Impact of exponential long range and Gaussian short range lateral connectivity on the distributed simulation of neural networks including up to 30 billion synapses
@article{Pastorelli2015ImpactOE, title={Impact of exponential long range and Gaussian short range lateral connectivity on the distributed simulation of neural networks including up to 30 billion synapses}, author={Elena Pastorelli and Pier Stanislao Paolucci and Roberto Ammendola and Andrea Biagioni and Ottorino Frezza and Francesca Lo Cicero and Alessandro Lonardo and Michele Martinelli and Francesco Simula and Piero Vicini}, journal={ArXiv}, year={2015}, volume={abs/1512.05264} }
Recent experimental neuroscience studies are pointing out the role of long-range intra-areal connectivity that can be modeled by a distance dependent exponential decay of the synaptic probability distribution. This short report provides a preliminary measure of the impact of exponentially decaying lateral connectivity compared to that of shorter-range Gaussian decays on the scaling behaviour and memory occupation of a distributed spiking neural network simulator (DPSNN). Two-dimensional grids…
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