Tadashi Yamazaki

Consensus Paper: The Cerebellum's Role in Movement and Cognition

While the cerebellums role in motor function is well recognized, the nature of its concurrent role in cognitive function remains considerably less clear. The current consensus paper gathers diverse views on a variety of important roles played by the cerebellum across a range of cognitive and emotional functions. This paper considers the cerebellum in relation to neurocognitive development, language function, working memory, executive function, and the development of cerebellar internal control models and reflects upon some of the ways in which better understanding the cerebellums status as a “supervised learning machine” can enrich our ability to understand human function and adaptation. As all contributors agree that the cerebellum plays a role in cognition, there is also an agreement that this conclusion remains highly inferential. Many conclusions about the role of the cerebellum in cognition originate from applying known information about cerebellar contributions to the coordination and quality of movement. These inferences are based on the uniformity of the cerebellums compositional infrastructure and its apparent modular organization. There is considerable support for this view, based upon observations of patients with pathology within the cerebellum.

Real-Time Simulation of Passage-of-Time Encoding in Cerebellum Using a Scalable FPGA-Based System

The cerebellum plays a critical role for sensorimotor control and learning. However, dysmetria or delays in movements onsets consequent to damages in cerebellum cannot be cured completely at the moment. Neuroprosthesis is an emerging technology that can potentially substitute such motor control module in the brain. A pre-requisite for this to become practical is the capability to simulate the cerebellum model in real-time, with low timing distortion for proper interfacing with the biological system. In this paper, we present a frame-based network-on-chip (NoC) hardware architecture for implementing a bio-realistic cerebellum model with ~ 100 000 neurons, which has been used for studying timing control or passage-of-time (POT) encoding mediated by the cerebellum. The simulation results verify that our implementation reproduces the POT representation by the cerebellum properly. Furthermore, our field-programmable gate array (FPGA)-based system demonstrates excellent computational speed that it can complete 1sec real world activities within 25.6 ms. It is also highly scalable such that it can maintain approximately the same computational speed even if the neuron number increases by one order of magnitude. Our design is shown to outperform three alternative approaches previously used for implementing spiking neural network model. Finally, we show a hardware electronic setup and illustrate how the silicon cerebellum can be adapted as a potential neuroprosthetic platform for future biological or clinical application.

2013 Special Issue: Transfer of memory trace of cerebellum-dependent motor learning in human prism adaptation: A model study

Accumulating experimental evidence suggests that the memory trace of ocular reflex adaptation is initially encoded in the cerebellar cortex, and later transferred to the cerebellar nuclei for consolidation through repetitions of training. However, the memory transfer is not well characterized in the learning of voluntary movement. Here, we implement our model of memory transfer to interpret the data of prism adaptation (Martin, Keating, Goodkin, Bastian, & Thach, 1996a, 1996b), assuming that the cerebellar nuclear memory formed by memory transfer is used for normal throwing. When the subject was trained to throw darts wearing prisms in 30-40 trials, the short-term memory for recalibrating the throwing direction by gaze would be formed in the cerebellar cortex, which was extinguished by throwing with normal vision in a similar number of trials. After weeks of repetitions of short-term prism adaptation, the long-term memory would be formed in the cerebellar nuclei through memory transfer, which enabled one to throw darts to the center wearing prisms without any training. These two long-term memories, one for throwing with normal vision and the other for throwing wearing prisms, are assumed to be utilized automatically under volitional control. Moreover, when the prisms were changed to new prisms, a new memory for adapting to the new prisms would be formed in the cerebellar cortex, just to counterbalance the nuclear memory of long-term adaptation to the original prisms in a similar number of trials. These results suggest that memory transfer may occur in the learning of voluntary movements.

