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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-05-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2014.00056/full |
| _version_ | 1811207214174568448 |
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| author | Brent eDoiron Brent eDoiron Ashok eLitwin-Kumar Ashok eLitwin-Kumar |
| author_facet | Brent eDoiron Brent eDoiron Ashok eLitwin-Kumar Ashok eLitwin-Kumar |
| author_sort | Brent eDoiron |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T04:00:40Z |
| format | Article |
| id | doaj.art-47be4b7d38d842ea91b7fe76c719be45 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-12T04:00:40Z |
| publishDate | 2014-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-47be4b7d38d842ea91b7fe76c719be452022-12-22T03:48:44ZengFrontiers 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 |
| 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 |
| title | 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_short | Balanced Neural Architecture and the Idling Brain |
| title_sort | balanced neural architecture and the idling brain |
| 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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