Mean Field description of and propagation of chaos in recurrent multipopulation networks of Hodgkin-Huxley and Fitzhugh-Nagumo neurons

We derive the mean-field equations arising as the limit of a network of interacting spiking neurons, as the number of neurons goes to infinity. The neurons belong to a fixed number of populations and are represented either by the Hodgkin-Huxley model or by one of its simplified version, the Fitzhugh-Nagumo model. The synapses between neurons are either electrical or chemical. The network is assumed to be fully connected. The maximum conductances vary randomly. Under the condition that all neurons initial conditions are drawn independently from the same law that depends only on the population they belong to, we prove that a propagation of chaos phenomenon takes places, namely that in the mean-field limit, any finite number of neurons become independent and, within each population, have the same probability distribution. This probability distribution is solution of a set of implicit equations, either nonlinear stochastic differential equations resembling the McKean-Vlasov equations, or non-local partial differential equations resembling the McKean-Vlasov-Fokker- Planck equations. We prove the well-posedness of these equations, i.e. the existence and uniqueness of a solution. We also show the results of some preliminary numerical experiments that indicate that the mean-field equations are a good representation of the mean activity of a finite size network, even for modest sizes. These experiment also indicate that the McKean-Vlasov-Fokker- Planck equations may be a good way to understand the mean-field dynamics through, e.g., a bifurcation analysis.Comment: 55 pages, 9 figure

Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain

Deep brain stimulation (DBS) has been successfully applied in various neurodegenerative diseases as an effective symptomatic treatment. However, its mechanisms of action within the brain network are still poorly understood. Many virtual DBS models analyze a subnetwork around the basal ganglia and its dynamics as a spiking network with their details validated by experimental data. However, connectomic evidence shows widespread effects of DBS affecting many different cortical and subcortical areas. From a clinical perspective, various effects of DBS besides the motoric impact have been demonstrated. The neuroinformatics platform The Virtual Brain (TVB) offers a modeling framework allowing us to virtually perform stimulation, including DBS, and forecast the outcome from a dynamic systems perspective prior to invasive surgery with DBS lead placement. For an accurate prediction of the effects of DBS, we implement a detailed spiking model of the basal ganglia, which we combine with TVB via our previously developed co-simulation environment. This multiscale co-simulation approach builds on the extensive previous literature of spiking models of the basal ganglia while simultaneously offering a whole-brain perspective on widespread effects of the stimulation going beyond the motor circuit. In the first demonstration of our model, we show that virtual DBS can move the firing rates of a Parkinson's disease patient's thalamus - basal ganglia network towards the healthy regime while, at the same time, altering the activity in distributed cortical regions with a pronounced effect in frontal regions. Thus, we provide proof of concept for virtual DBS in a co-simulation environment with TVB. The developed modeling approach has the potential to optimize DBS lead placement and configuration and forecast the success of DBS treatment for individual patients

Differences in Expression of IQSEC2 Transcript Isoforms in Male and Female Cases with Loss of Function Variants and Neurodevelopmental Disorder

Pathogenic hemizygous or heterozygous mutations in the IQSEC2 gene cause X-linked intellectual developmental disorder-1 (XLID1), characterized by a variable phenotype including developmental delay, intellectual disability, epilepsy, hypotonia, autism, microcephaly and stereotypies. It affects both males and females typically through loss of function in males and haploinsufficiency in heterozygous females. Females are generally less affected than males. Two novel unrelated cases, one male and one female, with de novo IQSEC2 variants were detected by trio-based whole exome sequencing. The female case had a previously undescribed frameshift mutation (NM_001111125:c.3300dup; p.Met1101Tyrfs*5), and the male showed an intronic variant in intron 6, with a previously unknown effect (NM_001111125:c.2459+21C>T). IQSEC2 gene expression study revealed that this intronic variant created an alternative donor splicing site and an aberrant product, with the inclusion of 19bp, confirming the pathogenic effect of the intron variant. Moreover, a strong reduction in the expression of the long, but also the short IQSEC2 isoforms, was detected in the male correlating with a more severe phenotype, while the female case showed no decreased expression of the short isoform, and milder effects of the disease. This suggests that the abnormal expression levels of the different IQSEC2 transcripts could be implicated in the severity of disease manifestations.This research was funded by INSTITUTO DE SALUD CARLOS III, institutional project Spain UDP and grant PT20CIII/00009.S

