From ‘Understanding the Brain by Creating the Brain’ towards manipulative neuroscience

Abstract

Ten years have passed since the Japanese 'Century of the Brain' was promoted, and its most notable objective, the unique 'creating the brain' approach, has led us to apply a humanoid robot as a neuroscience tool. Here, we aim to understand the brain to the extent that we can make humanoid robots solve tasks typically solved by the human brain by essentially the same principles. I postulate that this 'Understanding the Brain by Creating the Brain' approach is the only way to fully understand neural mechanisms in a rigorous sense. Several humanoid robots and their demonstrations are introduced. A theory of cerebellar internal models and a systems biology model of cerebellar synaptic plasticity is discussed. Both models are experimentally supported, but the latter is more easily verifiable while the former is still controversial. I argue that the major reason for this difference is that essential information can be experimentally manipulated in molecular and cellular neuroscience while it cannot be manipulated at the system level. I propose a new experimental paradigm, manipulative neuroscience, to overcome this difficulty and allow us to prove cause-and-effect relationships even at the system level.

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

“…Even in animal studies, the most frequently used technique is examining the temporal correlation between neural activities and certain hypothetical computational variables proposed by the experimenters. The lack of experimental tools for examining cause-and-effect relationships between brain activity and the mind in systems neuroscience has severely constrained its progress and applicability to such practical problems as robotics or clinical issues [36]. To bridge this gap between major concepts and current technology, we developed a new technique that manipulates neural codes [38] by applying a novel onlinefeedback method that uses decoded fMRI signals.…”

“…The application of computational neuroscience models to robots opened up an approach called 'understanding the brain by creating the brain' [5,36], which was emphasized in the Japanese promotion of neuroscience about two decades ago [37]. To fully validate the computational feasibility and the efficacy of some theories, we need to apply them to realworld problems; we need an artificial brain and body.…”

“…We discuss our novel unified view of creating the brain and interacting with it approaches by introducing our new humanoid exoskeleton studies and our recently developed DecNef method. For the brainmotivated humanoid-motor-learning studies, we explored the creating the brain approach by developing a series of humanoid robots [36,41], including CB-i, a human-sized humanoid robot (figure 2a) [42]. As a new generation of humanoids, we are developing exoskeleton robots that can physically interact with the human body to assist user movements (figure 2b) [43,44].…”

Introduction. Japan: its tradition and hot topics in biological sciences

“…Osada et al (2008) exquisitely integrate primate neurophysiology with various related fields of neuroscience to shed light on higher cognitive functions. Kawato (2008) describes the construction of a high-tech humanoid robot based on a computational approach for understanding and replicating the information-processing systems of the brain. Mizuno et al (2008) bring a basic molecular biology technique to clinical neurology to uncover important mechanisms in familial Parkinson's disease, which have implications for its treatment.…”

Mean-Field Theory of Meta-learning

“…This dynamical view of the consensus between various machine learning algorithms is especially useful for artificial intelligence, or robotic applications, where adaptive behavior given by the integration of results from ensemble of ML methods. My model of learning is based on nonlocal cellular automata (CA) approach known from physics [30][31][32][33][34], with a wide range of applications [35][36][37][38][39][40][41][42][43]. The first statistical mechanics of opinion formation in groups of individuals was proposed by Lewenstein et al [44] on the class of models that were based on probabilistic cellular automata and social impact theory introduced by Latane [45,46].…”