Fast calculation of synaptic conductances
Neural Computation
Learning in neural networks with material synapses
Neural Computation
On numerical simulations of integrate-and-fire neural networks
Neural Computation
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation
Neural Computation
Spike-driven synaptic dynamics generating working memory states
Neural Computation
Event-driven simulation of spiking neurons with stochastic dynamics
Neural Computation
Parallel Event-Driven Neural Network Simulations Using the Hodgkin-Huxley Neuron Model
Proceedings of the 19th Workshop on Principles of Advanced and Distributed Simulation
Attractor Networks for Shape Recognition
Neural Computation
Exact simulation of integrate-and-fire models with synaptic conductances
Neural Computation
A distributed and multithreaded neural event driven simulation framework
PDCN'06 Proceedings of the 24th IASTED international conference on Parallel and distributed computing and networks
Spiking neural nets with symbolic internal state
Information Processing Letters - Special issue on applications of spiking neural networks
Towards cortex sized artificial neural systems
Neural Networks
Mean-driven and fluctuation-driven persistent activity in recurrent networks
Neural Computation
How much can we trust neural simulation strategies?
Neurocomputing
Exact Simulation of Integrate-and-Fire Models with Exponential Currents
Neural Computation
Parallel computation in spiking neural nets
Theoretical Computer Science
Event-driven simulations of nonlinear integrate-and-fire neurons
Neural Computation
Anatomy of a cortical simulator
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Just-in-time connectivity for large spiking networks
Neural Computation
Spike-timing error backpropagation in theta neuron networks
Neural Computation
The cat is out of the bag: cortical simulations with 109 neurons, 1013 synapses
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Spiking neural nets with symbolic internal state
Information Processing Letters - Special issue on applications of spiking neural networks
Rule-based firing for network simulations
Neurocomputing
Spiking neural networks for reconfigurable POEtic tissue
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Scalable event-driven native parallel processing: the SpiNNaker neuromimetic system
Proceedings of the 7th ACM international conference on Computing frontiers
Spiking neural network simulation: memory-optimal synaptic event scheduling
Journal of Computational Neuroscience
Adaptive learning procedure for a network of spiking neurons and visual pattern recognition
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Lookup table powered neural event-driven simulator
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Spiking neurons computing platform
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Coarse-grained event tree analysis for quantifying Hodgkin-Huxley neuronal network dynamics
Journal of Computational Neuroscience
Journal of Computational Neuroscience
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A simulation procedure is described for making feasible large-scale simulations of recurrent neural networks of spiking neurons and plastic synapses. The procedure is applicable if the dynamic variables of both neurons and synapses evolve deterministically between any two successive spikes. Spikes introduce jumps in these variables, and since spike trains are typically noisy, spikes introduce stochasticity into both dynamics. Since all events in the simulation are guided by the arrival of spikes, at neurons or synapses, we name this procedure event-driven.The procedure is described in detail, and its logic and performance are compared with conventional (synchronous) simulations. The main impact of the new approach is a drastic reduction of the computational load incurred upon introduction of dynamic synaptic efficacies, which vary organically as a function of the activities of the pre- and postsynaptic neurons. In fact, the computational load per neuron in the presence of the synaptic dynamics grows linearly with the number of neurons and is only about 6% more than the load with fixed synapses. Even the latter is handled quite efficiently by the algorithm.We illustrate the operation of the algorithm in a specific case with integrate-and-fire neurons and specific spike-driven synaptic dynamics. Both dynamical elements have been found to be naturally implementable in VLSI. This network is simulated to show the effects on the synaptic structure of the presentation of stimuli, as well as the stability of the generated matrix to the neural activity it induces.