Learning in a Distributed Software Architecture for Large-Scale Neural Modeling

Progress on large-scale simulation of neural models depends in part on the availability of suitable hardware and software architectures. Heterogeneous hardware computing platforms are becoming increasingly popular as substrates for general-purpose simulation. On the other hand, recent work highlights that certain constraints on neural models must be imposed on neural and synaptic dynamics in order to take advantage of such systems. In this paper we focus on constraints related to learning in a simple visual system and those imposed by a new neural simulator for heterogeneous hardware systems, CogExMachina (Cog).

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