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Computational Intelligence and Neuroscience
Volume 2015 (2015), Article ID 359590, 11 pages
http://dx.doi.org/10.1155/2015/359590
Research Article

Characterizing the Input-Output Function of the Olfactory-Limbic Pathway in the Guinea Pig

1Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
2Laboratory for Computational and Statistical Learning, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
3Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), Università degli studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy

Received 7 April 2015; Accepted 6 July 2015

Academic Editor: Christian W. Dawson

Copyright © 2015 Gian Luca Breschi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Nowadays the neuroscientific community is taking more and more advantage of the continuous interaction between engineers and computational neuroscientists in order to develop neuroprostheses aimed at replacing damaged brain areas with artificial devices. To this end, a technological effort is required to develop neural network models which can be fed with the recorded electrophysiological patterns to yield the correct brain stimulation to recover the desired functions. In this paper we present a machine learning approach to derive the input-output function of the olfactory-limbic pathway in the in vitro whole brain of guinea pig, less complex and more controllable than an in vivo system. We first experimentally characterized the neuronal pathway by delivering different sets of electrical stimuli from the lateral olfactory tract (LOT) and by recording the corresponding responses in the lateral entorhinal cortex (l-ERC). As a second step, we used information theory to evaluate how much information output features carry about the input. Finally we used the acquired data to learn the LOT-l-ERC “I/O function,” by means of the kernel regularized least squares method, able to predict l-ERC responses on the basis of LOT stimulation features. Our modeling approach can be further exploited for brain prostheses applications.