From 'understanding the brain by creating the brain' towards manipulative neuroscience

The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach

Motor learning involves a widespread brain network including the basal ganglia, cerebellum, motor cortex, and brainstem. Despite its importance, little is known about how this network learns motor tasks and which role different parts of this network take. We designed a systems-level computational model of motor learning, including a cortex-basal ganglia motor loop and the cerebellum that both determine the response of central pattern generators in the brainstem. First, we demonstrate its ability to learn arm movements toward different motor goals. Second, we test the model in a motor adaptation task with cognitive control, where the model replicates human data. We conclude that the cortex-basal ganglia loop learns via a novelty-based motor prediction error to determine concrete actions given a desired outcome, and that the cerebellum minimizes the remaining aiming error.

Going Beyond the "Synthetic Method": New Paradigms Cross-Fertilizing Robotics and Cognitive Neuroscience

In so-called ethorobotics and robot-supported social cognitive neurosciences, robots are used as scientific tools to study animal behavior and cognition. Building on previous epistemological analyses of biorobotics, in this article it is argued that these two research fields, widely differing from one another in the kinds of robots involved and in the research questions addressed, share a common methodology, which significantly differs from the "synthetic method" that, until recently, dominated biorobotics. The methodological novelty of this strategy, the research opportunities that it opens, and the theoretical and technological challenges that it gives rise to, will be discussed with reference to the peculiarities of the two research fields. Some broad methodological issues related to the generalization of results concerning robot-animal interaction to theoretical conclusions on animal-animal interaction will be identified and discussed.

The evolution of theory of mind (ToM) within the evolution of cerebellar sequence detection in stone-tool making and language: implications for studies of higher-level cognitive functions in degenerative cerebellar atrophy

INTRODUCTION

Within the context of Clausi, Olivito, Lupo, Siciliano, Bozzali and Leggio's (Cell Neurosci 12:510, 2019) insightful study of how prediction of theory of mind (ToM) is compromised in degenerative cerebellar atrophy, this article describes how prediction can also be understood as the cerebro-cerebellar system's capacity to rapidly shift attention to manipulate cause-and-effect relationships embedded in language.

METHOD

The evolution of the capacity of ToM is described within the evolution of stone-tool making, language, and the origin of the phonological loop in verbal working memory. Specifically, it is argued that this evolutionary framework offers a way to get further inside the prediction process by illuminating how sub-vocal speech evolved during stone-tool evolution due to its adaptive refinement of early human ability to manipulate and hold in memory progressively more detailed cause-and-effect relationships in the origin of verbal working memory.

CONCLUSION

The addition of sub-vocal speech/cause-and-effect relationship to the analysis of prediction provides an evolutionary model of the mechanisms of ToM, which, in turn, brings forward additional cerebro-cerebellar mechanisms which can (1) further support Clausi, Olivito, Lupo et al's findings and (2) shed light on additional mechanisms that might further clarify what might be behind cerebellar dysfunction in the construction of ToM. Problems encountered by cerebellar degenerative atrophy patients with the Faux pas test and Advanced ToM task with unexpected events may stem from a combination of an inability (1) of their cerebellar internal models to rapidly switch attention among cause-and-effect elements of the stories and (2) to extend cerebellar internal models to the prediction of the resulting similar but unexpected events. That is, with both (1) and (2) occurring at the same time, alternative meanings of causes and effects might be missed in both automatic and consciously manipulated sub-vocal verbal working memory. A method to measure sub-vocal speech in this context is suggested.

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

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.

Adaptive filters and internal models: multilevel description of cerebellar function

Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles.