Explorer les codes neuronaux utilisant des machines parallèles

L'objectif de cette thèse est de comprendre la dynamique des grandes populations de neurones interconnectées. La méthode utilisée pour atteindre cet objectif est un mélange de modèles mésoscopiques et calculs de haute performance. Le premier permet de réduire la complexité du réseau neuronale et le second de réaliser des simulations à grandes échelles. Dans la première partie de cette thèse une nouvelle approche du champ moyen est utilisée pour étudier numériquement les effets du bruit sur un groupe extrêmement grand de neurones. La même approche a été utilisée pour créer un modèle d' hypercolonne du premier cortex visuel d'où l'unité basique, est des grandes populations de neurones au lieu d'une seule cellule. Les simulations sont réalisées en résolvant un système d'équation différentielle partielle qui décrit l'évolution de la fonction de densité de probabilité du réseau. Dans la deuxième partie de cette thèse est présentée une étude numérique de deux modèles de champs neuronaux du premier cortex visuel. Le principal objectif est de déterminer comment les contours sont sélectionnés dans le cortex visuel. La différence entre les deux modèles est la manière de représenter des préférences d'orientations des neurones. Pour l'un des modèles, l'orientation est une caractéristique de l'équation et la connectivité dépend d'elle. Dans l'autre, il existe une carte d'orientation qui définit une fonction d'entrée. Toutes les simulations sont réalisées sur un cluster de processeurs graphiques. Cette thèse propose des techniques pour simuler rapidement les modèles proposés sur ce type de machine. La vitesse atteinte est équivalente à un cluster standard très grand.The aim of this thesis is to understand the dynamics of large interconnected populations of neurons. The method we use to reach this objective is a mixture of mesoscopic modeling and high performance computing. The rst allows us to reduce the complexity of the network and the second to perform large scale simulations. In the rst part of this thesis a new mean eld approach for conductance based neurons is used to study numerically the eects of noise on extremely large ensembles of neurons. Also, the same approach is used to create a model of one hypercolumn from the primary visual cortex where the basic computational units are large populations of neurons instead of simple cells. All of these simulations are done by solving a set of partial dierential equations that describe the evolution of the probability density function of the network. In the second part of this thesis a numerical study of two neural eld models of the primary visual cortex is presented. The main focus in both cases is to determine how edge selection and continuation can be computed in the primary visual cortex. The dierence between the two models is in how they represent the orientation preference of neurons, in one this is a feature of the equations and the connectivity depends on it, while in the other there is an underlying map which denes an input function. All the simulations are performed on a Graphic Processing Unit cluster. Thethesis proposes a set of techniques to simulate the models fast enough on this kind of hardware. The speedup obtained is equivalent to that of a huge standard cluster

Explorer les codes neuronaux utilisant des machines parallèles

The aim of this thesis is to understand the dynamics of large interconnected populations of neurons. The method we use to reach this objective is a mixture of mesoscopic modeling and high performance computing. The rst allows us to reduce the complexity of the network and the second to perform large scale simulations. In the rst part of this thesis a new mean eld approach for conductance based neurons is used to study numerically the eects of noise on extremely large ensembles of neurons. Also, the same approach is used to create a model of one hypercolumn from the primary visual cortex where the basic computational units are large populations of neurons instead of simple cells. All of these simulations are done by solving a set of partial dierential equations that describe the evolution of the probability density function of the network. In the second part of this thesis a numerical study of two neural eld models of the primary visual cortex is presented. The main focus in both cases is to determine how edge selection and continuation can be computed in the primary visual cortex. The dierence between the two models is in how they represent the orientation preference of neurons, in one this is a feature of the equations and the connectivity depends on it, while in the other there is an underlying map which denes an input function. All the simulations are performed on a Graphic Processing Unit cluster. Thethesis proposes a set of techniques to simulate the models fast enough on this kind of hardware. The speedup obtained is equivalent to that of a huge standard cluster.L'objectif de cette thèse est de comprendre la dynamique des grandes populations de neurones interconnectées. La méthode utilisée pour atteindre cet objectif est un mélange de modèles mésoscopiques et calculs de haute performance. Le premier permet de réduire la complexité du réseau neuronale et le second de réaliser des simulations à grandes échelles. Dans la première partie de cette thèse une nouvelle approche du champ moyen est utilisée pour étudier numériquement les effets du bruit sur un groupe extrêmement grand de neurones. La même approche a été utilisée pour créer un modèle d' hypercolonne du premier cortex visuel d'où l'unité basique, est des grandes populations de neurones au lieu d'une seule cellule. Les simulations sont réalisées en résolvant un système d'équation différentielle partielle qui décrit l'évolution de la fonction de densité de probabilité du réseau. Dans la deuxième partie de cette thèse est présentée une étude numérique de deux modèles de champs neuronaux du premier cortex visuel. Le principal objectif est de déterminer comment les contours sont sélectionnés dans le cortex visuel. La différence entre les deux modèles est la manière de représenter des préférences d'orientations des neurones. Pour l'un des modèles, l'orientation est une caractéristique de l'équation et la connectivité dépend d'elle. Dans l'autre, il existe une carte d'orientation qui définit une fonction d'entrée. Toutes les simulations sont réalisées sur un cluster de processeurs graphiques. Cette thèse propose des techniques pour simuler rapidement les modèles proposés sur ce type de machine. La vitesse atteinte est équivalente à un cluster standard très grand

