Correlation detection method and connectivity-structure estimati

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36441305, G06F 1542

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active

053981871

ABSTRACT:
The present invention provides an interneuron crossrelation identification technique and an interneuron connection-structure estimation technique for inferring a connection-structure and the strengths of the connectivities among a plurality of neurons required for constructing a neural network model, by obtaining crossrelations among time-course data of neurons. The interneuron crossrelation detection technique may include steps of: calculating conditional probabilities by, among other things, normalizing crosscoincidence histograms calculated from time-course data of activities of the neurons representing a train of action potentials of the neurons representing a train of action potentials of the neurons, and comparing trains of symbols representing time-course states of the activities of the neurons; distinguishing an inhibitory connectivity form an excitatory connectivity by comparing the conditional probabilities to each other; and quantitatively estimating the magnitude of crossrelation among the time-course data. The interneuron connection-structure estimation technique may include steps of: computing conditional probabilities by normalizing cross-coincidence histograms calculated from time-course data of activities of the neurons representing a train of action potentials of the neurons; computing conditional mutual information and three-point mutual information from the computed conditional probabilities; and inferring a connection structure among the neurons.

REFERENCES:
Melssen, W. J. et al, "Detection and Estimation of Neural Connectivity Based on Cross Correlation Analysis", Biological Cybernetics issue No. 57 1987 pp. 403-414.
Aertsen, A. et al, "Dynamics or Neuronal Firing Correlation" Journal of Neurophysiology, vol. 61, #5, May 1989 pp. 900-917.
Yang et al., "Identification of Connectivity in Neural Networks", Biophysical Journal, issue 57 987-999, 1990.

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