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Global and Multiplexed Dendritic Computations under In Vivo-like Conditions

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Ujfalussy, Balázs B., Makara, Judit K., Lengyel, Máté, Branco, Tiago·2018·Neuron
DOI 10.1016/j.neuron.2018.08.032PMC PMC6226578

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Abstract

Dendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to the overall input-output transformation of single neurons. We developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex, in vivo-like spatiotemporal synaptic input patterns. We used the hLN to predict the somatic membrane potential of an in vivo-validated detailed biophysical model of a L2/3 pyramidal cell. Linear input integration with a single global dendritic nonlinearity achieved above 90% prediction accuracy. A novel hLN motif, input multiplexing into parallel processing channels, could improve predictions as much as conventionally used additional layers of local nonlinearities. We obtained similar results in two other cell types. This approach provides a data-driven characterization of a key component of cortical circuit computations: the input-output transformation of neurons during in vivo-like conditions.

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  • Some studies have addressed the gap in modeling synaptic integration, but not at the scale of a realistic number of synaptic inputs, full dendritic biophysical mechanisms, and high temporal precision of spike output.

    “Some studies have addressed this gap, but not at the scale of a realistic number of synaptic inputs, full dendritic biophysical mechanisms, and high temporal precision of spike output (Poirazi, Brannon, and Mel 2003b; Polsky, Mel, and Schiller 2004; Ujfalussy et al. 2018).”

    From Single cortical neurons as deep artificial neural networks (2021) · cites this paper as Ujfalussy et al. 2018

    The Results section, “Fitting the Input of a Biophysical Model to In Vivo Dendritic Recordings” (page unavailable), describes a biophysical model with more than 600 excitatory and 200 inhibitory synapses, so this study did address realistic input scale and dendritic mechanisms. The Discussion section, “Aspects of Dendritic Processing Not Captured by hLN Models” (page unavailable), says its spike-timing predictions had limited precision. Thus the claimed limitation applies to spike-output precision, but not to all three listed features. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Authors
Ujfalussy, B. B., Makara, J. K., Lengyel, M., & Branco, T.
Affiliations
  • MRC Laboratory of Molecular Biology (GB)
  • HUN-REN Institute of Experimental Medicine (HU)
  • Central European University (AT)
Corresponding author
Ujfalussy, Balázs B.
ORCID
Venue
Neuron
Publisher
Elsevier BV
Published
2018-11
Volume
100
Issue
3
Pages
579-592.e5
ISSN
0896-6273 (ISSN-L)
DOI
10.1016/j.neuron.2018.08.032
PMC
PMC6226578
OpenAlex
W2781725908
Cited by
114
References
8
Citations by year
2026: 11 · 2025: 13 · 2024: 18 · 2023: 14 · 2022: 19 · 2021: 16 · 2020: 9 · 2019: 13 · 2018: 1
FWCI
5.81
Type
journal article
Language
en
Open access
Licence
Locations
  • Neuron (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Open Research (Auckland University of Technology) (acceptedVersion, other-oa)
  • UCL Discovery (University College London) (submittedVersion)
  • Cambridge University Engineering Department Publications Database (submittedVersion)
  • Open Research (Auckland University of Technology) (submittedVersion, cc-by)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo (cc-by)
Funders
  • MTA
  • NKFIH (PD-020/2015, PD-125386)
  • Wellcome Trust (090915/Z/09/Z)
  • Howard Hughes Medical (55008740)
  • Medical Research Council (MC-UP-1201/1)
  • Gatsby Charitable (095621/Z/11/Z)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
dendritic integration, dendritic nonlinearities, input-output transformation, biophysical neuron models
MeSH terms
Animals, Dendrites, Humans, Membrane Potentials, Models, Neurological, Nerve Net, Linear Models

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