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A popular approach to neural characterization describes neural responses in terms of a cascade of linear and nonlinear stages: a linear filter to describe stimulus integration, followed by a nonlinear function to convert the filter output to spike rate. However, real neurons respond to stimuli in a manner that depends on the nonlinear integration of excitatory and inhibitory synaptic inputs. Here we introduce a biophysically inspired point process model that explicitly incorporates stimulus-induced changes in synaptic conductance in a dynamical model of neuronal membrane potential. Our work makes two important contributions. First, on a theoretical level, it offers a novel interpretation of the popular generalized linear model (GLM) for neural spike trains. We show that the classic GLM is a special case of our conductance-based model in which the stimulus linearly modulates excitatory and inhibitory conductances in an equal and opposite “push-pull” fashion. Our model can therefore be viewed as a direct extension of the GLM in which we relax these constraints; the resulting model can exhibit shunting as well as hyperpolarizing inhibition, and time-varying changes in both gain and membrane time constant. Second, on a practical level, we show that our model provides a tractable model of spike responses in early sensory neurons that is both more accurate and more interpretable than the GLM. Most importantly, we show that we can accurately infer intracellular synaptic conductances from extracellularly recorded spike trains. We validate these estimates using direct intracellular measurements of excitatory and inhibitory conductances in parasol retinal ganglion cells. We show that the model fit to extracellular spike trains can predict excitatory and inhibitory conductances elicited by novel stimuli with nearly the same accuracy as a model trained directly with intracellular conductances.
Author Information
Kenneth W Latimer (UT Austin)
E.J. Chichilnisky (Stanford University)
Fred Rieke (University of Washington, Seattle)
Jonathan W Pillow (UT Austin)
Jonathan Pillow is an assistant professor in Psychology and Neurobiology at the University of Texas at Austin. He graduated from the University of Arizona in 1997 with a degree in mathematics and philosophy, and was a U.S. Fulbright fellow in Morocco in 1998. He received his Ph.D. in neuroscience from NYU in 2005, and was a Royal Society postdoctoral reserach fellow at the Gatsby Computational Neuroscience Unit, UCL from 2005 to 2008. His recent work involves statistical methods for understanding the neural code in single neurons and neural populations, and his lab conducts psychophysical experiments designed to test Bayesian models of human sensory perception.
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2022 Poster: Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors »
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2019 Poster: Efficient characterization of electrically evoked responses for neural interfaces »
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2017 Spotlight: Deep Networks for Decoding Natural Images from Retinal Signals »
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2017 Poster: Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons »
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2017 Poster: YASS: Yet Another Spike Sorter »
Jin Hyung Lee · David Carlson · Hooshmand Shokri Razaghi · Weichi Yao · Georges A Goetz · Espen Hagen · Eleanor Batty · E.J. Chichilnisky · Gaute T. Einevoll · Liam Paninski -
2016 : Jonathan Pillow : Scalable Inference for Structured Hierarchical Receptive Field Models »
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2015 Poster: Recognizing retinal ganglion cells in the dark »
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2014 Poster: Optimal prior-dependent neural population codes under shared input noise »
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2014 Poster: Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit »
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2014 Poster: Low-dimensional models of neural population activity in sensory cortical circuits »
Evan Archer · Urs Koster · Jonathan W Pillow · Jakob H Macke -
2014 Poster: Sparse Bayesian structure learning with dependent relevance determination prior »
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2013 Poster: Spike train entropy-rate estimation using hierarchical Dirichlet process priors »
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2013 Poster: Bayesian entropy estimation for binary spike train data using parametric prior knowledge »
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2013 Poster: Universal models for binary spike patterns using centered Dirichlet processes »
Il Memming Park · Evan Archer · Kenneth W Latimer · Jonathan W Pillow -
2013 Spotlight: Bayesian entropy estimation for binary spike train data using parametric prior knowledge »
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2013 Poster: Spectral methods for neural characterization using generalized quadratic models »
Il Memming Park · Evan Archer · Nicholas Priebe · Jonathan W Pillow -
2013 Poster: Bayesian inference for low rank spatiotemporal neural receptive fields »
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2012 Poster: Fully Bayesian inference for neural models with negative-binomial spiking »
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2012 Poster: Bayesian active learning with localized priors for fast receptive field characterization »
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2012 Poster: Bayesian estimation of discrete entropy with mixtures of stick-breaking priors »
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2011 Session: Oral Session 13 »
Jonathan W Pillow -
2011 Poster: Bayesian Spike-Triggered Covariance Analysis »
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2011 Poster: Active learning of neural response functions with Gaussian processes »
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2011 Spotlight: Active learning of neural response functions with Gaussian processes »
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2011 Tutorial: Flexible, Multivariate Point Process Models for Unlocking the Neural Code »
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2009 Oral: Time-rescaling Methods for the Estimation and Assessment of Non-Poisson Neural Encoding Models »
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2009 Poster: Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models »
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2008 Poster: Characterizing neural dependencies with Poisson copula models »
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2008 Spotlight: Characterizing neural dependencies with Poisson copula models »
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2007 Oral: Neural characterization in partially observed populations of spiking neurons »
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2007 Poster: Neural characterization in partially observed populations of spiking neurons »
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