BSPlib: The BSP programming library
Parallel Computing
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Simulation of Spiking Neural Networks on Different Hardware Platforms
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Event-driven simulation of spiking neurons with stochastic dynamics
Neural Computation
A SIMD/Dataflow Architecture for a Neurocomputer for Spike-Processing Neural Networks (NESPINN)
MICRONEURO '96 Proceedings of the 5th International Conference on Microelectronics for Neural Networks and Fuzzy Systems
NeuroPipe-Chip: A digital neuro-processor for spiking neural networks
IEEE Transactions on Neural Networks
Synaptic plasticity in spiking neural networks (SP2INN): a system approach
IEEE Transactions on Neural Networks
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A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components. We focus on conductance-based models for neurons that emulate the temporal dynamics of the synaptic integration process. We have designed an efficient computing architecture using reconfigurable hardware in which the different stages of the neuron model are processed in parallel (using a customized pipeline structure). Further improvements occur by computing multiple neurons in parallel using multiple processing units. The computing platform is described and its scalability and performance evaluated. The goal is to investigate biologically realistic models for the control of robots operating within closed perception-action loops.