RetinotopicNET: An efficient simulator for retinotopic visual architectures

Raul C. Mureşan

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Abstract

RetinotopicNET is an efficient simulator for neural networks with retinotopic-like receptive fields. The system has two main characteristics: it is event-driven and it takes advantage of the retinotopic arrangement in the receptive fields. The dynamics of the simulator are driven by the spiking events of the simple integrate-and-fire neurons. By using an implicit synaptic rule to represent the synapses, RetinotopicNET achieves a great reduction of memory requirement for simulation. We show that under such conditions the system is fit for the simulation of very large networks of integrate-and-fire neurons. Furthermore we test RetinotopicNET in the simulation of a complex neural architecture for the ventral visual pathway. We prove that the system is linearly scalable with respect to the number of neurons in the simulation.

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