Explorer les codes neuronaux utilisant des machines parallèles

L'objectif de cette thèse est de comprendre la dynamique des grandes populations de neurones interconnectées. La méthode utilisée pour atteindre cet objectif est un mélange de modèles mésoscopiques et calculs de haute performance. Le premier permet de réduire la complexité du réseau neuronale et le second de réaliser des simulations à grandes échelles. Dans la première partie de cette thèse une nouvelle approche du champ moyen est utilisée pour étudier numériquement les effets du bruit sur un groupe extrêmement grand de neurones. La même approche a été utilisée pour créer un modèle d' hypercolonne du premier cortex visuel d'où l'unité basique, est des grandes populations de neurones au lieu d'une seule cellule. Les simulations sont réalisées en résolvant un système d'équation différentielle partielle qui décrit l'évolution de la fonction de densité de probabilité du réseau. Dans la deuxième partie de cette thèse est présentée une étude numérique de deux modèles de champs neuronaux du premier cortex visuel. Le principal objectif est de déterminer comment les contours sont sélectionnés dans le cortex visuel. La différence entre les deux modèles est la manière de représenter des préférences d'orientations des neurones. Pour l'un des modèles, l'orientation est une caractéristique de l'équation et la connectivité dépend d'elle. Dans l'autre, il existe une carte d'orientation qui définit une fonction d'entrée. Toutes les simulations sont réalisées sur un cluster de processeurs graphiques. Cette thèse propose des techniques pour simuler rapidement les modèles proposés sur ce type de machine. La vitesse atteinte est équivalente à un cluster standard très grand.The aim of this thesis is to understand the dynamics of large interconnected populations of neurons. The method we use to reach this objective is a mixture of mesoscopic modeling and high performance computing. The rst allows us to reduce the complexity of the network and the second to perform large scale simulations. In the rst part of this thesis a new mean eld approach for conductance based neurons is used to study numerically the eects of noise on extremely large ensembles of neurons. Also, the same approach is used to create a model of one hypercolumn from the primary visual cortex where the basic computational units are large populations of neurons instead of simple cells. All of these simulations are done by solving a set of partial dierential equations that describe the evolution of the probability density function of the network. In the second part of this thesis a numerical study of two neural eld models of the primary visual cortex is presented. The main focus in both cases is to determine how edge selection and continuation can be computed in the primary visual cortex. The dierence between the two models is in how they represent the orientation preference of neurons, in one this is a feature of the equations and the connectivity depends on it, while in the other there is an underlying map which denes an input function. All the simulations are performed on a Graphic Processing Unit cluster. Thethesis proposes a set of techniques to simulate the models fast enough on this kind of hardware. The speedup obtained is equivalent to that of a huge standard cluster.NICE-Bibliotheque electronique (060889901) / SudocSudocFranceF

Three applications of GPU computing in neuroscience

International audienceThree scenarios outlined here show the benefits of using a computer system with multiple GPUs in theoretical neuroscience. In each instance, it's clear that the GPU speedup considerably helps answer a scientific or technological question

Mean-field description and propagation of chaos in networks of Hodgkin-Huxley and FitzHugh-Nagumo neurons.

International audienceABSTRACT: We derive the mean-field equations arising as the limit of a network of interacting spiking neurons, as the number of neurons goes to infinity. The neurons belong to a fixed number of populations and are represented either by the Hodgkin-Huxley model or by one of its simplified version, the FitzHugh-Nagumo model. The synapses between neurons are either electrical or chemical. The network is assumed to be fully connected. The maximum conductances vary randomly. Under the condition that all neurons' initial conditions are drawn independently from the same law that depends only on the population they belong to, we prove that a propagation of chaos phenomenon takes place, namely that in the mean-field limit, any finite number of neurons become independent and, within each population, have the same probability distribution. This probability distribution is a solution of a set of implicit equations, either nonlinear stochastic differential equations resembling the McKean-Vlasov equations or non-local partial differential equations resembling the McKean-Vlasov-Fokker-Planck equations. We prove the wellposedness of the McKean-Vlasov equations, i.e. the existence and uniqueness of a solution. We also show the results of some numerical experiments that indicate that the mean-field equations are a good representation of the mean activity of a finite size network, even for modest sizes. These experiments also indicate that the McKean-Vlasov-Fokker-Planck equations may be a good way to understand the mean-field dynamics through, e.g. a bifurcation analysis

Exploration behavior after reversals is predicted by STN-GPe synaptic plasticity in a basal ganglia model

Summary: Humans can quickly adapt their behavior to changes in the environment. Classical reversal learning tasks mainly measure how well participants can disengage from a previously successful behavior but not how alternative responses are explored. Here, we propose a novel 5-choice reversal learning task with alternating position-reward contingencies to study exploration behavior after a reversal. We compare human exploratory saccade behavior with a prediction obtained from a neuro-computational model of the basal ganglia. A new synaptic plasticity rule for learning the connectivity between the subthalamic nucleus (STN) and external globus pallidus (GPe) results in exploration biases to previously rewarded positions. The model simulations and human data both show that during experimental experience exploration becomes limited to only those positions that have been rewarded in the past. Our study demonstrates how quite complex behavior may result from a simple sub-circuit within the basal ganglia pathways