A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects.

By Wilson A. Truccolo, Uri T. Eden, Matthew R. Fellows, John P. Donoghue, Emery N. Brown. Journal of Neurophysiology, 2005

1,143 citations recorded by the source corpus.

Subfields: Applied Mathematics, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c76d84d779e28e3b236054a0f34c3e48910399d8

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Citations in the corpus

Functional, but Not Anatomical, Separation of "what" and "when" in Prefrontal Cortex Gaussian-process Factor Analysis for Low-dimensional Single-trial Analysis of Neural Population Activity (positive): “A possible solution is to replace the Gaussian observation noise model of PPCA and FA with a point-process (Smith and Brown 2003; Truccolo et al. 2005; Yu et al. 2006) likelihood model.”
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, background): “The likelihood function depends on such parameters as the neuronal spiking history, activity of the other neurons in the population, external stimuli, and behaviors (144, 809).”
Brain-machine interfaces: past, present and future. (2006, methodology): “3, 391– 398 110 Truccolo, W. et al. (2005) A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J. Neurophysiol. 93, 1074–1089 111 Brown, E.N. et al. (2004) Multiple neural spike train data analysis: state-of-the-art and”
The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. (2014, methodology): “A theoretically principled approach is to replace the VAR modelling step with ﬁtting of full and reduced point process models, within a framework of maximum likelihood estimation (Okatan et al., 2005; Truccolo et al., 2005; Kim et al., 2011; Gerhard et al., 2013).”
Dimensionality reduction for large-scale neural recordings (2014, background): “It can be used in settings in which the explanatory variables are observed 64,66 or unobserved (that is, latent) 71,72,74–77 .”
Bridging the brain to the world: a perspective on neural interface systems. (2008, positive): “Virtually every readily identifiable method of decoding information has been attempted in both FP and spike signals (for further discussion see Paninski et al., 2007; Schwartz et al., 2006; Serruya et al., 2003; Srinivasan et al., 2007; Truccolo et al., 2005; Wu et al., 2006).”
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks (2015, methodology): “When the time axis is discretized, the point process data can be converted to binary time series (or time series of count data if binning is coarse) and analyzed via time series analysis techniques [47, 1, 38].”
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”
Information processing in the primate retina: circuitry and coding. (2007, background): “To test whether refractoriness alone is sufficient to explain the intrinsic structure of RGC spike trains, one study empirically expressed the probability of a spike at a given point in time as the product of an arbitrary " free " firing”
Hippocampal “time cells” bridge the gap in memory for discontiguous events (2011, positive): “The spike trains during the trial period of interest were modeled as point processes and analyzed within a GLM framework (McCullagh and Nelder, 1989; Daley and Vere-Jones, 2003; Brown et al., 2003; Truccolo et al., 2005 ).”