Statistical modelling of neuronal population activity: from data analysis to network function
Sorbaro Sindaci, Martino
The term statistical modelling refers to a number of abstract models designed to reproduce and understand the statistical properties of the activity of neuronal networks at the population level. Large-scale recordings by multielectrode arrays (MEAs) have now made possible to scale their use to larger groups of neurons. The initial step in this work focused on improving the data analysis pipeline that leads from the experimental protocol used in dense MEA recordings to a clean dataset of sorted spike times, to be used in model training. In collaboration with experimentalists, I contributed to developing a fast and scalable algorithm for spike sorting, which is based on action potential shapes and on the estimated location for the spike. Using the resulting datasets, I investigated the use of restricted Boltzmann machines in the analysis of neural data, finding that they can be used as a tool in the detection of neural ensembles or low-dimensional activity subspaces. I further studied the physical properties of RBMs fitted to neural activity, finding they exhibit signatures of criticality, as observed before in similar models. I discussed possible connections between this phenomenon and the \dynamical" criticality often observed in neuronal networks that exhibit emergent behaviour. Finally, I applied what I found about the structure of the parameter space in statistical models to the discovery of a learning rule that helps long-term storage of previously learned memories in Hopfield networks during sequential learning tasks. Overall, this work aimed to contribute to the computational tools used for analysing and modelling large neuronal populations, on different levels: starting from raw experimental recordings and gradually proceeding towards theoretical aspects.