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Cited by 125Neural dynamics and brain functionNonlinear Dynamics and Pattern FormationNeuroscience and Neuropharmacology Research

The Relationship Between Synchronization Among Neuronal Populations and Their Mean Activity Levels

Chawla, D., Lumer, E. D., Friston, K. J.·1999·Neural Computation
DOI 10.1162/089976699300016287
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Abstract

In the past decade the importance of synchronized dynamics in the brain has emerged from both empirical and theoretical perspectives. Fast dynamic synchronous interactions of an oscillatory or nonoscillatory nature may constitute a form of temporal coding that underlies feature binding and perceptual synthesis. The relationship between synchronization among neuronal populations and the population firing rates addresses two important issues: the distinction between rate coding and synchronization coding models of neuronal interactions and the degree to which empirical measurements of population activity, such as those employed by neuroimaging, are sensitive to changes in synchronization. We examined the relationship between mean population activity and synchronization using biologically plausible simulations. In this article, we focus on continuous stationary dynamics. (In a companion article, Chawla (forth-coming), we address the same issue using stimulus-evoked transients.) By manipulating parameters such as extrinsic input, intrinsic noise, synaptic efficacy, density of extrinsic connections, the voltage-sensitive nature of postsynaptic mechanisms, the number of neurons, and the laminar structure within the populations, we were able to introduce variations in both mean activity and synchronization under a variety of simulated neuronal architectures. Analyses of the simulated spike trains and local field potentials showed that in nearly every domain of the model's parameter space, mean activity and synchronization were tightly coupled. This coupling appears to be mediated by an increase in synchronous gain when effective membrane time constants are lowered by increased activity. These observations show that under the assumptions implicit in our models, rate coding and synchrony coding in neural systems with reciprocal interconnections are two perspectives on the same underlying dynamic. This suggests that in the absence of specific mechanisms decoupling changes in synchronization from firing levels, indexes of brain activity that are based purely on synaptic activity (e.g., functional magnetic resonance imaging) may also be sensitive to changes in synchronous coupling.

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  • Synaptic gain is determined by network dynamics, like synchronization of fast oscillations in the 40-100 Hz or gamma frequencies.

    “Synaptic gain is not just determined by receptor activity but also by network dynamics, like the synchronization of fast oscillations, especially in the 40–100 Hz or gamma frequencies (c.f., synchronous gain: Chawla et al., 1999).”

    From The Computational Anatomy of Psychosis (2013) · cites this paper as Chawla et al., 1999

    The Discussion (pp. 15–16) links network activity, synchronization, and increased synchronous gain: neurons become more responsive to coincident synaptic inputs. The paper discusses fast oscillatory dynamics, but the passages do not establish the claimed 40–100 Hz gamma range or show that synchronization determines synaptic gain generally. (LLM, conf=0.85)

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  • The encoding of salience or precision can also be understood in terms of synchronous gain and fast oscillatory dynamics associated with binding, attention and dynamic coordination.

    “As noted above, the encoding of salience or precision can also be understood in terms of synchronous gain [30] and fast (oscillatory) dynamics associated with binding, attention and dynamic coordination [4,5].”

    From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [30]

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Research works printed but not linked: 3

  1. Engel, A. K., Konig, P., Kreiter, A. K., Gray, C. M., & Singer, W., (1990). Temporal coding bycoherent oscillations as a potential solution to the binding problem. in H. G. Schuster (Ed.), Nonlinear dynamics and neural networks . New York : VCH Publishers.
  2. Gray, C. M., Engel, A. K., Konig, P., & Singer, W. (1990). Temporal properties of synchronous oscillatory neuronal interactions in cat striate cortex, In W. G. Schuster (Ed.), Nonlinear dynamics and neural networks . New York : VCH Publishers.
  3. Sporns, O., Tononi, G., & Edelman, G. M. (1990). Dynamic interactions of neuronal groups and the problem of cortical integration. In W. G. Schuster (Ed.), Nonlinear dynamics and neural networks . New York : VCH Publishers.

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Authors
Chawla, D., Lumer, E. D., & Friston, K. J.
Affiliations
UCL Queen Square Institute of Neurology (GB)
ORCID
Friston, K. J.
Venue
Neural Computation
Publisher
MIT Press - Journals
Published
1999-08-01
Volume
11
Issue
6
Pages
1389-1411
ISSN
  • 0899-7667 (ISSN-L)
  • 1530-888X
DOI
10.1162/089976699300016287
OpenAlex
W2147782383
Cited by
125
References
43
Citations by year
2026: 2 · 2025: 1 · 2024: 2 · 2023: 3 · 2022: 3 · 2020: 1 · 2019: 5 · 2018: 3 · 2017: 7 · 2016: 10 · 2015: 6 · 2014: 10 · 2013: 1 · 2012: 5
FWCI
3.70
Type
journal article
Language
en
Open access
closed
Locations
  • Neural Computation (publishedVersion)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
Wellcome Trust
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Physical Sciences › Computer Science › Computer Networks and Communications › Nonlinear Dynamics and Pattern Formation
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
Keywords
neuronal synchronization, population firing rate, rate coding, neural oscillations, local field potentials, spike trains, membrane time constant, intrinsic noise, synaptic efficacy, laminar structure, feature binding, temporal coding
MeSH terms
Brain, Computer Simulation, Models, Neurological, Neurons, Time Factors, Neural Networks, Computer

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