Perspectives on the modeling of the neuromusculoskeletal system to investigate the influence of neurodegenerative diseases on sensorimotor control

The understanding of the neurophysiological mechanisms underlying movement control can be much furthered using computational models of the neuromusculoskeletal system. Biologically based multi-scale neuromusculoskeletal models have a great potential to provide new theories and explanations related to mechanisms behind muscle force generation at the molecular, cellular, synaptic, and systems levels. Albeit some efforts have been made to investigate how neurodegenerative diseases alter the dynamics of individual elements of the neuromuscular system, such diseases have not been analyzed from a systems viewpoint using multi-scale models. This perspective article synthesizes what has been done in terms of multi-scale neuromuscular development and points to a few directions where such models could be extended so that they can be useful in the future to discover early predictors of neurodegenerative diseases, as well as to propose new quantitative clinical neurophysiology approaches to follow the course of improvements associated with different therapies (drugs or others). Therefore, this article will present how existing biologically based multi-scale models of the neuromusculoskeletal system could be expanded and adapted for clinical applications. It will point to mechanisms operating at different levels that would be relevant to be considered during model development, along with implications for interpreting experimental results from neurological patients342176186CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP409302/2016-3; 312442/2017-3; 303809/2016-72015/21819-32015/14917-9; 2017/11464-

Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks

Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened understanding of the brain, such as the inspiration of neocognitron, which is the pioneering work of convolutional neural networks. Per the motives of the emerging field: NeuroAI, a great amount of neuroscience knowledge can help catalyze the next generation of AI by endowing a network with more powerful capabilities. As we know, the human brain has numerous morphologically and functionally different neurons, while artificial neural networks are almost exclusively built on a single neuron type. In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. Since an artificial network is a miniature of the human brain, introducing neuronal diversity should be valuable in terms of addressing those essential problems of artificial networks such as efficiency, interpretability, and memory. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron. Then, we review studies of designing new neurons for artificial networks. Next, we discuss what gains can neuronal diversity bring into artificial networks and exemplary applications in several important fields. Lastly, we discuss the challenges and future directions of neuronal diversity to explore the potential of NeuroAI

Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit

The striatum, the principal input structure of the basal ganglia, is crucial to both motor control and learning. It receives convergent input from all over the neocortex, hippocampal formation, amygdala and thalamus, and is the primary recipient of dopamine in the brain. Within the striatum is a GABAergic microcircuit that acts upon these inputs, formed by the dominant medium-spiny projection neurons (MSNs) and fast-spiking interneurons (FSIs). There has been little progress in understanding the computations it performs, hampered by the non-laminar structure that prevents identification of a repeating canonical microcircuit. We here begin the identification of potential dynamically-defined computational elements within the striatum. We construct a new three-dimensional model of the striatal microcircuit's connectivity, and instantiate this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs is introduced and tuned to experimental data. We introduce a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We find that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appear, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation is strongly dependent on the simulated concentration of dopamine. We also show that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs. Such small cell assemblies forming spontaneously only in the absence of dopamine may contribute to motor control problems seen in humans and animals following a loss of dopamine cells. (C) 2009 Elsevier Ltd. All rights reserved

Influence of the dentritic morphology on electrophysiological responses of thalamocortical neurons

