Signal propagation in cortical networks: a digital signal processing approach
This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characteriz...
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Frontiers Media S.A.
2009-07-01
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Series: | Frontiers in Neuroinformatics |
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Online Access: | http://journal.frontiersin.org/Journal/10.3389/neuro.11.024.2009/full |
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doaj-914bcc86eadc4f48b2817bc410e3078c2020-11-24T21:03:12ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962009-07-01310.3389/neuro.11.024.2009610Signal propagation in cortical networks: a digital signal processing approachFrancisco A Rodrigues0Luciano da F Costa1University of São PauloUniversity of São PauloThis work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.http://journal.frontiersin.org/Journal/10.3389/neuro.11.024.2009/fullnetworkscorticocortical networksdigital signal processinggraphs |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Francisco A Rodrigues Luciano da F Costa |
spellingShingle |
Francisco A Rodrigues Luciano da F Costa Signal propagation in cortical networks: a digital signal processing approach Frontiers in Neuroinformatics networks corticocortical networks digital signal processing graphs |
author_facet |
Francisco A Rodrigues Luciano da F Costa |
author_sort |
Francisco A Rodrigues |
title |
Signal propagation in cortical networks: a digital signal processing approach |
title_short |
Signal propagation in cortical networks: a digital signal processing approach |
title_full |
Signal propagation in cortical networks: a digital signal processing approach |
title_fullStr |
Signal propagation in cortical networks: a digital signal processing approach |
title_full_unstemmed |
Signal propagation in cortical networks: a digital signal processing approach |
title_sort |
signal propagation in cortical networks: a digital signal processing approach |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroinformatics |
issn |
1662-5196 |
publishDate |
2009-07-01 |
description |
This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks. |
topic |
networks corticocortical networks digital signal processing graphs |
url |
http://journal.frontiersin.org/Journal/10.3389/neuro.11.024.2009/full |
work_keys_str_mv |
AT franciscoarodrigues signalpropagationincorticalnetworksadigitalsignalprocessingapproach AT lucianodafcosta signalpropagationincorticalnetworksadigitalsignalprocessingapproach |
_version_ |
1716773901357285376 |