Parallel and pseudorandom discrete event system specification vs. networks of spiking neurons: Formalization and preliminary implementation results

International audienceUsual Parallel Discrete Event System Specification (P-DEVS) allows specifying systems from modeling to simulation. However, the framework does not incorporate parallel and stochastic simulations. This work intends to extend P-DEVS to parallel simulations and pseudorandom number generators in the context of a spiking neural network. The discrete event specification presented here makes explicit and centralized the parallel computation of events as well as their routing, making further implementations more easy. It is then expected to dispose of a well defined mathematical and computational framework to deal with networks of spiking neurons

Verification of Temporal Properties of Neuronal Archetypes Modeled as Synchronous Reactive Systems

International audienceThere exists many ways to connect two, three or more neu-rons together to form different graphs. We call archetypes only the graphs whose properties can be associated with specific classes of biologically relevant structures and behaviors. These archetypes are supposed to be the basis of typical instances of neuronal information processing. To model different representative archetypes and express their temporal properties, we use a synchronous programming language dedicated to reactive systems (Lustre). The properties are then automatically validated thanks to several model checkers supporting data types. The respective results are compared and depend on their underlying abstraction methods

Vérification de propriétés temporelles d'archéypes de neurones à l'aide de modèles synchrones

There exists many ways to connect two, three or more neurons together to form different graphs. We call archetypes only the graphs whose properties can be associated with specific classes of biologically relevant structures and behaviors. These archetypes are supposed to be the basis of typical instances of neuronal information processing. To model different representative archetypes and express their temporal properties, we use a synchronous programming language dedicated to reactive systems (Lustre). The properties are then automatically validated thanks to several model checkers supporting data types. The respective results are compared and depend on their underlying abstraction methods

Responses of mirror neurons in area F5 to hand and tool grasping observation

Mirror neurons are a distinct class of neurons that discharge both during the execution of a motor act and during observation of the same or similar motor act performed by another individual. However, the extent to which mirror neurons coding a motor act with a specific goal (e.g., grasping) might also respond to the observation of a motor act having the same goal, but achieved with artificial effectors, is not yet established. In the present study, we addressed this issue by recording mirror neurons from the ventral premotor cortex (area F5) of two monkeys trained to grasp objects with pliers. Neuron activity was recorded during the observation and execution of grasping performed with the hand, with pliers and during observation of an experimenter spearing food with a stick. The results showed that virtually all neurons responding to the observation of hand grasping also responded to the observation of grasping with pliers and, many of them to the observation of spearing with a stick. However, the intensity and pattern of the response differed among conditions. Hand grasping observation determined the earliest and the strongest discharge, while pliers grasping and spearing observation triggered weaker responses at longer latencies. We conclude that F5 grasping mirror neurons respond to the observation of a family of stimuli leading to the same goal. However, the response pattern depends upon the similarity between the observed motor act and the one executed by the hand, the natural motor template

On the Use of Formal Methods to Model and Verify Neuronal Archetypes

International audienceHaving a formal model of neural networks can greatly help in understanding and verifying their properties, behavior, and response to external factors such as disease and medicine. In this paper, we adopt a formal model to represent neurons, some neuronal graphs, and their composition. Some specific neuronal graphs are known for having biologically relevant structures and behaviors and we call them archetypes. These archetypes are supposed to be the basis of typical instances of neuronal information processing. In this paper we study six fundamental archetypes (simple series, series with multiple outputs, parallel composition, negative loop, inhibition of a behavior, and contralateral inhibition), and we consider two ways to couple two archetypes: (i) connecting the output(s) of the first archetype to the input(s) of the second archetype and (ii) nesting the first archetype within the second one. We report and compare two key approaches to the formal modeling and verification of the proposed neuronal archetypes and some selected couplings. The first approach exploits the synchronous programming language Lustre to encode archetypes and their couplings, and to express properties concerning their dynamic behavior. These properties are verified thanks to the use of model checkers. The second approach relies on a theorem prover, the Coq Proof Assistant, to prove dynamic properties of neurons and archetype

La question des enchevêtrements hiérarchiques dans les Sciences du vivant en général et dans les Neurosciences en particulier

Le terme d’enchevêtrement hiérarchique est avant tout issu de la Logique, en tant que discipline. Cependant, cette notion se définit généralement de manière faible et l’on doit le plus souvent utiliser d’autres concepts tels ceux de circularité, d’autoréférence, d’imprédicativité et d’autres encore pour tenter d’en expliquer le sens. Dans ce contexte, la notion d’enchevêtrement hiérarchique fait donc principalement référence au fait qu’au sein de certains systèmes, certains éléments peuvent s..

Rôle fonctionnel de la coopérativité neuronale impliquée dans la préparation à l'action

AIX-MARSEILLE1-BU Sci.St Charles (130552104) / SudocSudocFranceF

Naturalizing Intention in action

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Iterative Specification as a Modeling and Simulation Formalism for I/O General Systems

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