MSc Project

Abstract: In hippocampal CA1 and CA3 regions, various properties of neuronal activity follow skewed, lognormal-like distributions, including average firing rates, rate and magnitude of spike bursts, magnitude of population synchrony, and correlations between pre- and postsynaptic spikes. In recent studies, the lognormal features of hippocampal activities were well replicated by a multi-timescale adaptive threshold (MAT) neuron network of lognormally distributed excitatory-to-excitatory synaptic weights, though it remains unknown whether and how other neuronal and network properties can be replicated in this model. Here we implement two additional studies of the same network: first, we further analyze its burstiness properties by identifying and clustering neurons with exceptionally bursty features, once again demonstrating the importance of the lognormal synaptic weight distribution. Second, we characterize dynamical patterns of activity termed neuronal avalanches in in vivo CA3 recordings of behaving rats and in the model network, revealing the similarities and differences between experimental and model avalanche size distributions across the sleep-wake cycle. These results show the comparison between the MAT neuron network and hippocampal readings in a different approach than shown before, providing more insight into the mechanisms behind activity in hippocampal subregions.

Dissertation available online at Teses USP

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