[A new neuroscientific approach using decoded neurofeedback (DecNef)]

Neurofeedback is defined as a method to read out information from the brain and feed the information back to the brain. This technology has developed in the past ten years and attracted considerable attention as potential treatments for rehabilitation and psychiatric disease. We recently invented the decoded neurofeedback (DecNef) method, a new neurofeedback technique using functional magnetic resonance imaging. With DecNef, subjects were trained to regulate their brain activation pattern in a specific area and lead the pattern to a target state. We found that the DecNef training for several days leads to perceptual improvement that corresponds to the induced target state. DecNef enables us to test cause-and-effect relationships between neural activation in a target brain area and changes in perception, cognition, and behavior. In this sense, this method can be a powerful tool in cognitive and systems neuroscience. In addition, the concept of DecNef, leading a neural activation pattern to a specific state, can be applied for a variety of fields including engineering and medical treatment.

Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation

It is controversial whether the adult primate early visual cortex is sufficiently plastic to cause visual perceptual learning (VPL). The controversy occurs partially because most VPL studies have examined correlations between behavioral and neural activity changes rather than cause-and-effect relationships. With an online-feedback method that uses decoded functional magnetic resonance imaging (fMRI) signals, we induced activity patterns only in early visual cortex corresponding to an orientation without stimulus presentation or participants' awareness of what was to be learned. The induced activation caused VPL specific to the orientation. These results suggest that early visual areas are so plastic that mere inductions of activity patterns are sufficient to cause VPL. This technique can induce plasticity in a highly selective manner, potentially leading to powerful training and rehabilitative protocols.

Cerebellar-inspired adaptive control of a robot eye actuated by pneumatic artificial muscles

In this paper, a model of cerebellar function is implemented and evaluated in the control of a robot eye actuated by pneumatic artificial muscles. The investigated control problem is stabilization of the visual image in response to disturbances. This is analogous to the vestibuloocular reflex (VOR) in humans. The cerebellar model is structurally based on the adaptive filter, and the learning rule is computationally analogous to least-mean squares, where parameter adaptation at the parallel fiber/Purkinje cell synapse is driven by the correlation of the sensory error signal (carried by the climbing fiber) and the motor command signal. Convergence of the algorithm is first analyzed in simulation on a model of the robot and then tested online in both one and two degrees of freedom. The results show that this model of neural function successfully works on a real-world problem, providing empirical evidence for validating: 1) the generic cerebellar learning algorithm; 2) the function of the cerebellum in the VOR; and 3) the signal transmission between functional neural components of the VOR.

The neuroscience of primate intellectual evolution: natural selection and passive and intentional niche construction

We trained Japanese macaque monkeys to use tools, an advanced cognitive function monkeys do not exhibit in the wild, and then examined their brains for signs of modification. Following tool-use training, we observed neurophysiological, molecular genetic and morphological changes within the monkey brain. Despite being 'artificially' induced, these novel behaviours and neural connectivity patterns reveal overlap with those of humans. Thus, they may provide us with a novel experimental platform for studying the mechanisms of human intelligence, for revealing the evolutionary path that created these mechanisms from the 'raw material' of the non-human primate brain, and for deepening our understanding of what cognitive abilities are and of those that are not uniquely human. On these bases, we propose a theory of 'intentional niche construction' as an extension of natural selection in order to reveal the evolutionary mechanisms that forged the uniquely intelligent human brain.

Brain controlled robots

In January 2008, Duke University and the Japan Science and Technology Agency (JST) publicized their successful control of a brain-machine interface for a humanoid robot by a monkey brain across the Pacific Ocean. The activities of a few hundred neurons were recorded from a monkey's motor cortex in Miguel Nicolelis's lab at Duke University, and the kinematic features of monkey locomotion on a treadmill were decoded from neural firing rates in real time. The decoded information was sent to a humanoid robot, CB-i, in ATR Computational Neuroscience Laboratories located in Kyoto, Japan. This robot was developed by the JST International Collaborative Research Project (ICORP) as the "Computational Brain Project." CB-i's locomotion-like movement was video-recorded and projected on a screen in front of the monkey. Although the bidirectional communication used a conventional Internet connection, its delay was suppressed below one over several seconds, partly due to a video-streaming technique, and this encouraged the monkey's voluntary locomotion and influenced its brain activity. This commentary introduces the background and future directions of the brain-controlled robot.