Balanced Neural Architecture and the Idling Brain

A signature feature of cortical spike trains is their trial-to-trial variability. This variability is large in spontaneous conditions and is reduced when cortex is driven by a stimulus or task. Models of recurrent cortical networks with unstructured, yet balanced, excitation and inhibition generat...

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Main Authors: Brent eDoiron, Ashok eLitwin-Kumar
Format: Article
Language:English
Published: Frontiers Media S.A. 2014-05-01
Series:Frontiers in Computational Neuroscience
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00056/full
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spelling doaj-47be4b7d38d842ea91b7fe76c719be452020-11-24T21:11:57ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882014-05-01810.3389/fncom.2014.0005678458Balanced Neural Architecture and the Idling BrainBrent eDoiron0Brent eDoiron1Ashok eLitwin-Kumar2Ashok eLitwin-Kumar3University of PittsburghUniversity of Pittsburgh and Carnegie Mellon UniversityUniversity of Pittsburgh and Carnegie Mellon UniversityUniversity of Pittsburgh and Carnegie Mellon UniversityA signature feature of cortical spike trains is their trial-to-trial variability. This variability is large in spontaneous conditions and is reduced when cortex is driven by a stimulus or task. Models of recurrent cortical networks with unstructured, yet balanced, excitation and inhibition generate variability consistent with evoked conditions. However, these models lack the long timescale fluctuations and large variability present in spontaneous conditions. We propose that global network architectures which support a large number of stable states (attractor networks) allow balanced networks to capture key features of neural variability in both spontaneous and evoked conditions. We illustrate this using balanced spiking networks with clustered assembly, feedforward chain, and ring structures. By assuming that global network structure is related to stimulus preference, we show that signal correlations are related to the magnitude of correlations in the spontaneous state. In our models, the dynamics of spontaneous activity encompasses much of the possible evoked states, consistent with many experimental reports. Finally, we contrast the impact of stimulation on the trial-to-trial variability in attractor networks with that of strongly coupled spiking networks with chaotic firing rate instabilities, recently investigated by Ostojic (2014). We find that only attractor networks replicate an experimentally observed stimulus-induced quenching of trial-to-trial variability. In total, the comparison of the trial-variable dynamics of single neurons or neuron pairs during spontaneous and evoked activity can be a window into the global structure of balanced cortical networks.http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00056/fullCortical Circuitsneural variabilityspiking modelsBalanced cortical networksSpontaneous cortical activity
collection DOAJ
language English
format Article
sources DOAJ
author Brent eDoiron
Brent eDoiron
Ashok eLitwin-Kumar
Ashok eLitwin-Kumar
spellingShingle Brent eDoiron
Brent eDoiron
Ashok eLitwin-Kumar
Ashok eLitwin-Kumar
Balanced Neural Architecture and the Idling Brain
Frontiers in Computational Neuroscience
Cortical Circuits
neural variability
spiking models
Balanced cortical networks
Spontaneous cortical activity
author_facet Brent eDoiron
Brent eDoiron
Ashok eLitwin-Kumar
Ashok eLitwin-Kumar
author_sort Brent eDoiron
title Balanced Neural Architecture and the Idling Brain
title_short Balanced Neural Architecture and the Idling Brain
title_full Balanced Neural Architecture and the Idling Brain
title_fullStr Balanced Neural Architecture and the Idling Brain
title_full_unstemmed Balanced Neural Architecture and the Idling Brain
title_sort balanced neural architecture and the idling brain
publisher Frontiers Media S.A.
series Frontiers in Computational Neuroscience
issn 1662-5188
publishDate 2014-05-01
description A signature feature of cortical spike trains is their trial-to-trial variability. This variability is large in spontaneous conditions and is reduced when cortex is driven by a stimulus or task. Models of recurrent cortical networks with unstructured, yet balanced, excitation and inhibition generate variability consistent with evoked conditions. However, these models lack the long timescale fluctuations and large variability present in spontaneous conditions. We propose that global network architectures which support a large number of stable states (attractor networks) allow balanced networks to capture key features of neural variability in both spontaneous and evoked conditions. We illustrate this using balanced spiking networks with clustered assembly, feedforward chain, and ring structures. By assuming that global network structure is related to stimulus preference, we show that signal correlations are related to the magnitude of correlations in the spontaneous state. In our models, the dynamics of spontaneous activity encompasses much of the possible evoked states, consistent with many experimental reports. Finally, we contrast the impact of stimulation on the trial-to-trial variability in attractor networks with that of strongly coupled spiking networks with chaotic firing rate instabilities, recently investigated by Ostojic (2014). We find that only attractor networks replicate an experimentally observed stimulus-induced quenching of trial-to-trial variability. In total, the comparison of the trial-variable dynamics of single neurons or neuron pairs during spontaneous and evoked activity can be a window into the global structure of balanced cortical networks.
topic Cortical Circuits
neural variability
spiking models
Balanced cortical networks
Spontaneous cortical activity
url http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00056/full
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