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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
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.
An eventdriven framework for the simulation of networks of spiking neurons
 in proc. 11th European Symposium On Artificial Neural Networks  ESANN’2003
, 2003
"... Abstract. We propose an eventdriven framework dedicated to the design and the simulation of networks of spiking neurons. It consists of an abstract model of spiking neurons and an efficient eventdriven simulation engine so as to achieve good performance in the simulation phase while maintaining a ..."
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Cited by 23 (7 self)
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Abstract. We propose an eventdriven framework dedicated to the design and the simulation of networks of spiking neurons. It consists of an abstract model of spiking neurons and an efficient eventdriven simulation engine so as to achieve good performance in the simulation phase while maintaining a high level of flexibility and programmability in the modelling phase. Our model of neurons encompasses a large class of spiking neurons ranging from usual leaky integrateandfire neurons to more abstract neurons, e.g. defined as complex finite state machines. As a result, the proposed framework allows the simulation of large networks that can be composed of unique or different types of neurons. 1
EventDriven Simulation Of Spiking Neurons With Stochastic Dynamics
 Neural Computation
, 2002
"... We present a new technique, based on a proposed eventbased strategy (Mattia & Del Giudice, 2000), for efficiently simulating large networks of simple model neurons. The strategy was based on the fact that interactions between neurons occur by means of events which are welllocalized in time (th ..."
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Cited by 18 (1 self)
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We present a new technique, based on a proposed eventbased strategy (Mattia & Del Giudice, 2000), for efficiently simulating large networks of simple model neurons. The strategy was based on the fact that interactions between neurons occur by means of events which are welllocalized in time (the action potentials) and relatively rare. In the interval between two of these events, the state variables associated with a model neuron or a synapse evolved deterministically and in a predictable way. Here we extend the eventdriven simulation strategy to the case in which the dynamics of the state variables in the interevent intervals are stochastic. This extension captures both the situation in which the simulated neurons are inherently noisy and the case in which they are embedded in a very large network and receive a huge number of random synaptic inputs. We show how to effectively include the impact of large background populations into neuronal dynamics by means of the numerical evaluation of the statistical properties of single model neurons under random current injection. The new simulation strategy allows to study networks of interacting neurons with an arbitrary number of external afferents and inherent stochastic dynamics.
Towards Efficient Hardware for SpikeProcessing Neural Networks
 Proc. of the World Congress on Neural Networks
, 1995
"... . We present the requirements for a neurocomputer for spikeprocessing neural networks. In a simulation study we investigated the performance of available hardware and showed, that there is still a need for a specific neurocomputer dedicated to the simulation of spikeprocessing networks. On the bas ..."
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Cited by 15 (5 self)
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. We present the requirements for a neurocomputer for spikeprocessing neural networks. In a simulation study we investigated the performance of available hardware and showed, that there is still a need for a specific neurocomputer dedicated to the simulation of spikeprocessing networks. On the basis of our simulation study and an investigation of the features of spikeprocessing networks we analyses the requirements for the design of dedicated hardware. An efficient hardware architecture should contain an eventlist module, a senderoriented connection module and a number of fixedpoint processing units. 1 Introduction Experimental results [1] [2] together with theoretical studies [3] [4] suggest that the time structure of neuronal spike trains is relevant in neuronal signal processing. The synchronized firing of neuronal assemblies could serve as a versatile and general mechanism for feature binding, pattern segmentation and figure/ground separation. This mechanism could also be u...
Discrete Event Abstraction: An Emerging Paradigm For Modeling Complex Adaptive Systems Perspectives on Adaptation
 John Holland, Ed; Lashon Booker, Oxford University
"... Computer modeling and simulation is recognized by John Holland and many others as the central tool with which to experiment on complex adaptive systems (CAS). Less well recognized is that in the last thirty years, advances in the theory of modeling and simulation have crystallized a new class of mo ..."
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Cited by 14 (4 self)
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Computer modeling and simulation is recognized by John Holland and many others as the central tool with which to experiment on complex adaptive systems (CAS). Less well recognized is that in the last thirty years, advances in the theory of modeling and simulation have crystallized a new class of models suitable for the computational requirements of CAS studies. This article discusses the abstractions underlying the DEVS formalism, a system theoretic characterization of discrete event simulations, that has been widely adopted in recent years. Abstraction of events and time intervals from a continuous data stream is shown to carry information that can be efficiently employed, not only in simulation, but also in accounting for the real world constraints that shape the information processes within CAS. Indeed, an important paradigm is emerging in which discrete event abstraction is recognized as fundamental to modeling CAS phenomena at various levels of organization. Discrete event models of neurons, neural processing architectures, and "fast frugal " bounded rational decision making and shortest path solvers are discussed as examples. Such models capture ideas that are coming from various disparate directions and offer evidence that a new modeling and simulation paradigm is emerging.
