publication . Article . Other literature type . 2018

Criteria on Balance, Stability, and Excitability in Cortical Networks for Constraining Computational Models.

Andrei Maksimov; Markus Diesmann; Markus Diesmann; Markus Diesmann; Sacha J. van Albada;
Open Access English
  • Published: 01 Jul 2018 Journal: Frontiers in Computational Neuroscience, volume 12 (eissn: 1662-5188, Copyright policy)
  • Country: Germany
Abstract
Abstract During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done s...
Subjects
free text keywords: ddc:610, Cellular and Molecular Neuroscience, Neuroscience (miscellaneous), spiking neural networks, up/down states, validation, benchmarking, computational models, asynchronous irregular activity, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neuroscience, Original Research, fluctuations
Funded by
EC| HBP SGA2
Project
HBP SGA2
Human Brain Project Specific Grant Agreement 2
  • Funder: European Commission (EC)
  • Project Code: 785907
  • Funding stream: H2020 | SGA-RIA
,
EC| BRAINSCALES
Project
BRAINSCALES
Brain-inspired multiscale computation in neuromorphic hybrid systems
  • Funder: European Commission (EC)
  • Project Code: 269921
  • Funding stream: FP7 | SP1 | ICT
,
EC| HBP
Project
HBP
The Human Brain Project
  • Funder: European Commission (EC)
  • Project Code: 604102
  • Funding stream: FP7 | SP1 | ICT
,
EC| HBP SGA1
Project
HBP SGA1
Human Brain Project Specific Grant Agreement 1
  • Funder: European Commission (EC)
  • Project Code: 720270
  • Funding stream: H2020 | SGA-RIA
Communities
FET FP7FET Proactive: FET proactive 8: Brain Inspired ICT
FET FP7FET Proactive: Brain-inspired multiscale computation in neuromorphic hybrid systems
FET FP7FET Flagships: FET Flagships
FET FP7FET Flagships: The Human Brain Project
FET H2020FET FLAG: HBP FET Flagship core project
128 references, page 1 of 9

Ali A. B.Bannister A. P.Thomson A. M. (2007). Robust correlations between action potential duration and the properties of synaptic connections in layer 4 interneurones in neocortical slices from juvenile rats and adult rat and cat. J. Physiol. 580, 149–169. 10.1113/jphysiol.2006.124214 17234697 [OpenAIRE] [PubMed] [DOI]

Argaman T.Golomb D. (2018). Does layer 4 in the barrel cortex function as a balanced circuit when responding to whisker movements? Neuro Sci. 368, 29–45. 10.1016/j.neuroscience.2017.07.054 28774782 [OpenAIRE] [PubMed] [DOI]

Avermann M.Tomm C.Mateo C.Gerstner W.Petersen C. (2012). Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. J. Neurophysiol. 107, 3116–3134. 10.1152/jn.00917.2011 22402650 [OpenAIRE] [PubMed] [DOI]

Barth A. L.Poulet J. F. (2012). Experimental evidence for sparse firing in the neocortex. Trends Neurosci. 35, 345–355. 10.1016/j.tins.2012.03.008 22579264 [PubMed] [DOI]

Beaulieu C. (1993). Numerical data on neocortical neurons in adult rat, with special reference to the GABA population. Brain Res. 609, 284–292. 10.1016/0006-8993(93)90884-P 8508310 [OpenAIRE] [PubMed] [DOI]

Beaulieu C.Kisvarday Z.Somogyi P.Cynader M.Cowey A. (1992). Quantitative distribution of GABA-immunopositive and-immunonegative neurons and synapses in the monkey striate cortex (area 17). Cereb. Cortex 2, 295–309. 10.1093/cercor/2.4.295 1330121 [OpenAIRE] [PubMed] [DOI]

Beierlein M.Fall C. P.Rinzel J.Yuste R. (2002). Thalamocortical bursts trigger recurrent activity in neocortical networks: layer 4 as a frequency-dependent gate. J. Neurosci. 22, 9885–9894. 10.1523/JNEUROSCI.22-22-09885.2002 12427845 [OpenAIRE] [PubMed] [DOI]

