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

Gian Luca Breschi,Lorenzo Rosasco,Thierry Nieus,Valentina Pasquale,Carlo Ciliberto,Michela Chiappalone,Stefano Taverna

doi:10.1155/2015/359590

Gian Luca Breschi, Lorenzo Rosasco + Show 5 more

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https://doi.org/10.1155/2015/359590

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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.

Highlights

Thanks to recent advances in neurotechnology and neurosurgery the possibility of implanting smart devices in the brain to replace damaged neuronal circuits or deliver appropriate electrical stimulation is opening the way to innovative treatment of neurological disorders, from epilepsy to stroke [1,2,3,4]
Proposed solutions range from simple activitydependent stimulators [3, 4], which monitor the activity of selected brain regions and deliver electrical pulses to other regions depending on the detected patterns, to more complex modeling approaches [1, 2], which aim at learning the input/output (I/O) function of specific neuronal circuits to be replaced or “repaired.” In both approaches electrical stimulation is continuously modulated in response to specific recorded patterns of activity, in real time and in a closed-loop fashion
The former corresponds to the activation of apical dendrites in the superficial layer of the posterior piriform cortex (pPC), while the latter is sustained by intra-pPC fibers and by associative fibers originating from neighboring cortical structures. lateral entorhinal cortex (l-ERC) responses were characterized by a large wave component that represents the direct propagation of the olfactory input, followed by large-amplitude polysynaptic response [8, 20, 21]

Summary

Introduction

Thanks to recent advances in neurotechnology and neurosurgery the possibility of implanting smart devices in the brain to replace damaged neuronal circuits or deliver appropriate electrical stimulation is opening the way to innovative treatment of neurological disorders, from epilepsy to stroke [1,2,3,4]. Proposed solutions range from simple activitydependent stimulators [3, 4], which monitor the activity of selected brain regions and deliver electrical pulses to other regions depending on the detected patterns, to more complex modeling approaches [1, 2], which aim at learning the input/output (I/O) function of specific neuronal circuits to be replaced or “repaired.” In both approaches electrical stimulation is continuously modulated in response to specific recorded patterns of activity, in real time and in a closed-loop fashion.

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