Analytical IntegrateandFire Neuron Models with ConductanceBased Dynamics for EventDriven Simulation Strategies
, 2006
"... Eventdriven simulation strategies were proposed recently to simulate integrateandfire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating largescale networks of neurons; most important, such approaches are more precise than traditional cloc ..."
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Cited by 13 (1 self)
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Eventdriven simulation strategies were proposed recently to simulate integrateandfire (IF) type neuronal models. These strategies can lead to computationally efficient algorithms for simulating largescale networks of neurons; most important, such approaches are more precise than traditional clockdriven numerical integration approaches because the timing of spikes is treated exactly. The drawback of such eventdriven methods is that in order to be efficient, the membrane equations must be solvable analytically, or at least provide simple analytic approximations for the state variables describing the system. This requirement prevents, in general, the use of conductancebased synaptic interactions within the framework of eventdriven simulations and, thus, the investigation of network paradigms where synaptic conductances are important. We propose here a number of extensions of the classical leaky IF neuron model involving approximations of the membrane equation with conductancebased synaptic current, which lead to simple analytic expressions for the
Anatomy of a Cortical Simulator
"... Insights into brain’s highlevel computational principles will lead to novel cognitive systems, computing architectures, programming paradigms, and numerous practical applications. An important step towards this end is the study of large networks of cortical spiking neurons. We have built a cortical ..."
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Cited by 12 (1 self)
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Insights into brain’s highlevel computational principles will lead to novel cognitive systems, computing architectures, programming paradigms, and numerous practical applications. An important step towards this end is the study of large networks of cortical spiking neurons. We have built a cortical simulator, C2, incorporating several algorithmic enhancements to optimize the simulation scale and time, through: computationally efficient simulation of neurons in a clockdriven and synapses in an eventdriven fashion; memory efficient representation of simulation state; and communication efficient message exchanges. Using phenomenological, singlecompartment models of spiking neurons and synapses with spiketiming dependent plasticity, we represented a ratscale cortical model (55 million neurons, 442 billion synapses) in 8TB memory of a 32,768processor BlueGene/L. With 1 millisecond resolution for neuronal dynamics and 120 milliseconds axonal delays, C2 can simulate 1 second of model time in 9 seconds per Hertz of average neuronal firing rate. In summary, by combining stateoftheart hardware with innovative algorithms and software design, we simultaneously achieved unprecedented timetosolution on an unprecedented problem size. 1.
Scalable eventdriven native parallel processing: The spinnaker neuromimetic system
 in ACM International Conference on Computing Frontiers
, 2010
"... Neural networks present a fundamentally different model of computation from the conventional sequential digital model. Modelling large networks on conventional hardware thus tends to be inefficient if not impossible. Neither dedicated neural chips, with model limitations, nor FPGA implementations, ..."
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Cited by 9 (7 self)
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Neural networks present a fundamentally different model of computation from the conventional sequential digital model. Modelling large networks on conventional hardware thus tends to be inefficient if not impossible. Neither dedicated neural chips, with model limitations, nor FPGA implementations, with scalability limitations, offer a satisfactory solution even though they have improved simulation performance dramatically. SpiNNaker introduces a different approach, the “neuromimetic ” architecture, that maintains the neural optimisation of dedicated chips while offering FPGAlike universal configurability. Central to this parallel multiprocessor is an asynchronous eventdriven model that uses interruptgenerating dedicated hardware on the chip to support realtime neural simulation. While this architecture is particularly suitable for spiking models, it can also implement “classical ” neural models
A Discrete Event Neural Network Simulator for General Neuron Models
, 2003
"... this paper. The simulator is a 3000step C## program library, which is highly objectoriented and easily used by C## programs. Punnets has a class that simulates any neuron based on SpikeResponse model, as well as an optimised version of classes simulating an integrateandfire neuron with a dynami ..."
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Cited by 6 (0 self)
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this paper. The simulator is a 3000step C## program library, which is highly objectoriented and easily used by C## programs. Punnets has a class that simulates any neuron based on SpikeResponse model, as well as an optimised version of classes simulating an integrateandfire neuron with a dynamic threshold. Since neurons and synapses are designed as an object, a user can use various styles of neurons and synapses, including stochastic neurons and dynamically learning synapses. The library also has a logging ability to record the behaviour of neurons as either event reports or state graphs