Inferring synaptic inputs from spikes with a conductance-based neural encoding model

  1. Kenneth W Latimer  Is a corresponding author
  2. Fred Rieke
  3. Jonathan W Pillow  Is a corresponding author
  1. University of Washington, United States
  2. Princeton University, United States

Abstract

Descriptive statistical models of neural responses generally aim to characterize the mapping from stimuli to spike responses while ignoring biophysical details of the encoding process. Here, we introduce an alternative approach, the conductance-based encoding model (CBEM), which describes amapping fromstimuli to excitatory and inhibitory synaptic conductances governing the dynamics of sub-threshold membrane potential. Remarkably, we show that the CBEM can be fit to extracellular spike train data and then used to predict excitatory and inhibitory synaptic currents. We validate these predictions with intracellular recordings from macaque retinal ganglion cells. Moreover, we offer a novel quasi-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case of the CBEM in which excitation and inhibition are perfectly balanced. This work forges a new link between statistical and biophysical models of neural encoding and sheds new light on the biophysical variables that underlie spiking in the early visual pathway.

Data availability

All modeling tools have been made publicly available at https://github.com/pillowlab/CBEM. The datasets analyzed in this paper have been previously published as the following:1. Conductance and cell-attached spike recordings: Philipp Khuc Trong & Fred Rieke (2008). "Origin of correlated activity between parasol retinal ganglion cells." https://doi.org/10.1038/nn.2199. Dataset available via figshare https://figshare.com/articles/ON-Parasol_RGCs_for_the_conductance-based_encoding_model/9636854.2. Full-field extracellular recordings (including multiple contrasts): V. J. Uzzell & E. J. Chichilnisky (2004). "Precision of Spike Trains in Primate Retinal Ganglion Cells." https://doi.org/10.1152/jn.01171.2003. Dataset can be accessed through a response to the corresponding author.3. Spatio-temporal stimuli: Jonathan W. Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M. Litke, E. J. Chichilnisky & Eero P. Simoncelli (2008). "Spatio-temporal correlations and visual signalling in a complete neuronal population." https://doi.org/10.1038/nature07140. Dataset can be accessed through a response to the corresponding author.

Article and author information

Author details

  1. Kenneth W Latimer

    Department of Physiology and Biophysics, University of Washington, Seattle, United States
    For correspondence
    latimerk@uchicago.edu
    Competing interests
    No competing interests declared.
  2. Fred Rieke

    Department of Physiology and Biophysics, University of Washington, Seattle, United States
    Competing interests
    Fred Rieke, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1052-2609
  3. Jonathan W Pillow

    Princeton Neuroscience Institute, Department of Psychology, Princeton University, Princeton, United States
    For correspondence
    pillow@princeton.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3638-8831

Funding

McKnight Foundation

  • Jonathan W Pillow

Simons Foundation (SCGB AWD1004351)

  • Jonathan W Pillow

National Science Foundation (IIS-1150186)

  • Jonathan W Pillow

National Institute of Mental Health (MH099611)

  • Jonathan W Pillow

Howard Hughes Medical Institute

  • Fred Rieke

National Institutes of Health (EY011850)

  • Fred Rieke

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: Tissue was obtained via the tissue distribution program at the Washington National Primate Research Center. All animal procedures were performed in accordance with IACUC protocols at the University of Washington (IACUC protocol number 4277-01).

Reviewing Editor

  1. Stephanie Palmer, University of Chicago, United States

Publication history

  1. Received: March 22, 2019
  2. Accepted: December 17, 2019
  3. Accepted Manuscript published: December 18, 2019 (version 1)
  4. Version of Record published: January 29, 2020 (version 2)

Copyright

© 2019, Latimer et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Kenneth W Latimer
  2. Fred Rieke
  3. Jonathan W Pillow
(2019)
Inferring synaptic inputs from spikes with a conductance-based neural encoding model
eLife 8:e47012.
https://doi.org/10.7554/eLife.47012

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