-spaced electrodes called Multi-Electrode Arrays. The goal of my PhD was to develop methods and tools to improve the way we read from and write to the brain tissue using these newly developed probes. In order to achieve my goal, I followed a computationally-assisted approach. The idea is to use very detailed models of single neurons (mathematical description of how the neuron behaves) to run simulations, that can be used to guide the development of new analysis methods. I used this approach to tackle several open problems of extracellular electrophysiology, including spike sorting, neuron localization, cell-type classification, and selective electrical microstimulation of neurons. The outcome of this work is a collection of analytical and computational tools that will contribute to shed light on how this extremely fascinating and complicated organ that sits on our shoulders works