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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 ..."
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Cited by 106 (29 self)
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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
Fluctuating synaptic conductances recreate in vivolike activity in neocortical neurons
 Neuroscience
, 2001
"... AbstractöTo investigate the basis of the £uctuating activity present in neocortical neurons in vivo, we have combined computational models with wholecell recordings using the dynamicclamp technique. A simpli¢ed `pointconductance' model was used to represent the currents generated by thousand ..."
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Cited by 80 (23 self)
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AbstractöTo investigate the basis of the £uctuating activity present in neocortical neurons in vivo, we have combined computational models with wholecell recordings using the dynamicclamp technique. A simpli¢ed `pointconductance' model was used to represent the currents generated by thousands of stochastically releasing synapses. Synaptic activity was represented by two independent fast glutamatergic and GABAergic conductances described by stochastic randomwalk processes. An advantage of this approach is that all the model parameters can be determined from voltageclamp experiments. We show that the pointconductance model captures the amplitude and spectral characteristics of the synaptic conductances during background activity. To determine if it can recreate in vivolike activity, we injected this pointconductance model into a singlecompartment model, or in rat prefrontal cortical neurons in vitro using dynamic clamp. This procedure successfully recreated several properties of neurons intracellularly recorded in vivo, such as a depolarized membrane potential, the presence of highamplitude membrane potential £uctuations, a lowinput resistance and irregular spontaneous ¢ring activity. In addition, the pointconductance model could simulate the enhancement of responsiveness due to background activity. We conclude that many of the characteristics of cortical neurons in vivo can be explained by fast glutamatergic and
Shunting inhibition improves robustness of gamma oscillations in hippocampal interneuron networks by homogenizing firing rates
 Neuron
, 2006
"... Networks of GABAergic neurons are key elements in the generation of g oscillations in the brain. Computational studies suggested that the emergence of coherent oscillations requireshyperpolarizing inhibition. Here, we show that GABAA receptormediated inhibition in mature interneurons of the hipp ..."
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Cited by 67 (2 self)
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Networks of GABAergic neurons are key elements in the generation of g oscillations in the brain. Computational studies suggested that the emergence of coherent oscillations requireshyperpolarizing inhibition. Here, we show that GABAA receptormediated inhibition in mature interneurons of the hippocampal dentate gyrus is shunting rather than hyperpolarizing. Unexpectedly, when shunting inhibition is incorporated into a structured interneuron network model with fast and strong synapses, coherent oscillations emerge. In comparison to hyperpolarizing inhibition, networks with shunting inhibition show several advantages. First, oscillations are generated with smaller tonic excitatory drive. Second, network frequencies are tuned to the g band. Finally, robustness against heterogeneity in the excitatory drive is markedly improved. In single interneurons, shunting inhibition shortens the interspike interval for low levels of drive but prolongs it for high levels, leading to homogenization of neuronal firing rates. Thus, shunting inhibition may confer increased robustness to g oscillations in the brain.
Advancing the Boundaries of HighConnectivity Network Simulation with Distributed Computing
, 2005
"... The availability of efficient and reliable simulation tools is one of the missioncritical technologies in the fastmoving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of e ..."
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Cited by 63 (23 self)
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The availability of efficient and reliable simulation tools is one of the missioncritical technologies in the fastmoving field of computational neuroscience. Research indicates that higher brain functions emerge from large and complex cortical networks and their interactions. The large number of elements (neurons) combined with the high connectivity (synapses) of the biological network and the specific type of interactions impose severe constraints on the explorable system size that previously have been hard to overcome. Here we present a collection of new techniques combined to a coherent simulation tool removing the fundamental obstacle in the computational study of biological neural networks: the enormous number of synaptic contacts per neuron. Distributing an individual simulation over multiple computers enables the investigation of networks orders of magnitude larger than previously possible. The
Spikenet: an eventdriven simulation package for modelling large networks of spiking neurons
 Neural Networks
, 2003
"... Abstract: Many biological neural network models face the problem of scalability because of the limited computational power of today’s computers. Thus, it is difficult to assess the efficiency of these models to solve complex problems such as image processing. Here, we describe how this problem can b ..."
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Cited by 37 (1 self)
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Abstract: Many biological neural network models face the problem of scalability because of the limited computational power of today’s computers. Thus, it is difficult to assess the efficiency of these models to solve complex problems such as image processing. Here, we describe how this problem can be tackled using eventdriven computation. Only the neurons that emit a discharge are processed and, as long as the average spike discharge rate is low, millions of neurons and billions of connections can be modeled. We describe the underlying computation and implementation of such a mechanism in SpikeNET, our neural network simulation package. The type of model one can build is not only biologically compliant, it is also computationally efficient as 400 000 synaptic weights can be propagated per second on a standard desktop computer. In addition, for large networks, we can set very small time steps (less than 0.01 ms) without significantly increasing the computation time. As an example, this method is applied to solve complex cognitive tasks such as face recognition in natural images.
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 ..."
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Cited by 37 (6 self)
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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.
Characterization of Subthreshold Voltage Fluctuations in Neuronal Membranes
, 2003
"... Synaptic noise due to intense network activity can have a significant impact on the electrophysiological properties of individual neurons. This is the case for the cerebral cortex, where ongoing activity leads to strong barrages of synaptic inputs, which act as the main source of synaptic noise affe ..."
