The book of GENESIS (2nd ed.): exploring realistic neural models with the GEneral NEural SImulation System
Neural networks with dynamic synapses
Neural Computation
Digital simulation of spiking neural networks
Pulsed neural networks
Fast calculation of short-term depressing synaptic conductances
Neural Computation
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Polychronization: Computation with Spikes
Neural Computation
Expanding NEURON’s Repertoire of Mechanisms with NMODL
Neural Computation
The NEURON Book
Spike-Timing-Dependent Plasticity in Balanced Random Networks
Neural Computation
Bayesian spiking neurons i: Inference
Neural Computation
Optimizing synaptic conductance calculation for network simulations
Neural Computation
Phenomenological models of synaptic plasticity based on spike timing
Biological Cybernetics - Special Issue: Object Localization
Simplicity and efficiency of integrate-and-fire neuron models
Neural Computation
Fast and exact simulation methods applied on a broad range of neuron models
Neural Computation
Compass: a scalable simulator for an architecture for cognitive computing
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
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High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.