Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Abstract

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the 'understanding the brain by creating the brain' approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain-machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers

“…As such, this method provides a means to aid participants to learn to induce brain activity toward a desired pattern of neural activity relying only on participants’ own endogenous factors. Among several neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy, fMRI-based neurofeedback has attracted considerable attention for its potential as a novel method of therapeutic treatment in clinical neuroscience ( Fovet et al, 2015 ; Morimoto and Kawato, 2015 ). To date, several studies have applied fMRI-based neurofeedback methods to psychiatric patients by training them to upregulate or downregulate the level of activation in single or multiple regions-of-interest (ROI) ( Sitaram et al, 2016 ).…”

Applications and development of artificial intelligence system from the perspective of system science: A bibliometric review

“…In this system, intelligent robots with perception, thinking and behaviour cooperate. Simultaneously, combined with the robot technology of brain science (Morimoto & Kawato, 2015), humans participate in the cooperation of intelligent systems and enhance the social nature of AI systems.…”

Hierarchical Control of Visually-Guided Movements in a 3D-Printed Robot Arm

“…Additionally, as claimed by Brooks (1992), there is a near certainty that programs that work well on simulated robots completely fail in real robots because of the differences between simulated and real-world sensing and actuation. Moreover, designing and building robots that work decently can generate insights about the function of structures in the nervous system that produce analogous behaviors in living organisms (Floreano et al, 2014;Morimoto and Kawato, 2015).…”