Multiple neural spike train data analysis: state-of-the-art and future challenges

By Emery N. Brown, Robert E. Kass, Partha P. Mitra. Nature Neuroscience, 2004

936 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrochemistry

View Paper: https://www.semanticscholar.org/paper/2dfa8200052fe8963540b1a0ea4c627c8df3f4dc

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In Cellular and Molecular Neuroscience

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Citations in the corpus

How advances in neural recording affect data analysis (2011, background): “Understanding what makes neurons fire is a central question in neuroscience and being able to accurately predict neural activity is at the heart of many neural data analysis technique”
A Method for Selecting the Bin Size of a Time Histogram (2007, neutral): “…estimating the instantaneous activity of an individual neuron or the joint activity of multiple neurons (DiMatteo, Genovese, & Kass, 2001; Wiener & Rich-mond, 2002; Sanger, 2002; Kass, Ventura, & Cai, 2003; Brockwell, Rojas, & Kass, 2004; Kass, Ventura, & Brown, 2005; Brown, Kass, & Mitra, 2004).”
CIFAR10-DVS: An Event-Stream Dataset for Object Classification (2017, background): “Point process whose values are point patterns is often used to model many kinds of data, such as spikes of neurons in computational neuroscience (Brown et al., 2004), positions of trees or plants in the forestry and plant ecology.”
Large-scale recording of neuronal ensembles (2004, background): “Large-scale recorded spike trains create new challenges for data management, visualization and analysi”
Online Optimization in Dynamic Environments (2013, background): “In a biological neural network, a node could correspond to a neuron and an action could correspond to a neural spike [57].”
Self‐consistent method for density estimation (2009, methodology): “, i.e. by performing the integral 1 (2π)d R ℜd dt d instead of 1 2π R ℜ dt. Among many possible implementations, the method could be then applied to the analysis of multielectrode neuronal recordings [6], multivariate ﬁnancial data [5], and reconstruction of Ramachandran angle distributions [18]. Ap”
Estimation of (near) low-rank matrices with noise and high-dimensional scaling (2009, methodology): “ the market might be governed by a much smaller set of macro-variables (combinations of these ﬁnancial instruments). Similar statements apply to other types of time series data, including neural data [11, 20], subspace tracking models in signal processing, and motion models models in computer vision”
Online Convex Optimization in Dynamic Environments (2015, background): “In a biological neural network, a node could correspond to a neuron and an action could correspond to a neural spike [59].”