student profile: Mr Guozhang Chen


Thesis work

Thesis title: Information processing principles of self-organizing, spatially extended neural circuits

Supervisors: Peter ROBINSON , Pulin GONG

Thesis abstract:

It has been widely observed that cortical populations produce complex spatiotemporal activity patterns spontaneously without sensory input. The functional roles of such spontaneous activity patterns in cortical computation, however, remain unclear. In this study, we first demonstrate that spatiotemporal patterns with criticality emerging from a spatially-extended neural circuit model can capture salient features of spontaneous cortical dynamics, and that these patterns can be modulated by natural stimuli such as faces. We then illustrate that the modulating process provides a mechanistic explanation for a range of experimental observations, including the similarity between spontaneous and evoked patterns, and the variability of response latency and firing rates. In addition, we show that the modulating process can be formulated as a process of Bayesian inference, in which the dynamical spontaneous patterns sample target probability distributions to speed up network responses. Our results thus suggest that spontaneous activity is essential for understanding the mechanism of active cortical processing.

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