Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model

By Jonathan W. Pillow, Liam Paninski, V. Uzzell, Eero P. Simoncelli, E. Chichilnisky. Journal of Neuroscience, 2005

394 citations recorded by the source corpus.

Sensory encoding in spiking neurons depends on both the integration of sensory inputs and the intrinsic dynamics and variability of spike generation. We show that the stimulus selectivity, reliability, and timing precision of primate retinal ganglion cell (RGC) light responses can be reproduced accurately with a simple model consisting of a leaky integrate-and-fire spike generator driven by a linearly filtered stimulus, a postspike current, and a Gaussian noise current. We fit model parameters for individual RGCs by maximizing the likelihood of observed spike responses to a stochastic visual stimulus. Although compact, the fitted model predicts the detailed time structure of responses to novel stimuli, accurately capturing the interaction between the spiking history and sensory stimulus selectivity. The model also accounts for the variability in responses to repeated stimuli, even when fit to data from a single (nonrepeating) stimulus sequence. Finally, the model can be used to derive an explicit, maximum-likelihood decoding rule for neural spike trains, thus providing a tool for assessing the limitations that spiking variability imposes on sensory performance.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/aee92d05fe9449687df7d1cf5ae2cbf65a833bc3

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Citations in the corpus

Functional mechanisms shaping lateral geniculate responses to artificial and natural stimuli. (2008, background): “, 2004), single spike generation (Butts et al., 2007; Carandini et al., 2007; Keat et al., 2001; Pillow et al., 2005), and burst generation (Lesica and Stanley, 2004; Sherman, 2001).”
Eye smarter than scientists believed: Neural computations in circuits of the retina (2010, positive): “Indeed, several of the novel aspects of retinal function have been confirmed in the primate retina (Chander and Chichilnisky, 2001; Uzzell and Chichilnisky, 2004; Pillow et al., 2005; Petrusca et al., 2007) and thus are intimately relevant for an understanding of human vision.”
Dynamical principles for neuroscience and intelligent biomimetic devices (2006, methodology): “Different anatomic levels have been used to implement hybrid interfaces from the channel level (dynamic clamp [7]) to global signal such as EEG (Brain Machine Interface[1]) or implanted cortical electrodes.”
Information processing in the primate retina: circuitry and coding. (2007, methodology): “However, at least two studies have exploited decoding approaches (see Decoding the Visual Signal from Retinal Spike Trains) to show that the intrinsic structure of RGC spike trains contains significant information about the visual stimulus (Frechette et al. 2005, Pillow et al. 2005b).”
Spatio-temporal correlations and visual signalling in a complete neuronal population (2008, methodology): “Models included: (1) a point process model with a flexible nonlinearity, parametrized using cubic splines (red squares); (2) a stochastic, leaky integrate-and-fire model with postspike aftercurrent [7]; and (3) a point process model with additional excitatory and suppressive stimulus kernels, allowi”
Spike-triggered neural characterization. (2006, methodology): “This Brecursive LNP[ model (also referred to as a general linear model [GLM]) has appeared in recent literature ( Pillow, Paninski, Uzzell, Simoncelli, & Chichilnisky, 2005; Truccolo, Eden, Fellows, Donogue, & Brown, 2005) and may allow the introduction of some adaptation effects, as well as shorte”