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21
Introducing numerical bounds to improve eventbased neural network simulation, 2009, http://hal.inria.fr/inria00382534/en/, RR6924, Rapport de recherche
"... apport de recherche ..."
To which extend is the ”neural code” a metric
 In Neurocomp
, 2008
"... Here is proposed a review of the different choices to structure spike trains, using deterministic metrics. Temporal constraints observed in biological or computational spike trains are first taken into account The relation with existing neural codes (rate coding, rank coding, phase coding,..) is the ..."
Abstract

Cited by 5 (5 self)
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Here is proposed a review of the different choices to structure spike trains, using deterministic metrics. Temporal constraints observed in biological or computational spike trains are first taken into account The relation with existing neural codes (rate coding, rank coding, phase coding,..) is then discussed. To which extend the “neural code ” contained in spike trains is related to a metric appears to be a key point, a generalization of the VictorPurpura metric family being proposed for temporal constrained causal spike trains.
A view of Neural Networks as dynamical systems
 in "International Journal of Bifurcations and Chaos", 2009, http://lanl.arxiv.org/abs/0901.2203
"... We present some recent investigations resulting from the modelling of neural networks as dynamical systems, and dealing with the following questions, adressed in the context of specific models. (i). Characterizing the collective dynamics; (ii). Statistical analysis of spikes trains; (iii). Interplay ..."
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Cited by 2 (1 self)
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We present some recent investigations resulting from the modelling of neural networks as dynamical systems, and dealing with the following questions, adressed in the context of specific models. (i). Characterizing the collective dynamics; (ii). Statistical analysis of spikes trains; (iii). Interplay between dynamics and network structure; (iv). Effects of synaptic plasticity.
Journal of Computational Neuroscience manuscript No. (will be inserted by the editor)
"... Selfsustained asynchronous irregular states and Up–Down states in thalamic, cortical and thalamocortical networks of nonlinear integrateandfire neurons ..."
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Selfsustained asynchronous irregular states and Up–Down states in thalamic, cortical and thalamocortical networks of nonlinear integrateandfire neurons
STATISTICS OF SPIKES TRAINS, SYNAPTIC PLASTICITY AND
, 810
"... We introduce a mathematical framework where the statistics of spikes trains, produced by neural networks evolving under synaptic plasticity, can be analysed. ..."
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We introduce a mathematical framework where the statistics of spikes trains, produced by neural networks evolving under synaptic plasticity, can be analysed.
unknown title
, 2009
"... How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A modelbased argumentation. ..."
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How Gibbs distributions may naturally arise from synaptic adaptation mechanisms. A modelbased argumentation.
networks of conductancebased integrateandfire neurons
"... Abstract The relationship between the dynamics of neural networks and their patterns of connectivity is far from clear, despite its importance for understanding functional properties. Here, we have studied sparselyconnected networks of conductancebased integrateandfire (IF) neurons with balanced e ..."
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Abstract The relationship between the dynamics of neural networks and their patterns of connectivity is far from clear, despite its importance for understanding functional properties. Here, we have studied sparselyconnected networks of conductancebased integrateandfire (IF) neurons with balanced excitatory and inhibitory connections and with finite axonal propagation speed. We focused on the genesis of states with highly irregular spiking activity and synchronous firing patterns at low rates, called slow Synchronous Irregular (SI) states. In such balanced networks, we examined the “macroscopic ” properties of the spiking activity, such as ensemble correlations and mean firing rates, for different intracortical connectivity profiles ranging from randomly connected networks to networks with Gaussiandistributed local connectivity. We systematically computed the distancedependent correlations at the extracellular (spiking) and intracellular (membrane potential) levels between randomly assigned pairs of neurons. The main finding is that such properties, when they are averaged at a macroscopic scale, are invariant with respect to the different connectivity patterns, Action Editor: Mark van Rossum Pierre Yger and Sami El Boustani contributed equally. Electronic supplementary material The online version of this article (doi:10.1007/s108270100310z) contains supplementary material, which is available to authorized users.
Dynamics of large cooperative pulsedcoupled networks
"... We study the deterministic dynamics of networks N composed by m non identical, mutually pulsecoupled cells. We assume weighted, asymmetric and positive (cooperative) interactions among the cells, and arbitrarily large values of m. We consider two cases of the network’s graph: the complete graph, an ..."
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We study the deterministic dynamics of networks N composed by m non identical, mutually pulsecoupled cells. We assume weighted, asymmetric and positive (cooperative) interactions among the cells, and arbitrarily large values of m. We consider two cases of the network’s graph: the complete graph, and the existence of a large core (i.e. a large complete subgraph). First, we prove that the system periodically eventually synchronizes with a natural “spiking period ” p ≥ 1, and that if the cells are mutually structurally identical or similar, then the synchronization is complete (p = 1). Second, we prove that the amount of information H that N generates or processes, equals log p. Therefore, if N completely synchronizes, the information is null. Finally, we prove that N protects the cells from their risk of death.