Code-specific policy gradient rules for spiking neurons

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This thesis explores stochastic optimal control through three interconnected contributions: a theoretical framework, and the development of two regression methods, POLK and POLRF, tailored to high-dimensional regression challenges in this domain. In the f ...

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In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

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In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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Code-specific policy gradient rules for spiking neurons

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