Bioartificial Brains and Mobile Robots

Abstract

The growth in neuroscience discoveries continues to be explosive, with new frontiers being reached every year in the understanding of new principles of the central and peripheral nervous system (CNS and PNS), in the interplay between structure and function at different scales (from molecules to behavior), and with the introduction of new technologies for direct transfer of information between natural neuronal systems and artificial devices. Rapid advances in biomedical engineering and computer science are producing the methodologies required for predictive models of neural function that can interact with the brain in real time. The continuous achievements in microelectronics that allow ever-greater circuitry miniaturization together with increased speed and computational capacity are providing the next-generation hardware platforms for neuroprostheses and Brain Computer Interfaces (BCIs) or Brain Machine Interfaces (BMIs). On the other hand, in the last ten years, demonstrations of direct, real-time interfaces between living brain tissues and artificial devices, such as computer cursors, robots and mechanical prostheses, have opened new avenues for experimental and clinical investigations (Nicolelis and Lebedev, 2009). Interest in these BMIs has been kindled by the contribution that they may make to the treatment or rehabilitation of patients suffering from severe motor disabilities (Daly and Wolpaw, 2008; Hatsopoulos and Donoghue, 2009). When motor pathways fail, BMIs offer a physical bridge for movement intention to reach the external world (Donoghue, 2008). The first experimental demonstration that ensemble of cortical neurons could directly control a robotic manipulator was given in 1999 by the group of M. Nicolelis at the Duke University (USA) (Chapin et al., 1999). Since then, a continuous stream of research papers in the BMI field came out. Many groups (Nicolelis and Chapin, 2002; Taylor et al., 2002; Andersen et al., 2004; Schwartz, 2004; Hochberg et al., 2006; Velliste et al., 2008) have successfully shown the possibility of using cortical signals, recorded from different subjects, like rats, monkeys or humans, to move an artificial effector and, in these systems, the feedback was constituted by vision, tactile information and proprioception. BMIs have also been recently used as a tool for studying neural processing of information (Nicolelis and Lebedev, 2009). Nevertheless the limitations arising from the reduced possibility of a full control and observation of the system do not allow a systematic study on how information is processed and transmitted within and among cell-assemblies.