This title appears in the Scientific Report :
2014
The origin of population rate oscillations in spiking neural networks
The origin of population rate oscillations in spiking neural networks
Fast oscillations of the population firing rate in the gamma range (50-200 Hz), where each individual neuron fires irregularly with a low rate, have been observed in networks of simulated leaky integrate-and-fire (LIF) neurons as well as in population signals in the living brain.An analytical explan...
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Personal Name(s): | Bos, Hannah (Corresponding Author) |
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Helias, Moritz | |
Contributing Institute: |
Computational and Systems Neuroscience; IAS-6 Computational and Systems Neuroscience; INM-6 |
Published in: | 2014 |
Imprint: |
2014
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Conference: | INM Retreat, Juelich (Germany), 2014-07-01 - 2014-07-02 |
Document Type: |
Abstract |
Research Program: |
Brain-inspired multiscale computation in neuromorphic hybrid systems The Human Brain Project Supercomputing and Modelling for the Human Brain Theory of multi-scale neuronal networks Signalling Pathways and Mechanisms in the Nervous System Connectivity and Activity Helmholtz Alliance on Systems Biology |
Publikationsportal JuSER |
Fast oscillations of the population firing rate in the gamma range (50-200 Hz), where each individual neuron fires irregularly with a low rate, have been observed in networks of simulated leaky integrate-and-fire (LIF) neurons as well as in population signals in the living brain.An analytical explanation of oscillatory population rates has been given in [1]. However a systematic approach identifying circuits responsible for specific oscillations in a potentially complicated structured network of populations of neurons is currently not available. Such a method would provide a tool for the identification of connections responsible for the emergence and propagation of oscillations in anatomically constrained networks and shed light on local dynamical mechanisms amplifying or suppressing certain frequencies . It could also be used to design a LIF-network with desired spectral features.In this study we consider the spectra of population firing rates produced by networks with population specific connectivity. The populations are composed of randomly connected LIF-neurons. In our analysis we make use of the formalism developed in [2], who recently closed the gap between the descriptions of linear rate models and LIF-neurons. We derive an expression for the effective connectivity matrix which incorporates the anatomical (synaptic weights, in-degrees) as well as the dynamical properties [3] of the circuit. We are able to predict the spectra of the population firing rates for any connectivity pattern that allows for asynchronous activity. The analytically obtained predictions are validated by comparison with simulation results of a multi-layered cortical network model [4].Decomposing the effective connectivity matrix at a frequency where the spectrum shows a peak reveals the dominating eigenmode. By reconstructing the anatomical and dynamical contributions to this eigenmode we can narrow down the physical connections associated with the peak in the power spectrum. |