This report introduces ongoing research concerning hardware implementations of spiking neural network on embedded systems. Goal is to implement a spiking neural network in reconfigurable network, more specifically embedded systems.Spiky neural networks are widely used in neural modeling, due to their biological relevance and high computational power.
This report gives investigation of the usage of spiking dynamics in embedded artificial neural networks, which serve as a control mechanism for evolved autonomous agents performing a delayed-response task. Here an evolved spiky network is compared with evolved McCulloch-Pitts networks, while confronting new questions about the nature of spikiness and its contribution to the neurocontroller's processing. On the behavioral level, it shows that in a memory-dependent task, network solutions that incorporate spiking dynamics can be less complex and easier to evolve than neurocontrollers involving McCulloch-Pitts neurons.

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