On Numerical Simulations of Integrate-and-Fire Neural Networks

Hansel, David; Mato, Germán; Meunier, Claude; Neltner, L.

doi:10.1162/089976698300017845

Cited by 191 publications

(173 citation statements)

References 9 publications

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“…In all cases, this quest leads to the implementation of a specific strategy for numerical simulations which is found to be optimal given the set of constraints set by the particular simulation tool. However, as shown recently (Hansel et al 1998;Lee and Farhat 2001;Morrison et al 2007a), quantitative results and their qualitative interpretation strongly depend on the simulation strategy utilized, and may vary across available simulation tools or for different settings within the same simulator. The specificity of neuronal simulations is that spikes induce either a discontinuity in the dynamics (IF models) or have very fast dynamics (HH type models).…”

Section: Precision Of Different Simulation Strategiesmentioning

confidence: 99%

Simulation of networks of spiking neurons: A review of tools and strategies

et al. 2007

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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 Hodgkin-Huxley type, integrate-andfire models, interacting with current-based or conductance-based synapses, using clock-driven or NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript event-driven 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 modeling problem related to spiking neural networks.

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Section: Precision Of Different Simulation Strategiesmentioning

confidence: 99%

Simulation of networks of spiking neurons: A review of tools and strategies

et al. 2007

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“…Checks were performed to show that this was sufficiently small. For the neural membrane potential equations, interpolation of the spike times and their use in the synaptic currents and potentials were taken into account following the prescription of Hansel, Mato, Meunier, and Neltner (1998) to avoid numerical problems due to the discontinuity of the membrane potential and its derivative at the spike firing time. The external trains of Poisson spikes were generated randomly and independently.…”

Section: Prefrontal Architecturementioning

confidence: 99%

Sequential Memory: A Putative Neural and Synaptic Dynamical Mechanism

Deco

Rolls

2005

Journal of Cognitive Neuroscience

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Abstract& A key issue in the neurophysiology of cognition is the problem of sequential learning. Sequential learning refers to the ability to encode and represent the temporal order of discrete elements occurring in a sequence. We show that the short-term memory for a sequence of items can be implemented in an autoassociation neural network. Each item is one of the attractor states of the network. The autoassociation network is implemented at the level of integrate-and-fire neurons so that the contributions of different biophysical mechanisms to sequence learning can be investigated. It is shown that if it is a property of the synapses or neurons that support each attractor state that they adapt, then everytime the network is made quiescent (e.g., by inhibition), then the attractor state that emerges next is the next item in the sequence. We show with numerical simulations implementations of the mechanisms using (1) a sodium inactivation-based spike-frequency-adaptation mechanism, (2) a Ca 2+ -activated K + current, and (3) short-term synaptic depression, with sequences of up to three items. The network does not need repeated training on a particular sequence and will repeat the items in the order that they were last presented. The time between the items in a sequence is not fixed, allowing the items to be read out as required over a period of up to many seconds. The network thus uses adaptation rather than associative synaptic modification to recall the order of the items in a recently presented sequence. &

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“…1B). The latter determines the accuracy of the numerical simulation and introduces an artificial cutoff for time-scales captured by the simulation [2,10]. In contrast, the event-driven approach is free from the dependence on the temporal resolution by using the exact times of events (Fig.…”

Section: Neuronal Models and Simulation Strategiesmentioning

confidence: 99%

“…1D) or occurrence of additional spikes compared to the more precise event-driven simulations. At the network level, small differences in spike times of individual neurons can lead to crucial differences in the global activity pattern, such as synchronisation [2,5]. We considered a network of 15 Â 15 LIF neurons (see above) with all-to-all excitatory connectivity with fixed weights ðDm ¼ 0:0085Þ and not distance-dependent synaptic transmission delay (0.2 ms).…”

Section: Article In Pressmentioning

confidence: 99%

“…However, despite these advances, the results and, hence, qualitative interpretation of numerical simulations are still tightly bound to the simulation strategy used, and may vary across available simulation tools or for different settings within the same simulator. Specifically for networks of integrateand-fire (IF) neurons, crucial differences in the appearance of synchronous activity patterns were observed, depending on the temporal resolution of the neural simulator [2] or the integration method used [5] (for an evaluation of the dependence on initial conditions, see [1]). …”

Section: Introductionmentioning

confidence: 99%

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How much can we trust neural simulation strategies?

Rudolph

Destexhe

2007

Neurocomputing

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Despite a steady improvement of computational hardware, results of numerical simulation are still tightly bound to the simulation tool and strategy used, and may substantially vary across available simulation tools or for different settings within the same simulator. Clockdriven simulation strategies proved efficient for large and highly active networks but are outperformed with respect to precision by the recently introduced event-driven strategies. Focusing on most commonly used clock-driven and event-driven approaches, in this paper we evaluate to which extent the temporal precision of spiking events impacts on neuronal dynamics of single as well as small networks of IF neurons with plastic synapses. We find that the used strategy can severely alter simulated neural dynamics and, therefore, turns out to be crucial for the interpretation of the result of numerical simulations. Drastic differences were observed in models with spike timing dependent plasticity, arguing that the speed of neuronal simulations should not be the sole criteria for evaluation of the efficiency of simulation tools, but must complement an evaluation of their exactness, possibly in disfavour of their speed.

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On Numerical Simulations of Integrate-and-Fire Neural Networks

Cited by 191 publications

References 9 publications

Simulation of networks of spiking neurons: A review of tools and strategies

Simulation of networks of spiking neurons: A review of tools and strategies

Sequential Memory: A Putative Neural and Synaptic Dynamical Mechanism

How much can we trust neural simulation strategies?

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