The Ability of MEAs Containing Cultured Neuroglial Networks to Process Information

Alberto      Alvarellos; Noha      Veiguela; Cristian      R. Munteanu; Julian      Dorado; Alejandro      Pazos; Ana      B. Porto-Pazos

Abstract

The study of the nervous system of human beings is an arduous task. The reasons are that it is very complex and it is internal to the organism. The nervous system is comprised not only of neuronal networks but also of different types of cells that constitute the glial system. Astrocytes, a type of glial cells, have traditionally been considered as passive, supportive cells. However, through the use of neuroscientific techniques, it has recently been demonstrated that astrocytes are actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. Also in recent studies employing artificial intelligence (AI) techniques, it has been shown that adding artificial astrocytes to Artificial Neural Networks (ANNs), the effectiveness of such networks in classification tasks is markedly improved. At present, the actual impact of astrocytes in neural network function is largely unknown. Therefore, our group is placing increasing emphasis on the study of the influence that astrocytes may have on brain information processing using a rather different perspective based on the use of multielectrode arrays (MEAs). This represents a hybrid approach given that it combines a biological component (cultured cells), hardware technology (MEAs), and AI (computer simulations based on AI techniques to control the system). With this in mind, the objective of this paper is to present a review of the state of the art in the use of MEAs containing nerve cells. This review is intended as a preliminary theoretical analysis on the suitability of these devices to achieve the aforementioned future goal of fusing bioinformatics, micro/nano-technologies, and AI techniques to study these complex systems.

Keywords: Astrocyte, cells culture, MEA multielectrode array, neuroglial networks, Artificial Neural Networks, micro/nano-technologies, Electroencephalography, Magnetoencephalography, Functional magnetic resonance imaging, Electromyography