Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.
With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.
Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center Jülich, Germany.
Sandra
S
Diaz-Pier
Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research CenterJülich, Germany; Serine Proteases and Pathophysiology of the Neurovascular Unit, Institut National de la Santé et de la Recherche Médicale UMR-S U919, Caen Normandy University, Groupement d'Intérêt Public (GIP) CYCERONCaen, France.
Mikaël
M
Naveau
Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center Jülich, Germany.
Markus
M
Butz-Ostendorf
Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research CenterJülich, Germany; Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Jülich Research CentreJülich, Germany; Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University BochumBochum, Germany.
Abigail
A
Morrison
high performance computing; homeostatic growth; large scale neural networks; self-organizing network; structural plasticity