Phase-coupling in two-dimensional networks of interacting oscillators
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Alternating and synchronous rhythms in reciprocally inhibitory model neurons
Neural Computation
Fast calculation of synaptic conductances
Neural Computation
Conductance-based integrate-and-fire models
Neural Computation
The NEURON simulation environment
Neural Computation
Fast calculation of short-term depressing synaptic conductances
Neural Computation
Event-driven simulation of spiking neurons with stochastic dynamics
Neural Computation
Exact simulation of integrate-and-fire models with synaptic conductances
Neural Computation
Just-in-time connectivity for large spiking networks
Neural Computation
Synaptic information transfer in computer models of neocortical columns
Journal of Computational Neuroscience
Vectorized algorithms for spiking neural network simulation
Neural Computation
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High computational requirements in realistic neuronal network simulations have led to attempts to realize implementation efficiencies while maintaining as much realism as possible. Since the number of synapses in a network will generally far exceed the number of neurons, simulation of synaptic activation may be a large proportion of total processing time. We present a consolidating algorithm based on a recent biophysically-inspired simplified Markov model of the synapse. Use of a single lumped state variable to represent a large number of converging synaptic inputs results in substantial speed-ups.