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

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-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or 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.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

Fast calculation of short-term depressing synaptic conductances

An efficient implementation of synaptic transmission models in realistic network simulations is an important theme of computational neuroscience. The amount of CPU time required to simulate synaptic interactions can increase as the square of the number of units of such networks, depending on the connectivity convergence. As a consequence, any realistic description of synaptic phenomena, incorporating biophysical details, is computationally highly demanding. We present a consolidating algorithm based on a biophysical extended model of ligand-gated postsynaptic channels, describing short-term plasticity such as synaptic depression. The considerable speedup of simulation times makes this algorithm suitable for investigating emergent collective effects of short-term depression in large-scale networks of model neurons

Modélisation mathématique de la dépression synaptique et des périodes réfractaires pour le Quantron

RÉSUMÉ : La reconnaissance de formes est un mécanisme omniprésent dans le cerveau humain. Dans le cas des ordinateurs, ce mécanisme est plus complexe et plus lourd à effectuer. Des modèles de réseaux de neurones artificiels (RNA) ont été développés afin de pallier aux limites des machines. Le Quantron, dont le potentiel à générer des formes hautement non-linéaires a été démontré, est cependant incapable de produire des formes entièrement convexes. L’idée de raffiner le modèle du Quantron en y ajoutant des considérations biologiques est explorée. La dépression synaptique et les périodes réfractaires servent de tremplin pour ce raffinement, dans le but d’augmenter le potentiel de reconnaissance du Quantron. L’influence de la dépression synaptique est testée en développant trois modèles différents. Le premier modèle correspond à une dépression très brusque. Le deuxième modèle représente une dépression plus lisse. Le troisième modèle se rapproche encore plus de la réalité. Ces modèles ont amené une variabilité inhibitrice importante pour les frontières de séparation. Ils ont également permis d’obtenir des formes convexes presque circulaires (îlots). L’ajout de périodes réfractaires a également été étudié en développant trois modèles supplémentaires. Ceux-ci ont montré que la contribution aux frontières de séparation des périodes réfractaires, sous formes de processus stochastique, est significative, mais peu ciblée. En combinant ces deux notions biologiques on a pu montrer, notamment grâce aux propriétés excitatrices et inhibitrices des périodes réfractaires, ainsi qu’à travers la capacité de filtre fréquentiel de la dépression, que le nouveau Quantron ainsi créé peut générer, pour la première fois, des îlots convexes en utilisant très peu de paramètres.----------ABSTRACT : Pattern recognition is a ubiquitous mechanism of the human brain. For computers, this mechanism is more complex and computationally harder to perform. Artificial neural networks have been developed to overcome the limits of machines. The Quantron, whose potential to generate highly non-linear forms has been demonstrated, is however unable to produce forms entirely convex. The idea of creating a more sophisticated Quantron model by adding biological considerations is explored. Synaptic depression and refractory periods serve as a springboard to this sophistication, in order to enhance the recognition potential of the Quantron. Effects of synaptic depression are tested by developing three different models. The first model corresponds to an abrupt depression. The second model represents a smoother depression. The third model represents reality even more closely. The three models have provided major inhibitory variability to the decision boundaries. They were able to obtain convex forms nearly circular (“island-shaped”). The insertion of refractory period has also been studied by developing three additional models. They showed that contribution to decision boundaries with refractory periods, as stochastic process, is significant, but lacks focus. By joining these two biological notions, it has been shown, especially through excitatory and inhibitory properties of the refractory periods, as well as through the frequency filter capability of the depression, that the new created Quantron could generate, for the first time, convex islands using very few parameters