This title appears in the Scientific Report :
2014
A spiking multi-area network model of macaque visual cortex
A spiking multi-area network model of macaque visual cortex
The primate visual cortex consists of a set of specialized areas whose inter-connections have been shown to influence its dynamics both in spontaneous and driven conditions. Hitherto, models of this system have either concentrated on local detailed circuits or studied the interplay of areas, each re...
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Personal Name(s): | Schmidt, Maximilian (Corresponding Author) |
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van Albada, Sacha / Bakker, Rembrandt / Diesmann, Markus | |
Contributing Institute: |
Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Published in: | 2013 |
Imprint: |
2013
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Conference: | Osaka (Japan), 2014-07-02 - 2014-07-02 |
Document Type: |
Talk (non-conference) |
Research Program: |
Theory, modelling and simulation Brain-Scale Simulations Helmholtz Alliance on Systems Biology Brain-inspired multiscale computation in neuromorphic hybrid systems Signalling Pathways and Mechanisms in the Nervous System |
Publikationsportal JuSER |
The primate visual cortex consists of a set of specialized areas whose inter-connections have been shown to influence its dynamics both in spontaneous and driven conditions. Hitherto, models of this system have either concentrated on local detailed circuits or studied the interplay of areas, each represented by a few dynamical equations. We present a model which bridges this gap between microscopic and macroscopic dynamics by extending a spiking model of a 1mm2 patch of early sensory cortex (Potjans T & Diesmann M, Cereb Cortex 2014) to all vision-related areas of the macaque cortex. The single-cell dynamics is kept simple in order to bring out the influence of the complex connectivity, which is based on a systematic synthesis of anatomical and electrophysiological findings. The extension to multiple areas allows us to replace random inputs to the network in part by simulated synapses, thereby increasing the self-consistency of the model. Here the immediate aim is not to address network function from a top-down perspective but to explore the relationship between network structure and fundamental multi-scale activity states.The talk will start with a general introduction to the neuron model and the concept of the balanced random network, which is being used in the microcircuit and the multi-area model. Afterwards, the microcircuit model as the basis of the multi-area network is introduced and its key insights are discussed. In the main part, I will explain the definition of the multi-area model and its key ingredients from anatomical studies and present results from the dynamical simulations using NEST. |