Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering

By Uri T. Eden, Loren M. Frank, Riccardo Barbieri, Victor Solo, Emery N. Brown. Neural Computation, 2004

388 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/64b3fed6224627f2f080f821391f3ff215850529

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. (2017, methodology): “An analog of the Kalman filter can be formulated using a point-process model of the observation state (97, 222, 490).”
Brain correlates of autonomic modulation: Combining heart rate variability with fMRI (2008, methodology): “This approach is based on the point process methods for neural spike train data analysis (Brown et al., 2001; Barbieri et al., 2004; Eden et al., 2004) already used to develop both local likelihood (Barbieri et al.”
Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter (2006, methodology): “In the Bayesian formulation, this assumption too can be relaxed and a point process model, taking into account spike timing, can be used for decoding (Brown et al., 1998; Eden, Frank, Barbieri, Solo, & Brown, 2004 ).”
Hippocampal Plasticity across Multiple Days of Exposure to Novel Environments (2004, positive): “We recently developed an adaptive point process estimation algorithm to track changes in receptive field structure on a millisecond time scale (Brown et al., 2001; Frank et al., 2002; Eden et al., 2004).”
Know Thy Neighbour: A Normative Theory of Synaptic Depression (2009, background): “Given suitable assumptions about presynaptic membrane potential dynamics and spike generation, this optimal estimator can be written in closed form exactly [9, 10].”
A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. (2005, methodology): “…filter is based on the well-established (Mendel 1995; Kitagawa and Gersh 1996) relation between the posterior probability density and the Chapman– Kolmogorov (one-step prediction) probability density, and on a Gaussian approximation of the posterior density (for details see Eden et al. 2004).”