Journal Article

FZJ-2016-02710

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity

Diaz, S. (Corresponding author)FZJ* ; Naveau, M. ; Butz-Ostendorf, M. ; Morrison, A.FZJ*

2016
Frontiers Research Foundation Lausanne

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Please use a persistent id in citations: http://hdl.handle.net/2128/11281  doi:10.3389/fnana.2016.00057

Abstract: 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.

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Contributing Institute(s):

1. Institute for Advanced Simulation (IAS)
2. Jülich Supercomputing Center (JSC)
3. Computational and Systems Neuroscience (INM-6)
4. JARA - HPC (JARA-HPC)

Research Program(s):

1. 574 - Theory, modelling and simulation (POF3-574) (POF3-574)
2. 511 - Computational Science and Mathematical Methods (POF3-511) (POF3-511)
3. SMHB - Supercomputing and Modelling for the Human Brain (HGF-SMHB-2013-2017) (HGF-SMHB-2013-2017)
4. W2Morrison - W2/W3 Professorinnen Programm der Helmholtzgemeinschaft (B1175.01.12) (B1175.01.12)
5. SLNS - SimLab Neuroscience (Helmholtz-SLNS) (Helmholtz-SLNS)

Appears in the scientific report 2016

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Database coverage:
; ; ; ; BIOSIS Previews ; IF < 5 ; JCR ; NCBI Molecular Biology Database ; SCOPUS ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

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