Spatio-temporal correlations and visual signalling in a complete neuronal population

By Jonathan W. Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M. Litke, E. Chichilnisky, Eero P. Simoncelli. Nature, 2008

1,468 citations recorded by the source corpus.

Statistical dependencies in the responses of sensory neurons govern both the amount of stimulus information conveyed and the means by which downstream neurons can extract it. Although a variety of measurements indicate the existence of such dependencies, their origin and importance for neural coding are poorly understood. Here we analyse the functional significance of correlated firing in a complete population of macaque parasol retinal ganglion cells using a model of multi-neuron spike responses. The model, with parameters fit directly to physiological data, simultaneously captures both the stimulus dependence and detailed spatio-temporal correlations in population responses, and provides two insights into the structure of the neural code. First, neural encoding at the population level is less noisy than one would expect from the variability of individual neurons: spike times are more precise, and can be predicted more accurately when the spiking of neighbouring neurons is taken into account. Second, correlations provide additional sensory information: optimal, model-based decoding that exploits the response correlation structure extracts 20% more information about the visual scene than decoding under the assumption of independence, and preserves 40% more visual information than optimal linear decoding. This model-based approach reveals the role of correlated activity in the retinal coding of visual stimuli, and provides a general framework for understanding the importance of correlated activity in populations of neurons.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/2fbeef5ca17328590cf74353ff5654dad7800d67

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Citations in the corpus

Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “Simoncelli and collaborators [154, 164] and Cocco, Leibler, and Monasson [48] use an integrate-and-ﬁre model [39] to infer how the neurons are interconnected on the basis of time series of spikes in all neurons.”
Statistical models for neural encoding, decoding, and optimal stimulus design. (2007, methodology): “Finally, we return to the encoding problem to describe how to use these models to adaptively optimize the stimuli presented to the cell on a trial-by-trial basis, in order that we may infer the optimal model parameters as efficiently as possible.”
A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations (2011, background): “We believe this study is of great relevance given the ubiquity of higher-order correlations in the brain (22, 23) and their relevance for neural coding (24).”
Flexible statistical inference for mechanistic models of neural dynamics (2017, methodology): “We demonstrate the ability of SNPE to recover ground-truth posteriors in Gaussian Mixtures and Generalised Linear Models (GLMs) [41], and apply SNPE to a Hodgkin-Huxley neuron model and an autapse model, which can have parameter regimes of unstable behaviour and missing features.”
Using goal-driven deep learning models to understand sensory cortex (2016, result): “In both vision and audition, model architecture class could also be improved by building more biologically realistic sensor front-ends into early layers, using known results about subcortical structures 53 .”
Quantifying herding effects in crowd wisdom (2014, methodology): “We can draw a close analogy to the spiking activities of a multi-neuron system [17]: the response (i.”
Towards the neural population doctrine. (2019, background): “Retinal ganglion cells have been a particularly attractive target for population modeling, beginning (at least) a decade ago [56] and continuing with more recent ANN-based approaches [57 ,58].”
Spatial attention decorrelates intrinsic activity fluctuations in macaque area V4. (2009, background): “Response variability affects how reliably information is encoded by neuronal signals (Parker and Newsome, 1998; Zohary et al., 1994; Shadlen et al., 1996; Averbeck et al., 2006; Pillow et al., 2008).”