An empirical comparison of priority-queue and event-set implementations
Communications of the ACM
The NEURON simulation environment
Neural Computation
On embedding synfire chains in a balanced network
Neural Computation
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Optimizing synaptic conductance calculation for network simulations
Neural Computation
Exact simulation of integrate-and-fire models with synaptic conductances
Neural Computation
Just-in-time connectivity for large spiking networks
Neural Computation
Rule-based firing for network simulations
Neurocomputing
Parallel algorithms for parabolic problems on graphs
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part II
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Realistic neural networks involve the coexistence of stiff, coupled, continuous differential equations arising from the integrations of individual neurons, with the discrete events with delays used for modeling synaptic connections. We present here an integration method, the local variable time-step method (lvardt), that uses separate variable-step integrators for individual neurons in the network. Cells that are undergoing excitation tend to have small time steps, and cells that are at rest with little synaptic input tend to have large time steps. A synaptic input to a cell causes reinitialization of only that cell's integrator without affecting the integration of other cells. We illustrated the use of lvardt on three models: a worst-case synchronizing mutual-inhibition model, a best-case synfire chain model, and a more realistic thalamocortical network model.