Literature DB >> 30906524

Neuromodulation influences synchronization and intrinsic read-out.

Gabriele Scheler1.   

Abstract

Background: The roles of neuromodulation in a neural network, such as in a cortical microcolumn, are still incompletely understood. Neuromodulation influences neural processing by presynaptic and postsynaptic regulation of synaptic efficacy. Neuromodulation also affects ion channels and intrinsic excitability.
Methods: Synaptic efficacy modulation is an effective way to rapidly alter network density and topology. We alter network topology and density to measure the effect on spike synchronization. We also operate with differently parameterized neuron models which alter the neuron's intrinsic excitability, i.e., activation function.
Results: We find that (a) fast synaptic efficacy modulation influences the amount of correlated spiking in a network. Also, (b) synchronization in a network influences the read-out of intrinsic properties. Highly synchronous input drives neurons, such that differences in intrinsic properties disappear, while asynchronous input lets intrinsic properties determine output behavior. Thus, altering network topology can alter the balance between intrinsically vs. synaptically driven network activity.
Conclusion: We conclude that neuromodulation may allow a network to shift between a more synchronized transmission mode and a more asynchronous intrinsic read-out mode. This has significant implications for our understanding of the flexibility of cortical computations.

Entities:  

Keywords:  activation function; asynchronous; intrinsic excitability; network topology; neuromodulation; synaptic efficacy; synchronization

Mesh:

Year:  2018        PMID: 30906524      PMCID: PMC6426090          DOI: 10.12688/f1000research.15804.2

Source DB:  PubMed          Journal:  F1000Res        ISSN: 2046-1402


  49 in total

1.  Impact of correlated synaptic input on output firing rate and variability in simple neuronal models.

Authors:  E Salinas; T J Sejnowski
Journal:  J Neurosci       Date:  2000-08-15       Impact factor: 6.167

2.  The discharge variability of neocortical neurons during high-conductance states.

Authors:  M Rudolph; A Destexhe
Journal:  Neuroscience       Date:  2003       Impact factor: 3.590

3.  Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4.

Authors:  Jude F Mitchell; Kristy A Sundberg; John H Reynolds
Journal:  Neuron       Date:  2009-09-24       Impact factor: 17.173

Review 4.  Measuring and interpreting neuronal correlations.

Authors:  Marlene R Cohen; Adam Kohn
Journal:  Nat Neurosci       Date:  2011-06-27       Impact factor: 24.884

5.  Internal brain state regulates membrane potential synchrony in barrel cortex of behaving mice.

Authors:  James F A Poulet; Carl C H Petersen
Journal:  Nature       Date:  2008-07-16       Impact factor: 49.962

Review 6.  Neuromodulation of brain states.

Authors:  Seung-Hee Lee; Yang Dan
Journal:  Neuron       Date:  2012-10-04       Impact factor: 17.173

7.  Context-dependent changes in functional circuitry in visual area MT.

Authors:  Marlene R Cohen; William T Newsome
Journal:  Neuron       Date:  2008-10-09       Impact factor: 17.173

Review 8.  Correlated neuronal activity and the flow of neural information.

Authors:  E Salinas; T J Sejnowski
Journal:  Nat Rev Neurosci       Date:  2001-08       Impact factor: 34.870

9.  Sensory coding accuracy and perceptual performance are improved during the desynchronized cortical state.

Authors:  Charles B Beaman; Sarah L Eagleman; Valentin Dragoi
Journal:  Nat Commun       Date:  2017-11-03       Impact factor: 14.919

10.  Logarithmic distributions prove that intrinsic learning is Hebbian.

Authors:  Gabriele Scheler
Journal:  F1000Res       Date:  2017-07-25
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