Abstract
When implementing spiking neural networks into agents acting in virtual environments, there often exist an underlying problem of incongruity between the level of detail in the (biological) neural model and the agent’s environment, resulting in excessive network iterations required for functional behavior. This thesis aims to explore a new spiking
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