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

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

In-silico Models of Stem Cell and Developmental Systems

Understanding how developmental systems evolve over time is a key question in stem cell and developmental biology research. However, due to hurdles of existing experimental techniques, our understanding of these systems as a whole remains partial and coarse. In recent years, we have been constructing in-silico models that synthesize experimental knowledge using software engineering tools. Our approach integrates known isolated mechanisms with simplified assumptions where the knowledge is limited. This has proven to be a powerful, yet underutilized, tool to analyze the developmental process. The models provide a means to study development in-silico by altering the model’s specifications, and thereby predict unforeseen phenomena to guide future experimental trials. To date, three organs from diverse evolutionary organisms have been modeled: the mouse pancreas, the C. elegans gonad, and partial rodent brain development. Analysis and execution of the models recapitulated the development of the organs, anticipated known experimental results and gave rise to novel testable predictions. Some of these results had already been validated experimentally. In this paper, I review our efforts in realistic in-silico modeling of stem cell research and developmental biology and discuss achievements and challenges. I envision that in the future, in-silico models as presented in this paper would become a common and useful technique for research in developmental biology and related research fields, particularly regenerative medicine, tissue engineering and cancer therapeutics

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Gut microbiota-motility interregulation:Insights from in vivo, ex vivo and in silico studies

The human gastrointestinal tract is home to trillions of microbes. Gut microbial communities have a significant regulatory role in the intestinal physiology, such as gut motility. Microbial effect on gut motility is often evoked by bioactive molecules from various sources, including microbial break down of carbohydrates, fibers or proteins. In turn, gut motility regulates the colonization within the microbial ecosystem. However, the underlying mechanisms of such regulation remain obscure. Deciphering the inter-regulatory mechanisms of the microbiota and bowel function is crucial for the prevention and treatment of gut dysmotility, a comorbidity associated with many diseases. In this review, we present an overview of the current knowledge on the impact of gut microbiota and its products on bowel motility. We discuss the currently available techniques employed to assess the changes in the intestinal motility. Further, we highlight the open challenges, and incorporate biophysical elements of microbes-motility interplay, in an attempt to lay the foundation for describing long-term impacts of microbial metabolite-induced changes in gut motility

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud \u

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

Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering.

Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system

PACAP is a pathogen-inducible resident antimicrobial neuropeptide affording rapid and contextual molecular host defense of the brain

Defense of the central nervous system (CNS) against infection must be accomplished without generation of potentially injurious immune cell-mediated or off-target inflammation which could impair key functions. As the CNS is an immune-privileged compartment, inducible innate defense mechanisms endogenous to the CNS likely play an essential role in this regard. Pituitary adenylate cyclase-activating polypeptide (PACAP) is a neuropeptide known to regulate neurodevelopment, emotion, and certain stress responses. While PACAP is known to interact with the immune system, its significance in direct defense of brain or other tissues is not established. Here, we show that our machine-learning classifier can screen for immune activity in neuropeptides, and correctly identified PACAP as an antimicrobial neuropeptide in agreement with previous experimental work. Furthermore, synchrotron X-ray scattering, antimicrobial assays, and mechanistic fingerprinting provided precise insights into how PACAP exerts antimicrobial activities vs. pathogens via multiple and synergistic mechanisms, including dysregulation of membrane integrity and energetics and activation of cell death pathways. Importantly, resident PACAP is selectively induced up to 50-fold in the brain in mouse models of Staphylococcus aureus or Candida albicans infection in vivo, without inducing immune cell infiltration. We show differential PACAP induction even in various tissues outside the CNS, and how these observed patterns of induction are consistent with the antimicrobial efficacy of PACAP measured in conditions simulating specific physiologic contexts of those tissues. Phylogenetic analysis of PACAP revealed close conservation of predicted antimicrobial properties spanning primitive invertebrates to modern mammals. Together, these findings substantiate our hypothesis that PACAP is an ancient neuro-endocrine-immune effector that defends the CNS against infection while minimizing potentially injurious neuroinflammation