Beierlein M.Gibson J. R.Connors B. W. (2003). Two dynamically distinct inhibitory networks in layer 4 of the neocortex. J. Neurophysiol. 90, 2987–3000. 10.1152/jn.00283.2003 12815025 [OpenAIRE] [PubMed] [DOI]

Beltramo R.D'Urso G.Maschio M. D.Farisello P.Bovetti S.Clovis Y.. (2013). Layer-specific excitatory circuits differentially control recurrent network dynamics in the neocortex. Nat. Neurosci.16, 227–234. 10.1038/nn.3306 23313909 [OpenAIRE] [PubMed] [DOI]

Brosch M.Schreiner C. E. (1999). Correlations between neural discharges are related to receptive field properties in cat primary auditory cortex. Eur. J. Neurosci. 11, 3517–3530. 10.1046/j.1460-9568.1999.00770.x 10564360 [OpenAIRE] [PubMed] [DOI]

Brunel N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J. Comput. Neurosci. 8, 183–208. 10.1023/A:1008925309027 10809012 [PubMed] [DOI]

Burnham K. P. and Anderson, D. R. (2003). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. New York, NY: Springer Science & Business Media.

Chauvette S.Crochet S.Volgushev M.Timofeev I. (2011). Properties of slow oscillation during slow-wave sleep and anesthesia in cats. J. Neurosci. 31, 14998–15008. 10.1523/JNEUROSCI.2339-11.2011 22016533 [OpenAIRE] [PubMed] [DOI]

Chauvette S.Volgushev M.Timofeev I. (2010). Origin of active states in local neocortical networks during slow sleep oscillation. Cereb. Cortex 20, 2660–2674. 10.1093/cercor/bhq009 20200108 [OpenAIRE] [PubMed] [DOI]

Chen J.-Y.Chauvette S.Skorheim S.Timofeev I.Bazhenov M. (2012). Interneuron-mediated inhibition synchronizes neuronal activity during slow oscillation. J. Physiol. 590, 3987–4010. 10.1113/jphysiol.2012.227462 22641778 [OpenAIRE] [PubMed] [DOI]

128 references, page 1 of 9
Abstract
Abstract During ongoing and Up state activity, cortical circuits manifest a set of dynamical features that are conserved across these states. The present work systematizes these phenomena by three notions: excitability, the ability to sustain activity without external input; balance, precise coordination of excitatory and inhibitory neuronal inputs; and stability, maintenance of activity at a steady level. Slice preparations exhibiting Up states demonstrate that balanced activity can be maintained by small local circuits. While computational models of cortical circuits have included different combinations of excitability, balance, and stability, they have done s...
Subjects
free text keywords: ddc:610, Cellular and Molecular Neuroscience, Neuroscience (miscellaneous), spiking neural networks, up/down states, validation, benchmarking, computational models, asynchronous irregular activity, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neuroscience, Original Research, fluctuations
Funded by
EC| HBP SGA2
Project
HBP SGA2
Human Brain Project Specific Grant Agreement 2
  • Funder: European Commission (EC)
  • Project Code: 785907
  • Funding stream: H2020 | SGA-RIA
,
EC| BRAINSCALES
Project
BRAINSCALES
Brain-inspired multiscale computation in neuromorphic hybrid systems
  • Funder: European Commission (EC)
  • Project Code: 269921
  • Funding stream: FP7 | SP1 | ICT
,
EC| HBP
Project
HBP
The Human Brain Project
  • Funder: European Commission (EC)
  • Project Code: 604102
  • Funding stream: FP7 | SP1 | ICT
,
EC| HBP SGA1
Project
HBP SGA1
Human Brain Project Specific Grant Agreement 1
  • Funder: European Commission (EC)
  • Project Code: 720270
  • Funding stream: H2020 | SGA-RIA
Communities
FET FP7FET Proactive: FET proactive 8: Brain Inspired ICT
FET FP7FET Proactive: Brain-inspired multiscale computation in neuromorphic hybrid systems
FET FP7FET Flagships: FET Flagships
FET FP7FET Flagships: The Human Brain Project
FET H2020FET FLAG: HBP FET Flagship core project
128 references, page 1 of 9

