Event-driven simulation scheme for spiking neural networks using lookup tables to characterize neuronal dynamics. Ros, E.; Carrillo, R.; Ortigosa, E. M.; Barbour, B.; and Ag�s, R. Neural Computation, 18:2959--2993, Dec, 2006.
abstract   bibtex   
Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.
@article{ Ros_etal06a,
  author = {Ros, E. and Carrillo, R. and Ortigosa, E. M. and Barbour, B. and
	Ag�s, R. },
  title = {{{E}vent-driven simulation scheme for spiking neural networks using
	lookup tables to characterize neuronal dynamics}},
  journal = {Neural Computation},
  year = {2006},
  volume = {18},
  pages = {2959--2993},
  month = {Dec},
  abstract = {Nearly all neuronal information processing and interneuronal communication
	in the brain involves action potentials, or spikes, which drive the
	short-term synaptic dynamics of neurons, but also their long-term
	dynamics, via synaptic plasticity. In many brain structures, action
	potential activity is considered to be sparse. This sparseness of
	activity has been exploited to reduce the computational cost of large-scale
	network simulations, through the development of event-driven simulation
	schemes. However, existing event-driven simulations schemes use extremely
	simplified neuronal models. Here, we implement and evaluate critically
	an event-driven algorithm (ED-LUT) that uses precalculated look-up
	tables to characterize synaptic and neuronal dynamics. This approach
	enables the use of more complex (and realistic) neuronal models or
	data in representing the neurons, while retaining the advantage of
	high-speed simulation. We demonstrate the method's application for
	neurons containing exponential synaptic conductances, thereby implementing
	shunting inhibition, a phenomenon that is critical to cellular computation.
	We also introduce an improved two-stage event-queue algorithm, which
	allows the simulations to scale efficiently to highly connected networks
	with arbitrary propagation delays. Finally, the scheme readily accommodates
	implementation of synaptic plasticity mechanisms that depend on spike
	timing, enabling future simulations to explore issues of long-term
	learning and adaptation in large-scale networks.}
}
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