Spike Sorting of Human Single Neuron Signal Recordings

Abstract

Neuroscience is a field in constant evolution, searching for better ways to understand human brains and behaviour. Challenged by the complexity and difficult accessibility of the brain, neuroscience must constantly be at the edge of scientific and technical developments, learning new ways to obtain and understand neural data. The approach of this project departs from one such novel findings, human single neuron signal recordings, an emerging technology with a great many applications in research fields but currently limited by the lack of extensive data and previous research. Invasive cell-level recording techniques have been extensively used since its inception in animal brain studies, where they have contributed to wide-ranging research and count with consistent and standardized procedures for its application and analysis. On the other hand, its potential use in theoretical human research is still underdeveloped and does not count with a comprehensive and systematic data analysis framework. However, human single neuron data has the potential to path the way for state-of-the-art research focusing on the behaviour of deep neural processes, such as learning and cognition. The main objective of the project is to adapt pre-existing single-neuron data-analysis software for animal studies to human recordings, creating a continuous dataflow from the obtention of the data to its ultimate analysis.