A Method for the Measurement and Interpretation of Neuronal Interactions: Improved Fitting of Cross-Correlation Histograms using 1D-Gabor Functions

Abstract

Cross-correlation analysis of separable multi-unit activity is the most used method to investigate neuronal connectivity. Features such as peaks, troughs, and satellite peaks in the cross-correlogram reflect the temporal relation between the activities of neurons. Precise estimation of such features requires independent measures. A very popular and effective method is to perform curve fitting using 1D Gabor functions. However, because of the non-linearity of the function, an iterative fitting procedure using optimization algorithms is required. As proposed from literature, we used the Levenberg-Marquardt algorithm. However, when applied to our data, the algorithm performed poorly. Here, we show that Trust Region algorithm represent a more attractive alternative to Levenberg-Marquardt in terms of performance and computational cost.

Meeting Name

IEEE 15th International Conference on Intelligent Computer Communication and Processing, ICCP 2019 (2019: Sep. 5-7, Cluj-Napoca, Romania)

Department(s)

Economics

Comments

This work was supported by: two grants from the Romanian National Authority for Scientific Research and Innovation, CNCS-UEFISCDI (Project Numbers PN-III-P4-ID-PCE-2016-0010 and COFUND-NEURON-NMDAR-PSY), and a National Science Foundation grant NSF-IOS-1656830 funded by the U.S. Government.

Keywords and Phrases

1D-Gabor function; Cross-correlation; Levenberg-Marquardt algorithm; Signal processing; Spike sorting; Trust-Region algorithm

International Standard Book Number (ISBN)

978-172814914-1

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Sep 2019

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