Literature DB >> 2996632

A new statistical method for identifying interconnections between neuronal network elements.

G N Borisyuk, R M Borisyuk, A B Kirillov, E I Kovalenko, V I Kryukov.   

Abstract

A new method is proposed to analyse dependencies in point processes, which takes into account specific character of neuronal activity. Simulation modelling of neuronal network revealed that the estimated weight of connection depends monotonically on the value of the model synaptic strength. In contrast to the crosscorrelation, the method allows for nonlinear interconnections and does not require point processes to be stationary and samples to be large. Examples are presented of the method's application to neurophysiological data analysis.

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Year:  1985        PMID: 2996632     DOI: 10.1007/BF00355752

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  4 in total

1.  Interactions between neurons in auditory cortex of the cat.

Authors:  J W Dickson; G L Gerstein
Journal:  J Neurophysiol       Date:  1974-11       Impact factor: 2.714

2.  [Correlation analysis of the impulse activity of neuron pairs in the cat midbrain tegmentum].

Authors:  A S Iagodnitsyn; M L Shik
Journal:  Biofizika       Date:  1973 Jul-Aug

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Authors:  E A Liamin
Journal:  Biofizika       Date:  1968 Sep-Oct

4.  Synaptic facilitation in Aplysia explored by random presynaptic stimulation.

Authors:  J P Kroeker
Journal:  J Gen Physiol       Date:  1979-06       Impact factor: 4.086

  4 in total
  6 in total

1.  Bayesian inference for generalized linear models for spiking neurons.

Authors:  Sebastian Gerwinn; Jakob H Macke; Matthias Bethge
Journal:  Front Comput Neurosci       Date:  2010-05-28       Impact factor: 2.380

2.  Minimum mean square error estimation of connectivity in biological neural networks.

Authors:  X Yang; S A Shamma
Journal:  Biol Cybern       Date:  1991       Impact factor: 2.086

3.  A sensitive estimator for crosscorrelograms.

Authors:  I Nelken; E Vaadia
Journal:  Biol Cybern       Date:  1990       Impact factor: 2.086

4.  Maximum likelihood analysis of spike trains of interacting nerve cells.

Authors:  D R Brillinger
Journal:  Biol Cybern       Date:  1988       Impact factor: 2.086

5.  Maximum likelihood identification of neural point process systems.

Authors:  E S Chornoboy; L P Schramm; A F Karr
Journal:  Biol Cybern       Date:  1988       Impact factor: 2.086

6.  Multivariate autoregressive modeling and granger causality analysis of multiple spike trains.

Authors:  Michael Krumin; Shy Shoham
Journal:  Comput Intell Neurosci       Date:  2010-04-29
  6 in total

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