Results 1  10
of
26
Simulation of networks of spiking neurons: A review of tools and strategies
 Journal of Computational Neuroscience
, 2007
"... We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on ..."
Abstract

Cited by 108 (29 self)
 Add to MetaCart
We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including HodgkinHuxley type, integrateandfire models, interacting with currentbased or conductancebased synapses, using clockdriven or eventdriven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given
neuroConstruct: a tool for modeling networks in 3D space. Neuron 54:219–235
, 2007
"... Conductancebased neuronal network models can help us understand how synaptic and cellular mechanisms underlie brain function. However, these complex models are difficult to develop and are inaccessible to most neuroscientists. Moreover, even the most biologically realistic network models disregard ..."
Abstract

Cited by 39 (6 self)
 Add to MetaCart
(Show Context)
Conductancebased neuronal network models can help us understand how synaptic and cellular mechanisms underlie brain function. However, these complex models are difficult to develop and are inaccessible to most neuroscientists. Moreover, even the most biologically realistic network models disregard many 3D anatomical features of the brain. Here, we describe a new software application, neuroConstruct, that facilitates the creation, visualization, and analysis of networks of multicompartmental neurons in 3D space. A graphical user interface allows model generation and modification without programming. Models within neuroConstruct are based on new simulatorindependent NeuroML standards, allowing automatic generation of code for NEURON or GENESIS simulators. neuroConstruct was tested by reproducing published models and its simulator independence verified by comparing the same model on two simulators. We show how more anatomically realistic network models can be created and their properties compared with experimental measurements by extending a published 1D cerebellar granule cell layer model to 3D.
Interoperability of Neuroscience Modeling Software: Current Status and Future Directions
, 2007
"... Abstract Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is in ..."
Abstract

Cited by 28 (13 self)
 Add to MetaCart
(Show Context)
Abstract Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is in
NEURON: a tool for neuroscientists
 The Neuroscientist
, 2001
"... A revised preprint of: ..."
(Show Context)
Discrete event simulation in the NEURON environment
 Neurocomputing
, 2004
"... Hines and Carnevale: Discrete event simulation in the NEURON environment Page 1 Preprint of a manuscript that will be published in Neurocomputing. ..."
Abstract

Cited by 12 (5 self)
 Add to MetaCart
(Show Context)
Hines and Carnevale: Discrete event simulation in the NEURON environment Page 1 Preprint of a manuscript that will be published in Neurocomputing.
Simulating spiking neural networks on GPU
, 2012
"... Abstract Modern graphics cards contain hundreds of cores that can be programmed for intensive calculations. They are beginning to be used for spiking neural network simulations. The goal is to make parallel simulation of spiking neural networks available to a large audience, without the requirement ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract Modern graphics cards contain hundreds of cores that can be programmed for intensive calculations. They are beginning to be used for spiking neural network simulations. The goal is to make parallel simulation of spiking neural networks available to a large audience, without the requirements of a cluster. We review the ongoing efforts towards this goal, and we outline the main difficulties.
EFFICIENT DISCRETE EVENT SIMULATION OF SPIKING NEURONS IN NEURON
"... Recent releases of NEURON can perform efficient discrete event simulations of networks of integrate−and−fire spiking neurons, as well as hybrid simulations involving both integrate−and−fire neurons and cells with voltage−gated conductances. This is made possible by NEURON’s event delivery system, wh ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Recent releases of NEURON can perform efficient discrete event simulations of networks of integrate−and−fire spiking neurons, as well as hybrid simulations involving both integrate−and−fire neurons and cells with voltage−gated conductances. This is made possible by NEURON’s event delivery system, which opens up a large domain of problems in which certain types of &quot;artificial &quot; spiking cells, and networks of them, can be simulated hundreds of times faster than with numerical integration. Discrete event simulations are possible when all state variables of a model cell can be computed analytically from a new set of initial conditions. Computations are performed only when an event occurs, so total computation time is proportional to the number of events delivered, and is independent of the problem time and the numbers of cells and connections. Thus a simulation that involves 10 5 spikes in 1 hour for 100 cells takes the same time as one with 10 5 spikes in 1 second for 1 cell. The three classes of integrate−and− fire neurons built into NEURON are leaky integrators that differ in their response to input events. An input of weight w to an IntFire1 cell makes its &quot;membrane potential &quot; jump instantaneously by that amount. IntFire2 integrates a steady bias current plus a net synaptic current with first order kinetics that is driven by input events. Excitatory events to IntFire3 drive a &quot;depolarizing &quot; current with fast first order kinetics, while inhibitory events drive a &quot;hyperpolarizing &quot; current with slower, second order kinetics.
SEE PROFILE
"... The spatial pattern of light determines the kinetics and modulates backpropagation of optogenetic action potentials ..."
Abstract
 Add to MetaCart
(Show Context)
The spatial pattern of light determines the kinetics and modulates backpropagation of optogenetic action potentials
London
"... comments on the manuscript. This work was funded by the MRC, Wellcome Trust and the EC ..."
Abstract
 Add to MetaCart
(Show Context)
comments on the manuscript. This work was funded by the MRC, Wellcome Trust and the EC
SOFTWARE Open Access
"... Teaching and learning the HodgkinHuxley model based on software developed in NEURON’s programming language hoc ..."
Abstract
 Add to MetaCart
(Show Context)
Teaching and learning the HodgkinHuxley model based on software developed in NEURON’s programming language hoc