Real-time computing platform for spiking neurons (RT-spike). Ros, E.; Ortigosa, E. M.; Agis, R.; Carrillo, R.; and Arnold, M. IEEE Trans Neural Netw, 17:1050--1063, Jul, 2006.
abstract   bibtex   
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 to manage the tradeoff between flexibility and computational power; the neuron model is implemented in hardware and the network model and the learning are implemented in software. The incremental transition of the software components into hardware is supported. We focus on a spike response model (SRM) for a neuron where the synapses are modeled as input-driven conductances. The temporal dynamics of the synaptic integration process are modeled with a synaptic time constant that results in a gradual injection of charge. This type of model is computationally expensive and is not easily amenable to existing software-based event-driven approaches. As an alternative we have designed an efficient time-based computing architecture in hardware, where the different stages of the neuron model are processed in parallel. Further improvements occur by computing multiple neurons in parallel using multiple processing units. This design is tested using reconfigurable hardware and its scalability and performance evaluated. Our overall goal is to investigate biologically realistic models for the real-time control of robots operating within closed action-perception loops, and so we evaluate the performance of the system on simulating a model of the cerebellum where the emulation of the temporal dynamics of the synaptic integration process is important.
@article{ Ros_etal06b,
  author = {Ros, E. and Ortigosa, E. M. and Agis, R. and Carrillo, R. and Arnold,
	M. },
  title = {{{R}eal-time computing platform for spiking neurons ({R}{T}-spike)}},
  journal = {IEEE Trans Neural Netw},
  year = {2006},
  volume = {17},
  pages = {1050--1063},
  month = {Jul},
  abstract = {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 to manage the tradeoff
	between flexibility and computational power; the neuron model is
	implemented in hardware and the network model and the learning are
	implemented in software. The incremental transition of the software
	components into hardware is supported. We focus on a spike response
	model (SRM) for a neuron where the synapses are modeled as input-driven
	conductances. The temporal dynamics of the synaptic integration process
	are modeled with a synaptic time constant that results in a gradual
	injection of charge. This type of model is computationally expensive
	and is not easily amenable to existing software-based event-driven
	approaches. As an alternative we have designed an efficient time-based
	computing architecture in hardware, where the different stages of
	the neuron model are processed in parallel. Further improvements
	occur by computing multiple neurons in parallel using multiple processing
	units. This design is tested using reconfigurable hardware and its
	scalability and performance evaluated. Our overall goal is to investigate
	biologically realistic models for the real-time control of robots
	operating within closed action-perception loops, and so we evaluate
	the performance of the system on simulating a model of the cerebellum
	where the emulation of the temporal dynamics of the synaptic integration
	process is important.}
}
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