Exact simulation of integrate-and-fire models with synaptic conductances.
about
A robust and biologically plausible spike pattern recognition networkSimulation of networks of spiking neurons: a review of tools and strategies.Time-warp-invariant neuronal processing.Equation-oriented specification of neural models for simulations.NEVESIM: event-driven neural simulation framework with a Python interfaceAnalytical integrate-and-fire neuron models with conductance-based dynamics and realistic postsynaptic potential time course for event-driven simulation strategies.Reducing the computational footprint for real-time BCPNN learning.Equation-free analysis of spike-timing-dependent plasticity.Average synaptic activity and neural networks topology: a global inverse problem.Distinct current modules shape cellular dynamics in model neurons.Spiking neural network simulation: numerical integration with the Parker-Sochacki method.An efficient and accurate solver for large, sparse neural networks.Synchronization and long-time memory in neural networks with inhibitory hubs and synaptic plasticity.Heterogeneous mean field for neural networks with short-term plasticity.Event-driven simulations of a plastic, spiking neural network.A Markovian event-based framework for stochastic spiking neural networks.On the performance of voltage stepping for the simulation of adaptive, nonlinear integrate-and-fire neuronal networks.Simplicity and efficiency of integrate-and-fire neuron models.Generalization of the event-based Carnevale-Hines integration scheme for integrate-and-fire models.Accelerating event-driven simulation of spiking neurons with multiple synaptic time constants.Event-driven simulations of nonlinear integrate-and-fire neurons.Exact simulation of integrate-and-fire models with exponential currents.Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.Toward unified hybrid simulation techniques for spiking neural networks.Relationship between applicability of current-based synapses and uniformity of firing patterns.Accuracy evaluation of numerical methods used in state-of-the-art simulators for spiking neural networks.Event-Driven Simulation of Arbitrary Spiking Neural Networks on SpiNNakerCoincidence Detection Using Spiking Neurons with Application to Face Recognition
P2860
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P2860
Exact simulation of integrate-and-fire models with synaptic conductances.
description
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name
Exact simulation of integrate-and-fire models with synaptic conductances.
@en
Exact simulation of integrate-and-fire models with synaptic conductances.
@nl
type
label
Exact simulation of integrate-and-fire models with synaptic conductances.
@en
Exact simulation of integrate-and-fire models with synaptic conductances.
@nl
prefLabel
Exact simulation of integrate-and-fire models with synaptic conductances.
@en
Exact simulation of integrate-and-fire models with synaptic conductances.
@nl
P2860
P1433
P1476
Exact simulation of integrate-and-fire models with synaptic conductances.
@en
P2860
P304
P356
10.1162/NECO.2006.18.8.2004
P577
2006-08-01T00:00:00Z