Spikes: Exploring the Neural Code

Summary

Spikes provides a self-contained review of relevant concepts in information theory and statistical decision theory about the representation of sensory signals in neural spike trains and a quantitative framework is used to pose precise questions about the structure of the neural code.

Keywords

Stimulus (psychology), Sensory system, Perception, Computer science, Spike (software development)

References

No references recorded for this paper.

Cited by

Reproducing kernel Hilbert spaces for point processes, with applications to neural activity analysisAntónio R. C. Paiva — 2008
Introduction: Computer Vision Research at NECIIngemar J. Cox — 1999
An agent-independent task learning frameworkMark A. Wood — 2008
Computational model of carbachol-induced delta, theta and gamma-like oscillations in hippocampusJean‐Marc Fellous, Paul Tiesinga, Jorge V. José — 2001
Methods for short time series analysis of cell-based biosensor data.Lori Hausegger, Matthew Hennigar, Troy Riddell — 2001
Modulation of excitability as a learning and memory mechanism: a molecular genetic perspective.Lucy Orta, Jorge Orta — 2001
Neural timing netsMichael Kosztarab — 2001
Variability of spike trains and the processing of temporal patterns of acoustic signals—problems, constraints, and solutionsManami Iwata — 2004
Searching for Optimal Sensory Signals: Iterative Stimulus Reconstruction in Closed-Loop ExperimentsChanchal Yadav, B. L. Pareek — 2004
Optimal synchrony state for maximal information transmission布施佐代子, Feng Liu, Wei Wang — 2004
Efficiency of information transmission by retinal ganglion cells.María del Rosario Álvarez Martínez — 2004
Retino-cortical information transmission achievable with a retina implant.Pamela Figel — 2005
A comparison of Gamma and Gaussian dynamic convolution models of the fMRI BOLD response.Howard S. Levie — 2005
Dynamic synapses as archives of synaptic history: state‐dependent redistribution of synaptic efficacy in the rat hippocampal CA1Magali Deleu — 2005
Sufficient encoding of dynamical systemsFelix Creutzig — 2008
Dynamical basis of irregular spiking in NMDA-driven prefrontal cortex neurons.Rafael García Molina, Luis Antonio Villada Lobete — 2007
On the design of multi-site microelectrodes for neuronal recordingsUlrich Hofmann, Erik De Schutter, Ken Yoshida — 2000
Spike-time reliability of layered neural oscillator networksManuel Barrera Benítez — 2009
Uniform signal redundancy of parasol and midget ganglion cells in primate retina隼人川口, 恵介数野 — 2009
A toolbox for the fast information analysis of multiple-site LFP, EEG and spike train recordingsRony G. Flatscher — 2009

Related papers

SPIKING NEURON MODELS Single Neurons , Populations , PlasticityWulfram Gerstner, Werner M. Kistler — 2002
The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPsW. R. Softky, Christof Koch — 1993
Weak pairwise correlations imply strongly correlated network states in a neural populationElad Schneidman, Michael J. Berry, Ronen Segev — 2005
The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information CodingMichael N. Shadlen, William T. Newsome — 1998
The structure and precision of retinal spike trains.Michael J. Berry, David K. Warland, Markus Meister — 1997
Reproducibility and Variability in Neural Spike TrainsRob R. de Ruyter van Steveninck, Geoffrey D. Lewen, Steven Strong — 1997
Entropy and Information in Neural Spike TrainsSteven Strong, R. Köberle, Rob R. de Ruyter van Steveninck — 1996
Elements of Information TheoryThomas M. Cover, Joy A. Thomas — 2001
Reliability of spike timing in neocortical neurons.Zachary F. Mainen, Terrence J. Sejnowski — 1995
Neuronal population coding of movement direction.Apostolos P. Georgopoulos, Andrew B. Schwartz, Ronald E. Kettner — 1986