Robot-Embodied Neuronal Networks as an Interactive Model of Learning



Abraham M Shultz1, Sangmook Lee2, Mary Guaraldi2, Thomas B. Shea2, *, Holly A. Yanco1
1 Robotics Laboratory, Department of Computer Science, USA
2 Laboratory for Neuroscience, Department of Biological Sciences University of Massachusetts Lowell, Lowell, MA 01854, USA


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© 2017 Shultz et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Laboratory for Neuroscience, Department of Biological Sciences University of Massachusetts Lowell, Lowell, MA 01854, USA, Tel: 978-934-2881; Fax: 978-934-3044; E-mail: thomas_shea@uml.edu


Abstract

Background and Objective:

The reductionist approach of neuronal cell culture has been useful for analyses of synaptic signaling. Murine cortical neurons in culture spontaneously form an ex vivo network capable of transmitting complex signals, and have been useful for analyses of several fundamental aspects of neuronal development hitherto difficult to clarify in situ. However, these networks lack the ability to receive and respond to sensory input from the environment as do neurons in vivo. Establishment of these networks in culture chambers containing multi-electrode arrays allows recording of synaptic activity as well as stimulation.

Method:

This article describes the embodiment of ex vivo neuronal networks neurons in a closed-loop cybernetic system, consisting of digitized video signals as sensory input and a robot arm as motor output.

Results:

In this system, the neuronal network essentially functions as a simple central nervous system. This embodied network displays the ability to track a target in a naturalistic environment. These findings underscore that ex vivo neuronal networks can respond to sensory input and direct motor output.

Conclusion:

These analyses may contribute to optimization of neuronal-computer interfaces for perceptive and locomotive prosthetic applications. Ex vivo networks display critical alterations in signal patterns following treatment with subcytotoxic concentrations of amyloid-beta. Future studies including comparison of tracking accuracy of embodied networks prepared from mice harboring key mutations with those from normal mice, accompanied with exposure to Abeta and/or other neurotoxins, may provide a useful model system for monitoring subtle impairment of neuronal function as well as normal and abnormal development.

Keywords: Multi-electrode array, Neuronal network, Cortical neuronal culture, Learning, Plasticity, Sensory input, Cybernetics, Manus robot arm.