Back-engineering of spiking neural networks parameters

We consider the deterministic evolution of a time-discretized spiking network of neurons with connection weights having delays, modeled as a discretized neural network of the generalized integrate and fire (gIF) type. The purpose is to study a class of algorithmic methods allowing to calculate the proper parameters to reproduce exactly a given spike train generated by an hidden (unknown) neural network. This standard problem is known as NP-hard when delays are to be calculated. We propose here a reformulation, now expressed as a Linear-Programming (LP) problem, thus allowing to provide an efficient resolution. This allows us to "back-engineer" a neural network, i.e. to find out, given a set of initial conditions, which parameters (i.e., connection weights in this case), allow to simulate the network spike dynamics. More precisely we make explicit the fact that the back-engineering of a spike train, is a Linear (L) problem if the membrane potentials are observed and a LP problem if only spike times are observed, with a gIF model. Numerical robustness is discussed. We also explain how it is the use of a generalized IF neuron model instead of a leaky IF model that allows us to derive this algorithm. Furthermore, we point out how the L or LP adjustment mechanism is local to each unit and has the same structure as an "Hebbian" rule. A step further, this paradigm is easily generalizable to the design of input-output spike train transformations. This means that we have a practical method to "program" a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.Comment: 30 pages, 17 figures, submitte

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

How Gibbs distributions may naturally arise from synaptic adaptation mechanisms

International audienc

Currarino Syndrome and HPE Microform Associated with a 2.7-Mb Deletion in 7q36.3 Excluding SHH Gene

International audienceHoloprosencephaly (HPE) is the most common forebrain defect in humans. It results from incomplete midline cleavage of the prosencephalon and can be caused by environmental and genetic factors. HPE is usually described as a continuum of brain malformations from the most severe alobar HPE to the middle interhemispheric fusion variant or syntelencephaly. A microform of HPE is limited to craniofacial features such as congenital nasal pyriform aperture stenosis and single central maxillary incisor, without brain malformation. Among the heterogeneous causes of HPE, point mutations and deletions in the SHH gene at 7q36 have been identified as well as extremely rare chromosomal rearrangements in the long-range enhancers of this gene. Here, we report a boy with an HPE microform associated with a Currarino syndrome. Array CGH detected a de novo 2.7-Mb deletion in the 7q36.3 region including the MNX1 gene, usually responsible for the Currarino triad but excluding SHH, which is just outside the deletion. This new case provides further evidence of the importance of the SHH long-range enhancers in the HPE spectrum

Effector memory T cell responses are associated with protection of rhesus monkeys from mucosal simian immunodeficiency virus challenge

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