Les neurones thalamiques de relai ont un rôle exclusif dans la transformation et de transfert de presque toute l'information sensorielle dans le cortex. L'intégration synaptique et la réponse électrophysiologique des neurones thalamiques de relai sont déterminées non seulement par l’état du réseau impliqué, mais ils sont également contrôlés par leurs propriétés intrinsèques tels les divers canaux ioniques voltage-dépendants ainsi que l’arborisation dendritique élaboré. Par conséquent, investiguer sur le profil complexe de morphologie dendritique et sur les propriétés dendritiques actives révèle des renseignements importants sur la fonction d'entrée-sortie de neurones thalamiques de relai. Dans cette étude, nous avons reconstruit huit neurones thalamocorticaux (TC) du noyau VPL de chat adulte. En se basant sur ces données morphologiques complètes, nous avons développé plusieurs modèles multicompartimentaux afin de trouver un rôle potentiellement important des arbres dendritiques des neurones de TC dans l'intégration synaptique et l’intégration neuronale. L'analyse des caractéristiques morphologiques des neurones TC accordent des valeurs précises à des paramètres géométriques semblables ou différents de ceux publiés antérieurement. En outre, cette analyse fait ressortir de tous nouveaux renseignements concernant le patron de connectivité entre les sections dendritiques telles que l'index de l'asymétrie et la longueur de parcours moyen (c'est-à-dire, les paramètres topologiques). Nous avons confirmé l’étendue des valeurs rapportée antérieurement pour plusieurs paramètres géométriques tels que la zone somatique (2956.24±918.89 m2), la longueur dendritique totale (168017.49±4364.64 m) et le nombre de sous-arbres (8.3±1.5) pour huit neurones TC. Cependant, contrairement aux données rapportées antérieurement, le patron de ramification dendritique (avec des cas de bifurcation 98 %) ne suit pas la règle de puissance de Rall 3/2 pour le ratio géométrique (GR), et la valeur moyenne de GR pour un signal de propagation est 2,5 fois plus grande que pour un signal rétropropagé. Nous avons également démontré une variabilité significative dans l'index de symétrie entre les sous-arbres de neurones TC, mais la longueur du parcours moyen n'a pas montré une grande variation à travers les ramifications dendritiques des différents neurones. Nous avons examiné la conséquence d’une distribution non-uniforme des canaux T le long de l'arbre dendritique sur la réponse électrophysiologique émergeante, soit le potentiel Ca 2+ à seuil bas (low-threshold calcium spike, LTS) des neurones TC. En appliquant l'hypothèse du «coût minimal métabolique», nous avons constaté que le neurone modélisé nécessite un nombre minimal de canaux-T pour générer un LTS, lorsque les canaux-T sont situés dans les dendrites proximales. Dans la prochaine étude, notre modèle informatique a illustré l'étendue d'une rétropropagation du potentiel d'action et de l'efficacité de la propagation vers des PPSEs générés aux branches dendritiques distales. Nous avons démontré que la propagation dendritique des signaux électriques est fortement contrôlée par les paramètres morphologiques comme illustré par les différents paliers de polarisation obtenus par un neurone à équidistance de soma pendant la propagation et la rétropropagation des signaux électriques. Nos résultats ont révélé que les propriétés géométriques (c.-à-d. diamètre, GR) ont un impact plus fort sur la propagation du signal électrique que les propriétés topologiques. Nous concluons que (1) la diversité dans les propriétés morphologiques entre les sous-arbres d'un seul neurone TC donne une capacité spécifique pour l'intégration synaptique et l’intégration neuronale des différents dendrites, (2) le paramètre géométrique d'un arbre dendritique fournissent une influence plus élevée sur le contrôle de l'efficacité synaptique et l'étendue du potentiel d'action rétropropagé que les propriétés topologiques, (3) neurones TC suivent le principe d’optimisation pour la distribution de la conductance voltage-dépendant sur les arbres dendritiques.Thalamic relay neurons have an exclusive role in processing and transferring nearly all sensory information into the cortex. The synaptic integration and the electrophysiological response of thalamic relay neurons are determined not only by a state of the involved network, but they are also controlled by their intrinsic properties; such as diverse voltage-dependent ionic channels as well as by elaborated dendritic arborization. Therefore, investigating the complex pattern of dendritic morphology and dendritic active properties reveals important information on the input-output function of thalamic relay neurons. In this study, we reconstructed eight thalamocortical (TC) neurons from the VPL nucleus of adult cats. Based on these complete morphological data, we developed several multi-compartment models in order to find a potentially important role for dendritic trees of TC neurons in the synaptic integration and neuronal computation. The analysis of morphological features of TC neurons yield precise values of geometrical parameters either similar or different from those previously reported. In addition, this analysis extracted new information regarding the pattern of connectivity between dendritic sections such as asymmetry index and mean path length (i.e., topological parameters). We confirmed the same range of previously reported value for several geometric parameters such as the somatic area (2956.24±918.89 m2), the total dendritic length (168017.49±4364.64 m) and the number of subtrees (8.3±1.5) for eight TC neurons. However, contrary to previously reported data, the dendritic branching pattern (with 98% bifurcation cases) does not follow Rall’s 3/2 power rule for the geometrical ratio (GR), and the average GR value for a forward propagation signal was 2.5 times bigger than for a backward propagating signal. We also demonstrated a significant variability in the symmetry index between subtrees of TC neurons, but the mean path length did not show a large variation through the dendritic arborizations of different neurons. We examined the consequence of non-uniform distribution of T-channels along the dendritic tree on the prominent electrophysiological response, the low-threshold Ca2+ spike (LTS) of TC neurons. By applying the hypothesis of “minimizing metabolic cost”, we found that the modeled neuron needed a minimum number of T-channels to generate low-threshold Ca2+ spike (LTS), when T-channels were located in proximal dendrites. In the next study, our computational model illustrated the extent of an action potential back propagation and the efficacy of forward propagation of EPSPs arriving at the distal dendritic branches. We demonstrated that dendritic propagation of electrical signals is strongly controlled by morphological parameters as shown by different levels of polarization achieved by a neuron at equidistance from the soma during back and forward propagation of electrical signals. Our results revealed that geometrical properties (i.e. diameter, GR) have a stronger impact on the electrical signal propagation than topological properties. We conclude that (1) diversity in the morphological properties between subtrees of a single TC neuron lead to a specific ability for synaptic integration and neuronal computation of different dendrites, (2) geometrical parameter of a dendritic tree provide higher influence on the control of synaptic efficacy and the extent of the back propagating action potential than topological properties, (3) TC neurons follow the optimized principle for distribution of voltage-dependent conductance on dendritic trees

