Detecting precise spiking motifs in neurobiological data
At any given instant, hundreds of billions of cells in our brains are lighting up in a complicated yet highly coordinated manner to give rise to our thoughts, percepts, and movements. A single neuron may be connected to thousands of other cells, sending out and receiving information through electrical impulses called spikes. From an engineering perspective, these spikes form a signal that may be viewed as a series of ones and zeros rapidly unfolding in time. Altogether, these signals reflect the ongoing computations taking place inside the nervous system, and as such, constitute a window into the brain’s inner workings. Recent advances in recording techniques have allowed experimenters to collect data from hundreds to thousands of neurons simultaneously while animals perform simple tasks. Dealing with such high-dimensional data poses important technical challenges that require elaborate methods for data mining and analysis. In this project, students will deal with datasets of increasing complexity and develop a set of analyses to extract meaningful information from the data.
The folowing datasets will be shared by the teaching staff:
publicly available recordings from a reaching task from Hatsopoulos, Joshi, and O’Leary (2004) doi:10.1152/jn.01245.2003
publicly available recordings from the dorsomedial frontal cortex of NHPs performing a time-interval reproduction task Meirhaeghe, Sohn, and Jazayeri (2021) doi:10.1016/j.neuron.2021.08.025 - see (https://github.com/jazlab/Meirhaeghe2021).
Data visualisation, neural decoding, principal component analysis, kinematic and geometric analyses of neural trajectories in high-dimensional space, hypothesis-testing, null distributions and statistics