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Cited by 36 (14 self)
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Synaptic noise due to intense network activity can have a significant impact on the electrophysiological properties of individual neurons. This is the case for the cerebral cortex, where ongoing activity leads to strong barrages of synaptic inputs, which act as the main source of synaptic noise affecting on neuronal dynamics. Here, we characterize the subthreshold behavior of neuronal models in which synaptic noise is represented by either additive or multiplicative noise, described by OrnsteinUhlenbeck processes. We derive and solve the FokkerPlanck equation for this system, which describes the time evolution of the probability density function for the membrane potential. We obtain an analytic expression for the membrane potential distribution at steady state and compare this expression with the subthreshold activity obtained in HodgkinHuxleytype models with stochastic synaptic inputs. The differences between multiplicative and additive noise models suggest that multiplicative noise is adequate to describe the highconductance states similar to in vivo conditions. Because the steadystate membrane potential distribution is easily obtained experimentally, this approach provides a possible method to estimate the mean and variance of synaptic conductances in real neurons.
Modeling extracellular field potentials and the frequencyfiltering properties of extracellular space
 Biophys. J
, 2004
"... Extracellular local field potentials are usually modeled as arising from a set of current sources embedded in a homogeneous extracellular medium. Although this formalism can successfully model several properties of extracellular local field potentials, it does not account for their frequencydepende ..."
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Cited by 34 (8 self)
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Extracellular local field potentials are usually modeled as arising from a set of current sources embedded in a homogeneous extracellular medium. Although this formalism can successfully model several properties of extracellular local field potentials, it does not account for their frequencydependent attenuation with distance, a property essential to correctly model extracellular spikes. Here we derive expressions for the extracellular potential that include this frequencydependent attenuation. We first show that, if the extracellular conductivity is nonhomogeneous, there is induction of nonhomogeneous charge densities that may result in a lowpass filter. We next derive a simplified model consisting of a punctual (or spherical) current source with spherically symmetric conductivity/permittivity gradients around the source. We analyze the effect of different radial profiles of conductivity and permittivity on the frequencyfiltering behavior of this model. We show that this simple model generally displays lowpass filtering behavior, in which fast electrical events (such as Na 1mediated action potentials) attenuate very steeply with distance, whereas slower (K 1mediated) events propagate over larger distances in extracellular space, in qualitative agreement with experimental observations. This simple model can be used to obtain frequencydependent extracellular field potentials without taking into account explicitly the complex folding of extracellular space.
A Framework for ThreeDimensional Simulation of Morphogenesis
, 2004
"... We present COMPUCELL3D, a software framework for threedimensional simulation of morphogenesis in different organisms. COMPUCELL3D employs biologically relevant models for cell clustering, growth, and interaction with chemical fields. COMPUCELL3D uses design patterns for speed, efficient memory m ..."
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Cited by 31 (13 self)
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We present COMPUCELL3D, a software framework for threedimensional simulation of morphogenesis in different organisms. COMPUCELL3D employs biologically relevant models for cell clustering, growth, and interaction with chemical fields. COMPUCELL3D uses design patterns for speed, efficient memory management, extensibility, and flexibility, permitting the simulation of various organisms. We verify COMPUCELL3D by building a model of growth and skeletal pattern formation in the avian (chicken) limb bud. Binaries and source code are available, along with documentation and input files for sample simulations, at http://www.nd.edu/lcls/compucell.
Subtractive and Divisive Inhibition: Effect of VoltageDependent Inhibitory Conductances and Noise
, 2001
"... this article). Stochastic forcing also broadens the peak of the average subthreshold voltage versus input current curves as in Figure 5b (not shown). In view of this, we have set out to determine (1) whether a subtractive effect is also present with stochastic synaptic input and (2) whether stochast ..."
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Cited by 29 (3 self)
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this article). Stochastic forcing also broadens the peak of the average subthreshold voltage versus input current curves as in Figure 5b (not shown). In view of this, we have set out to determine (1) whether a subtractive effect is also present with stochastic synaptic input and (2) whether stochastic input produces a divisive regime at lower firing frequencies, as in the compartmental model. For simplicity we considered only the voltageindependent case (# 0) of equation 4.1, since divisiveness was also seen for the S synapses in the compartmental simulations (see Figure 5a). There are a variety of ways in which a stochastic synaptic model with reversal potentials can be approximated by diffusion models (Lansk y & Sato, 1999). Here we let the conductance g in the LIF model be a stochastic quantity by setting g = +#(g)#(t) where g is the mean conductance and #(t) is a stochastic process of standard deviation #( g). To match the smoothness of the conductance fluctuations in the compartmental model, we model #(t) as an OrnsteinUhlenbeck process (lowpassfiltered gaussian white noise) with correlation time # 75 ms; our results were not qualitatively sensitive to this correlation time. Equation 4.1 thus becomes a stochastic differential equation with multiplicative noise (since the noise term multiplies the state variable Vm ): #t + #( g)#(t) I (4.7 # ) ## #t = # +#(4.7 ## ) where # is gaussian white noise with zeromean and unit standard deviation. Numerical simulations produced sigmoidal curves of mean firing rate versus input current I (see Figure 7), as expected. Increases in inhibitory firing rate in the compartmental model were modeled here as increases in mean conductance g; these increases are linearly related, as discussed above...