Ali A. B.Bannister A. P.Thomson A. M. (2007). Robust correlations between action potential duration and the properties of synaptic connections in layer 4 interneurones in neocortical slices from juvenile rats and adult rat and cat. J. Physiol. 580, 149–169. 10.1113/jphysiol.2006.124214 17234697 [OpenAIRE] [PubMed] [DOI]

Argaman T.Golomb D. (2018). Does layer 4 in the barrel cortex function as a balanced circuit when responding to whisker movements? Neuro Sci. 368, 29–45. 10.1016/j.neuroscience.2017.07.054 28774782 [OpenAIRE] [PubMed] [DOI]

Avermann M.Tomm C.Mateo C.Gerstner W.Petersen C. (2012). Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. J. Neurophysiol. 107, 3116–3134. 10.1152/jn.00917.2011 22402650 [OpenAIRE] [PubMed] [DOI]

Barth A. L.Poulet J. F. (2012). Experimental evidence for sparse firing in the neocortex. Trends Neurosci. 35, 345–355. 10.1016/j.tins.2012.03.008 22579264 [PubMed] [DOI]

Beaulieu C. (1993). Numerical data on neocortical neurons in adult rat, with special reference to the GABA population. Brain Res. 609, 284–292. 10.1016/0006-8993(93)90884-P 8508310 [OpenAIRE] [PubMed] [DOI]

Beaulieu C.Kisvarday Z.Somogyi P.Cynader M.Cowey A. (1992). Quantitative distribution of GABA-immunopositive and-immunonegative neurons and synapses in the monkey striate cortex (area 17). Cereb. Cortex 2, 295–309. 10.1093/cercor/2.4.295 1330121 [OpenAIRE] [PubMed] [DOI]

Beierlein M.Fall C. P.Rinzel J.Yuste R. (2002). Thalamocortical bursts trigger recurrent activity in neocortical networks: layer 4 as a frequency-dependent gate. J. Neurosci. 22, 9885–9894. 10.1523/JNEUROSCI.22-22-09885.2002 12427845 [OpenAIRE] [PubMed] [DOI]

Beierlein M.Gibson J. R.Connors B. W. (2003). Two dynamically distinct inhibitory networks in layer 4 of the neocortex. J. Neurophysiol. 90, 2987–3000. 10.1152/jn.00283.2003 12815025 [OpenAIRE] [PubMed] [DOI]

Beltramo R.D'Urso G.Maschio M. D.Farisello P.Bovetti S.Clovis Y.. (2013). Layer-specific excitatory circuits differentially control recurrent network dynamics in the neocortex. Nat. Neurosci.16, 227–234. 10.1038/nn.3306 23313909 [OpenAIRE] [PubMed] [DOI]

Brosch M.Schreiner C. E. (1999). Correlations between neural discharges are related to receptive field properties in cat primary auditory cortex. Eur. J. Neurosci. 11, 3517–3530. 10.1046/j.1460-9568.1999.00770.x 10564360 [OpenAIRE] [PubMed] [DOI]

Brunel N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J. Comput. Neurosci. 8, 183–208. 10.1023/A:1008925309027 10809012 [PubMed] [DOI]

Burnham K. P. and Anderson, D. R. (2003). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. New York, NY: Springer Science & Business Media.

Chauvette S.Crochet S.Volgushev M.Timofeev I. (2011). Properties of slow oscillation during slow-wave sleep and anesthesia in cats. J. Neurosci. 31, 14998–15008. 10.1523/JNEUROSCI.2339-11.2011 22016533 [OpenAIRE] [PubMed] [DOI]

Chauvette S.Volgushev M.Timofeev I. (2010). Origin of active states in local neocortical networks during slow sleep oscillation. Cereb. Cortex 20, 2660–2674. 10.1093/cercor/bhq009 20200108 [OpenAIRE] [PubMed] [DOI]

Chen J.-Y.Chauvette S.Skorheim S.Timofeev I.Bazhenov M. (2012). Interneuron-mediated inhibition synchronizes neuronal activity during slow oscillation. J. Physiol. 590, 3987–4010. 10.1113/jphysiol.2012.227462 22641778 [OpenAIRE] [PubMed] [DOI]

128 references, page 1 of 9
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