Spikenet: an event-driven simulation package for modelling large networks of spiking neurons Export

@article{citeulike:3988009, abstract = {Abstract: Many biological neural network models face the problem of scalability because of the limited computational power of today's computers. Thus, it is difficult to assess the efficiency of these models to solve complex problems such as image processing. Here, we describe how this problem can be tackled using event-driven computation. Only the neurons that emit a discharge are processed and, as long as the average spike discharge rate is low, millions of neurons and billions of connections can be modeled. We describe the underlying computation and implementation of such a mechanism in SpikeNET, our neural network simulation package. The type of model one can build is not only biologically compliant, it is also computationally efficient as 400 000 synaptic weights can be propagated per second on a standard desktop computer. In addition, for large networks, we can set very small time steps (less than 0.01 ms) without significantly increasing the computation time. As an example, this method is applied to solve complex cognitive tasks such as face recognition in natural images.}, author = {Delorme, Arnaud and Thorpe, Simon J.}, citeulike-article-id = {3988009}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.95.29}, journal = {Neural Networks}, keywords = {artificial\_intelligence}, pages = {613--627}, posted-at = {2009-01-31 11:40:21}, priority = {2}, title = {Spikenet: an event-driven simulation package for modelling large networks of spiking neurons}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.95.29}, volume = {14}, year = {2003} }

TY - JOUR ID - citeulike:3988009 L3 - citeulike-article-id:3988009 N2 - Abstract: Many biological neural network models face the problem of scalability because of the limited computational power of today’s computers. Thus, it is difficult to assess the efficiency of these models to solve complex problems such as image processing. Here, we describe how this problem can be tackled using event-driven computation. Only the neurons that emit a discharge are processed and, as long as the average spike discharge rate is low, millions of neurons and billions of connections can be modeled. We describe the underlying computation and implementation of such a mechanism in SpikeNET, our neural network simulation package. The type of model one can build is not only biologically compliant, it is also computationally efficient as 400 000 synaptic weights can be propagated per second on a standard desktop computer. In addition, for large networks, we can set very small time steps (less than 0.01 ms) without significantly increasing the computation time. As an example, this method is applied to solve complex cognitive tasks such as face recognition in natural images. JF - Neural Networks EP - 627 TI - Spikenet: an event-driven simulation package for modelling large networks of spiking neurons VL - 14 SP - 613 KW - artificial_intelligence AU - Delorme, Arnaud AU - Thorpe, Simon PY - 2003/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.95.29 ER -