Computational Study of the Mechanisms Underlying Oscillation in Neuronal Locomotor Circuits

In this thesis we model two very different movement-related neuronal circuits, both of which produce oscillatory patterns of activity. In one case we study oscillatory activity in the basal ganglia under both normal and Parkinsonian conditions. First, we used a detailed Hodgkin-Huxley type spiking model to investigate the activity patterns that arise when oscillatory cortical input is transmitted to the globus pallidus via the subthalamic nucleus. Our model reproduced a result from rodent studies which shows that two anti-phase oscillatory groups of pallidal neurons appear under Parkinsonian conditions. Secondly, we used a population model of the basal ganglia to study whether oscillations could be locally generated. The basal ganglia are thought to be organised into multiple parallel channels. In our model, isolated channels could not generate oscillations, but if the lateral inhibition between channels is sufficiently strong then the network can act as a rhythm-generating ``pacemaker'' circuit. This was particularly true when we used a set of connection strength parameters that represent the basal ganglia under Parkinsonian conditions. Since many things are not known about the anatomy and electrophysiology of the basal ganglia, we also studied oscillatory activity in another, much simpler, movement-related neuronal system: the spinal cord of the Xenopus tadpole. We built a computational model of the spinal cord containing approximately 1,500 biologically realistic Hodgkin-Huxley neurons, with synaptic connectivity derived from a computational model of axon growth. The model produced physiological swimming behaviour and was used to investigate which aspects of axon growth and neuron dynamics are behaviourally important. We found that the oscillatory attractor associated with swimming was remarkably stable, which suggests that, surprisingly, many features of axonal growth and synapse formation are not necessary for swimming to emerge. We also studied how the same spinal cord network can generate a different oscillatory pattern in which neurons on both sides of the body fire synchronously. Our results here suggest that under normal conditions the synchronous state is unstable or weakly stable, but that even small increases in spike transmission delays act to stabilise it. Finally, we found that although the basal ganglia and the tadpole spinal cord are very different systems, the underlying mechanism by which they can produce oscillations may be remarkably similar. Insights from the tadpole model allow us to predict how the basal ganglia model may be capable of producing multiple patterns of oscillatory activity

Dynamics and Synchronization in Neuronal Models

La tesis está principalmente dedicada al modelado y simulación de sistemas neuronales. Entre otros aspectos se investiga el papel del ruido cuando actua sobre neuronas. El fenómeno de resonancia estocástica es caracterizado tanto a nivel teórico como reportado experimentalmente en un conjunto de neuronas del sistema motor. También se estudia el papel que juega la heterogeneidad en un conjunto de neuronas acopladas demostrando que la heterogeneidad en algunos parámetros de las neuronas puede mejorar la respuesta del sistema a una modulación periódica externa. También estudiamos del efecto de la topología y el retraso en las conexiones en una red neuronal. Se explora como las propiedades topológicas y los retrasos en la conducción de diferentes clases de redes afectan la capacidad de las neuronas para establecer una relación temporal bien definida mediante sus potenciales de acción. En particular, el concepto de consistencia se introduce y estudia en una red neuronal cuando plasticidad neuronal es tenida en cuenta entre las conexiones de la re

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

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)