Dynamically Repairing and Replacing Neural Networks: Using Hybrid Computational and Biological Tools

Author

Sanchez, Justin C. ; Lytton, William W. ; Carmena, Jose M. ; Principe, Jose C. ; Fortes, Jose ; Barbour, Randall L. ; Francis, Joseph T.

Author_Institution

Depts. of Biomed. Eng., Univ. of Miami, Coral Gables, FL, USA

Volume

3

Issue

1

fYear

2012

Firstpage

57

Lastpage

59

Abstract

The debilitating effects of injury to the nervous system can have a profound effect on daily life activities of the injured person [1]. In this article, we present a project overview in which we are utilizing computational and biological principles, along with simulation and experimentation, to create a realistic computational model of natural and injured sensorimotor control systems. Through the development of hybrid in silico/biological coadaptive symbiotic systems, the goal is to create new technologies that yield transformative neuroprosthetic rehabilitative solutions and a new test bed for the development of integrative medical devices for the repair and enhancement of biological systems.

Keywords

biocontrol; biocybernetics; medical computing; neural nets; neurophysiology; patient rehabilitation; prosthetics; experimentation; hybrid computational-biological tools; hybrid in silico-biological coadaptive symbiotic systems; injured sensorimotor control systems; integrative medical devices; natural sensorimotor control systems; neural network dynamical repair; neural network dynamical replacement; neuroprosthetic rehabilitative solutions; realistic computational model; simulation; Biological system modeling; Injuries; Nervous system; Neurocontrollers; Neurophysiology; Neuroscience; Patient rehabilitation; Animals; Humans; Models, Biological; Neural Networks (Computer); Software;

fLanguage

English

Journal_Title

Pulse, IEEE

Publisher

ieee

ISSN

2154-2287

Type

jour

DOI

10.1109/MPUL.2011.2175640

Filename

6153127