Neural code metrics: Analysis and application to the assessment of neural models

Authors:
João Martins;Pedro Tomás;Leonel Sousa
Affiliations:
INESC-ID, Lisboa, Portugal and Engineering Dept. at Escola Superior de Tecnologia e Gestão, Instituto Politécnico de Beja, Portugal;INESC-ID, Lisboa, Portugal and Electrical and Computers Engineering Dept. at Instituto Superior Técnico, Technical University of Lisbon, Portugal;INESC-ID, Lisboa, Portugal and Electrical and Computers Engineering Dept. at Instituto Superior Técnico, Technical University of Lisbon, Portugal
Venue:
Neurocomputing
Year:
2009

Citing 6
Cited 1

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Abstract

For the analysis of natural neural responses it is necessary to evaluate and compare the reliability of the produced spike sequences. The same occurs in the development and evaluation of neural models, which should mimic the real neural centers that are being modeled. Several neural metrics have been proposed to analyze neural responses, and to tune and evaluate neural models. Neural metrics measure different characteristics of the neural code and can be grouped into distinct classes, as they follow a firing rate or time-code perspective. In this paper, several metrics belonging to the firing rate, spike train and firing event classes are reviewed. Using sets of neuronal responses and a set of models, the metrics are analyzed and compared to disclose their advantages and drawbacks. In most cases these metrics depend on a free parameter, that establishes their sensitivity to particular characteristics of the neural code. After showing that the incorrect choice of these parameters can lead to meaningless results, methods are presented in this paper to define a valid range of values for the parameters. These methods are based on a statistical analysis of the inter-trials errors. The application of neural metrics to the tuning and assessment of neural models of distinct classes reveals important results. Some of the analyzed metrics possess pronounced minima, specifically around the origin, which makes the optimization process more difficult; nonetheless, they provide insightful results for the evaluation of models. This paper also discusses the application of the neural metrics to evaluate neural models, providing relevant guidelines for their utilization.