An Emergent Model for Mimicking Human Neuronal Pathways in Silico

International audienceIn this study, our aim is to mimick human neuronal pathways without assuming the transition from microscopic to macroscopic scales depend upon mathematical arguments. Human neuronal pathways are natural complex systems in which large sets of neurons interact locally and give bottomup rise to collective macroscopic behaviors. In this sense, correct knowledge of the synaptic effective connections between neurons is a key prerequisite for relating them to the operation of their central nervous system (CNS). However, estimating these effective connections between neurons in the human CNS poses a great challenge since direct recordings are impossible. Consequently, the network between human neurons is often expressed as a black box and the properties of connections between neurons are estimated using indirect methods (Türker and Powers, 2005). In indirect methods a particular receptor system is stimulated and the responses of neurons that are affected by the stimulus recorded to estimate the properties of the circuit. However, these neuronal circuits in human subjects are only estimations and their existence cannot be directly proven. Furthermore, there is no satisfactory theory on how these unknown parts of the CNS operate

Adaptive Synchronization of Robotic Sensor Networks

The main focus of recent time synchronization research is developing power-efficient synchronization methods that meet pre-defined accuracy requirements. However, an aspect that has been often overlooked is the high dynamics of the network topology due to the mobility of the nodes. Employing existing flooding-based and peer-to-peer synchronization methods, are networked robots still be able to adapt themselves and self-adjust their logical clocks under mobile network dynamics? In this paper, we present the application and the evaluation of the existing synchronization methods on robotic sensor networks. We show through simulations that Adaptive Value Tracking synchronization is robust and efficient under mobility. Hence, deducing the time synchronization problem in robotic sensor networks into a dynamic value searching problem is preferable to existing synchronization methods in the literature.Comment: First International Workshop on Robotic Sensor Networks part of Cyber-Physical Systems Week, Berlin, Germany, 14 April 201

Exploration of biological neural wiring using self-organizing agents

Cette thèse présente un nouveau modèle computationnel capable de détecter les configurations temporelles d'une voie neuronale donnée afin d'en construire sa copie artificielle. Cette construction représente un véritable défi puisqu'il est impossible de faire des mesures directes sur des neurones individuels dans le système nerveux central humain et que la voie neuronale sous-jacente doit être considérée comme une boîte noire. La théorie des Systèmes Multi-Agents Adaptatifs (AMAS) est utilisée pour relever ce défi. Dans ces systèmes auto-organisateurs, un grand nombre d'agents logiciels coopératifs interagissent localement pour donner naissance à un comportement collectif ascendant. Le résultat est un modèle émergent dans lequel chaque entité logicielle représente un neurone " intègre-et-tire ". Ce modèle est appliqué aux réponses réflexes d'unités motrices isolées obtenues sur des sujets humains conscients. Les résultats expérimentaux, comparés à des données obtenues expérimentalement, montrent que le modèle découvre la fonctionnalité de voies neuronales humaines. Ce qui rend le modèle prometteur est le fait que c'est, à notre connaissance, le premier modèle réaliste capable d'auto-construire un réseau neuronal artificiel en combinant efficacement les neurosciences et des systèmes multi-agents adaptatifs. Bien qu'aucune preuve n'existe encore sur la correspondance exacte entre connectivité du modèle et connectivité du système humain, tout laisse à penser que ce modèle peut aider les neuroscientifiques à améliorer leur compréhension des réseaux neuronaux humains et qu'il peut être utilisé pour établir des hypothèses afin de conduire de futures expérimentations.In this thesis, a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication is presented. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, the Adaptive Multi-Agent Systems (AMAS) theory in which large sets of cooperative software agents interacting locally give rise to collective behavior bottom-up is used. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model uncovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with self-adaptive multi-agent systems. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments

Effects of Topical Cova™, Tisseel® and Adcon®Gel application on the development of spinal peridural fibrosis: An experimental study in rats

AIm: Leptomeningeal adhesions and fibrosis in the spinal peridural space are the most common causes of post-laminectomy syndrome. Fibrin sealant agents and membrane barriers are commonly used for hemostasis and sealing purposes in spinal surgery. Peridural fibrosis may be a risk of the usage of these topical agents. In this study, we aimed to compare the effects of Cova™, Tisseel® and Adcon®Gel on the development of spinal peridural fibrosis in the experimental rat model. Mater Ial and Methods: Thirty-two Sprague Dawley female rats were randomly divided into 4 groups. Groups were constituted as group 1; Cova™ group (laminectomy+CovaTM), group 2; Tisseel® group (laminectomy+Tisseel®), group 3; Adcon®Gel group (laminectomy + Adcon®Gel), group 4; control group (laminectomy only). Six weeks after laminectomy, spinal columns were removed en bloc between L1 and L4 vertebrae. Peridural fibrosis was evaluated histologically and the results were compared statistically. Results: Statistically significant reduction of peridural fibrosis was achieved in groups 1, 2, and 3 when compared with the control group (p<0.05). Our data revealed a statistically significant difference between group 1 and group 3 (p<0.05). When we compared with group 2 and 3, the fibrosis grades were not different between these two groups (p>0.05). ConclusIon: Fibrin sealant agent Tisseel® and membrane barrier Cova™ do not enhance peridural fibrosis following laminectomy. Cova™ and Tisseel® may be appropriate for hemostasis and leakage prevention during the spinal surgery and it is safe to leave these materials on the operation surface

Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity

International audienceWe present a novel computational model that detects temporal configurations of a given human neuronal pathway and constructs its artificial replication. This poses a great challenge since direct recordings from individual neurons are impossible in the human central nervous system and therefore the underlying neuronal pathway has to be considered as a black box. For tackling this challenge, we used a branch of complex systems modeling called artificial self-organization in which large sets of software entities interacting locally give rise to bottom-up collective behaviors. The result is an emergent model where each software entity represents an integrate-and-fire neuron. We then applied the model to the reflex responses of single motor units obtained from conscious human subjects. Experimental results show that the model recovers functionality of real human neuronal pathways by comparing it to appropriate surrogate data. What makes the model promising is the fact that, to the best of our knowledge, it is the first realistic model to self-wire an artificial neuronal network by efficiently combining neuroscience with artificial self-organization. Although there is no evidence yet of the model's connectivity mapping onto the human connectivity, we anticipate this model will help neuroscientists to learn much more about human neuronal networks, and could also be used for predicting hypotheses to lead future experiments

Sur l’utilisation de la modélisation et de la simulation basées agents pour étudier les systèmes de chaînes de blocs

International audienceBitcoin is the core of decentralized cryptocurrency systems. The underlying data structure of Bitcoin is called the blockchain in which transactions of digital coins between accounts are batched in so-called blocks, where each block is appended to the last one in a cryptographic way to make the malicious/accidental change of blocks content very hard. Participants following this protocol can create together a distributed, economical, social and technical system where anyone can join/leave and perform transactions in-between without neither needing to trust each other nor having a trusted third party. It is a very attractive technology since it maintains a public, immutable and ordered log of transactions which guarantees an auditable ledger accessible by anyone