Biological Sciences Division
Published December 18, 2019 | Version v1
Journal article Open

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

Creators

  • 1. University of Chicago
  • 2. University of Washington
  • 3. Princeton University

Description

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 a mapping from stimuli 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 (copy archived at https://github.com/elifesciences-publications/CBEM). The datasets analyzed in this paper have been previously published as the following: 1. Conductance and cell-attached spike recordings: Philipp Khuc Trong and 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): VJ Uzzell and EJ 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, EJ Chichilnisky and 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.

The following previously published data sets were used:

Kenneth W Latimer Fred Rieke Jonathan W Pillow (2019) figshare ON-Parasol RGCs for the conductance-based encoding model. https://doi.org/10.6084/m9.figshare.9636854

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Additional details

Identifiers

DOI
10.7554/eLife.47012
Other
oai:uchicago.tind.io:9887

Funding

McKnight Foundation
Simons Foundation
SCGB AWD1004351
National Science Foundation
IIS-1150186
National Institute of Mental Health
MH099611
Howard Hughes Medical Institute
National Institutes of Health
EY011850

UChicago Information

Division(s)
Biological Sciences Division
Department(s)
Neurobiology