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Cited by 451Neural dynamics and brain functionNeuroscience and Neuropharmacology ResearchUndergraduate Neuroscience Education and Research

Multiple models to capture the variability in biological neurons and networks

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Marder, Eve, Taylor, Adam L·2011·Nature Neuroscience
DOI 10.1038/nn.2735PMC PMC3686573
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Abstract

How tightly tuned are the synaptic and intrinsic properties that give rise to neuron and circuit function? Experimental work shows that these properties vary considerably across identified neurons in different animals. Given this variability in experimental data, this review describes some of the complications of building computational models to aid in understanding how system dynamics arise from the interaction of system components. We argue that instead of trying to build a single model that captures the generic behavior of a neuron or circuit, it is beneficial to construct a population of models that captures the behavior of the population that provided the experimental data. Studying a population of models with different underlying structure and similar behaviors provides opportunities to discover unsuspected compensatory mechanisms that contribute to neuron and network function.

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  • Different runs of the evolutionary algorithm can produce different solutions with nearly the same behavioral performance, so the result is an ensemble of possible models.

    “Since different runs of the evolutionary algorithm can produce different solutions with nearly the same behavioral performance, the result of this process is not a unique model, but rather an ensemble of possible models [62].”

    From Connecting a Connectome to Behavior: An Ensemble of Neuroanatomical Models of C. elegans Klinotaxis (2013) · cites this paper as [62]

  • Variability in both model parameters and motor output should not necessarily be considered weaknesses of the model but may instead reflect true biological variability.

    “However, variability in both model parameters and motor output should not necessarily be considered weaknesses of the model but may instead reflect true biological variability (Marder and Taylor, 2011).”

    From Modeling spinal locomotor circuits for movements in developing zebrafish (2021) · cites this paper as Marder and Taylor, 2011

  • An ensemble of 50 models was generated because the constraints might not uniquely constrain model parameters.

    “As it is possible that these constraints might not uniquely constrain model parameters32, we generated an ensemble of 50 models, all constrained with the same connectome, and optimized to perform the same task.”

    From Connectome-constrained networks predict neural activity across the fly visual system (2024) · cites this paper as [32]

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Cited by

Sorted by citations

Authors
Marder, E., & Taylor, A. L.
Affiliations
Brandeis University (US)
Corresponding author
Marder, Eve
ORCID
Venue
Nature Neuroscience
Publisher
Springer Science and Business Media LLC
Published
2011-01-26
Volume
14
Issue
2
Pages
133-138
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
DOI
10.1038/nn.2735
PMC
PMC3686573
OpenAlex
W1990432147
Cited by
451
References
2
Citations by year
2026: 32 · 2025: 30 · 2024: 26 · 2023: 26 · 2022: 35 · 2021: 39 · 2020: 20 · 2019: 19 · 2018: 30 · 2017: 31 · 2016: 28 · 2015: 40 · 2014: 28 · 2013: 27 · 2012: 23
FWCI
15.43
Type
journal article
Language
en
Open access
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Neuroscience (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health
  • National Institute of Mental Health (MH 46742, R37MH046742, R01MH046742)
  • National Institute of Neurological Disorders and Stroke (R01NS017813, R37NS017813, NS17813)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
Keywords
neural variability, computational neuron model, synaptic properties, intrinsic neuronal properties, compensatory mechanisms, neural circuits, identified neurons
MeSH terms
Animals, Electrophysiology, Membrane Potentials, Models, Neurological, Nerve Net, Neural Conduction, Neurons

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