Literature DB >> 19447087

Poisson or not Poisson: differences in spike train statistics between parietal cortical areas.

Bruno B Averbeck1.   

Abstract

The variability of neuronal responses is proportional to the mean in many brain areas, which suggests that neural responses might follow a Poisson distribution. In this issue of Neuron, Maimon and Assad document a surprising violation of Poisson firing. Specifically, they show that there are differences in the amount of periodic structure in spike trains across cortical areas, with multimodal sensory areas being more regular than visual areas.

Mesh:

Year:  2009        PMID: 19447087     DOI: 10.1016/j.neuron.2009.04.021

Source DB:  PubMed          Journal:  Neuron        ISSN: 0896-6273            Impact factor:   17.173


  8 in total

1.  The subthreshold relation between cortical local field potential and neuronal firing unveiled by intracellular recordings in awake rats.

Authors:  Michael Okun; Amir Naim; Ilan Lampl
Journal:  J Neurosci       Date:  2010-03-24       Impact factor: 6.167

2.  Trial-to-trial variability in the responses of neurons carries information about stimulus location in the rat whisker thalamus.

Authors:  Alessandro Scaglione; Karen A Moxon; Juan Aguilar; Guglielmo Foffani
Journal:  Proc Natl Acad Sci U S A       Date:  2011-08-22       Impact factor: 11.205

3.  A new method to infer higher-order spike correlations from membrane potentials.

Authors:  Imke C G Reimer; Benjamin Staude; Clemens Boucsein; Stefan Rotter
Journal:  J Comput Neurosci       Date:  2013-03-10       Impact factor: 1.621

4.  Emergence of Narrowband High Frequency Oscillations from Asynchronous, Uncoupled Neural Firing.

Authors:  Stephen V Gliske; William C Stacey; Eugene Lim; Katherine A Holman; Christian G Fink
Journal:  Int J Neural Syst       Date:  2016-07-14       Impact factor: 5.866

5.  Modulation of Neural Variability in Premotor, Motor, and Posterior Parietal Cortex during Change of Motor Intention.

Authors:  Sohrab Saberi-Moghadam; Simone Ferrari-Toniolo; Stefano Ferraina; Roberto Caminiti; Alexandra Battaglia-Mayer
Journal:  J Neurosci       Date:  2016-04-20       Impact factor: 6.167

6.  Spike train auto-structure impacts post-synaptic firing and timing-based plasticity.

Authors:  Bertram Scheller; Marta Castellano; Raul Vicente; Gordon Pipa
Journal:  Front Comput Neurosci       Date:  2011-12-16       Impact factor: 2.380

7.  Closed Loop Interactions between Spiking Neural Network and Robotic Simulators Based on MUSIC and ROS.

Authors:  Philipp Weidel; Mikael Djurfeldt; Renato C Duarte; Abigail Morrison
Journal:  Front Neuroinform       Date:  2016-08-03       Impact factor: 4.081

8.  Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data.

Authors:  Cristiano Capone; Guido Gigante; Paolo Del Giudice
Journal:  Sci Rep       Date:  2018-11-19       Impact factor: 4.379

  8 in total

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