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

A novel optimization methodology of modular wiring harnesses in modern vehicles : weight reduction and safe operation

The weight of electric and electronic components of cars has been uninterruptedly increasing through the last decades, and thus the weight of their wiring harnesses. This fact has awakened the interest of car manufacturers on the weight and cost optimization of automotive wiring harnesses . For this reason, this dissertation discusses and develops approaches to reduce the amount of copper for the purpose of current conduction, i.e. the cross-sections of all of the wires of the car, without endangering safety. On the one hand, harnesses must withstand continuous operation currents. On account of this, it is necessary to know the characteristic flow of current of the in-vehicle electrical network. Nevertheless. the huge quantity of available combinations of equipment of the car produces a proportional variety of customer-specific wiring hamesses, and makes it unfeasible to simulate all of them. This thesis points attention on specific segments of the wiring harnesses. Sorne of them can have many possible compositions, which are related to the customer's car settings. Since computation time is a limiting factor here, it is proposed to predict the bundle heating behaviors by means of response surfaces, obtained from a set of finite element simulation results and the least squares method. On the other hand, the correct wire sizes must ensure that they are protected by their associated melting fuses, so that their maximum acceptable temperature is not exceeded after short circuits. Since many wires in cars are connected to other wires with splices, or may suffer short-circuits in their electric loads, these short-circuits can flow across different wires. In modular wiring harnesses, each of the wires can have different lengths and different installation ratios, their cross-section affects the cost of the wire harness with different importance, as well as the short circuit and the final temperature of the wire. The finite volume method is used to simulate the short circuit of series-connected wires. Finally, non-linear optimization is used to find the mínimum cross­ sections of wires respecting the constraints of maximum temperature and mínimum short-circuit current. Finally, these two different criteria for optima! wire dimensioning are combined in the analysis of the on-board network of the vehicle in order to make a complete weight and cost minimization of the cable harnesses in a particular vehicle, considering also its modularity of loads.El pes dels components elèctrics i electrònics deis automòbils ha crescut ininterrompudament al llarg de les darreres dècades, i conseqüentment ho han fet també els seus feixos de cables. Aquest fet ha despertat entre els fabricants de turismes un elevat interès en la minimització del pes i dels costos del cablejat del vehicle. Per aquest motiu, aquesta tesi desenvolupa mètodes per reduir la quantitat de coure destinat a la conducció de corrent, és a dir, les seccions de tots els fils elèctrics dins el cotxe, sense posar en risc la seguretat. Per una banda, els feixos han de resistir els corrents d'operació continuada. Per a aquest propòsit, cal conèixer el flux de corrents característic de la xarxa de bord del vehicle. No obstant, la immensa quantitat de combinacions de diferents equipaments del vehicle produeix proporcionalment una enorme varietat de feixos personalitzats per als clients, fet que fa inviable simular totes aquestes combinacions . El primer dels mètodes d'optimització que es proposen en aquesta tesi estudia segments dels feixos de cables per separat un a un. Alguns d'ells poden tenir diferents composicions de fils en funció de la configuració aplicada pel client. Com que el temps de calcul és un factor limitant, es proposa predir el comportament tèrmic dels segments per mitja de superfícies resposta, que s'obtenen a través del mètode deis mínims quadrats i un conjunt de resultats de simulació de feixos pel mètode dels elements finits. Per altra banda, les correctes seccions dels fils han de ser tals que els curtcircuits i les sobrecarregues no puguin malmetre'ls, gracies a la correcta coordinació amb els fusibles destinats a protegir-los. Atès que molts fils estan connectats amb altres fils per mitja d'unions soldades i que molts curtcircuits són provocats directament en bornes de les carregues elèctriques, els curtcircuits poden fluir a través de fils diferenciats connectats en serie. Als feixos modulars, cadascun deis fils té diferents longituds i ratis d'instal·lació. És per aquest darrer motiu que llur secció afecta de diferent manera al cost total del conjunt deis feixos de cables deis cotxes venuts . De la mateixa manera, les seves longituds diferents fan que les variacions en les seccions alterin els curtcircuits resultants amb diferent sensibilitat. És per això que es fa servir optimització no lineal per trobar les seccions separades de cadascun dels fils connectats en serie a través dels quals poden passar curtcircuits. Per a aquesta fi es fan simulacions en volums finits i models energètics dels fusibles integrades dins de l'optimització no lineal. Finalment, aquestes dues vies de dimensionament es combinen dins una anàlisi íntegra de la xarxa de bord per dimensionar de forma òptima cadascun dels fils del vehicle, tenint en compte les interconnexions entre feixos i totes les combinacions d'equipament

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