Lytton W W
Department of Neurology, University of Wisconsin, Madison 53706, USA.
Neural Comput. 1996 Apr 1;8(3):501-9. doi: 10.1162/neco.1996.8.3.501.
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.