A scalable FPGA-based cerebellum for passage-of-time representation

The cerebellum plays a critical role for sensorimotor control and learning. However dysmertria or delays in movements onsets consequent to damages in cerebellum cannot be cured completely at the moment. To foster a potential cure based on neuroprosthetic technology, we present a frame-based Network-on-Chip (NoC) hardware architecture for implementing a bio-realistic cerebellum model with 100,000 neurons, which has been used for studying timing control or passage-of-time (POT) encoding mediated by the cerebellum. The results demonstrate that our implementation can reproduce the POT functionality properly. The computational speed can achieve to 25.6 ms for simulating 1 sec real world activities. Furthermore, we show a hardware electronic setup and illustrate how the silicon cerebellum can be adapted as a potential neuroprosthetic platform for future biological or clinical applications.

A real-time silicon cerebellum spiking neural model based on FPGA

Sensorimotor control and learning require the function of sophisticated neural system. Cerebellum is one such brain region which comprises more than half of the total neuron population in the entire brain. Capable of simulating a bio-realistic cerebellum model provides important information for neuroscience and engineering. Here we present a Network-on-Chip (NoC) hardware architecture for implementing a bio-realistic cerebellum model of passage-of-time (POT) encoding with 100,000 neurons. The results demonstrate that our implementation can reproduce the POT functionality properly. The maximum computational speed can reach 25.6 ms for simulating 1 sec real world activities. Our silicon cerebellum can be readily interface with in vivo or in vitro experiment and be adapted as a potential neuroprosthetic platform for future biological or clinical applications.

A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing

While the anatomy of the cerebellar microcircuit is well-studied, how it implements cerebellar function is not understood. A number of models have been proposed to describe this mechanism but few emphasize the role of the vast network Purkinje cells (PKJs) form with the molecular layer interneurons (MLIs)—the stellate and basket cells. We propose a model of the MLI-PKJ network composed of simple spiking neurons incorporating the major anatomical and physiological features. In computer simulations, the model reproduces the irregular firing patterns observed in PKJs and MLIs in vitro and a shift toward faster, more regular firing patterns when inhibitory synaptic currents are blocked. In the model, the time between PKJ spikes is shown to be proportional to the amount of feedforward inhibition from an MLI on average. The two key elements of the model are: (1) spontaneously active PKJs and MLIs due to an endogenous depolarizing current, and (2) adherence to known anatomical connectivity along a parasagittal strip of cerebellar cortex. We propose this model to extend previous spiking network models of the cerebellum and for further computational investigation into the role of irregular firing and MLIs in cerebellar learning and function.

A Model of In vitro Plasticity at the Parallel Fiber-Molecular Layer Interneuron Synapses.

Theoretical and computational models of the cerebellum typically focus on the role of parallel fiber (PF)—Purkinje cell (PKJ) synapses for learned behavior, but few emphasize the role of the molecular layer interneurons (MLIs)—the stellate and basket cells. A number of recent experimental results suggest the role of MLIs is more important than previous models put forth. We investigate learning at PF—MLI synapses and propose a mathematical model to describe plasticity at this synapse. We perform computer simulations with this form of learning using a spiking neuron model of the MLI and show that it reproduces six in vitro experimental results in addition to simulating four novel protocols. Further, we show how this plasticity model can predict the results of other experimental protocols that are not simulated. Finally, we hypothesize what the biological mechanisms are for changes in synaptic efficacy that embody the phenomenological model proposed here.

Implementation of Learning Mechanisms on a Cat-Scale Cerebellar Model and Its Simulation

We have built a large-scale spiking network model of the cerebellum with 1 billion neurons on a supercomputer previously. The model, however, did not incorporate synaptic plasticity such as long-term depression and potentiation at parallel fiber-Purkinje cell synapses. In this study, we implemented them on the model. To test the learning capability, as a benchmark, we carried out simulation of eye movement reflex called gain adaptation of optokinetic response (OKR). The present model successfully reproduced the increase of firing rate modulation of a Purkinje cell during simulated OKR training, resulting in the increase of OKR gain. The model completed a 6 s simulation within 4.4 s, suggesting realtime simulation even with the learning mechanisms. These results suggest that the present cerebellar model can now perform reservoir computing, a supervised learning machine for spatiotemporal signals, with very large reservoir composed of 1 billion neurons.

Efficient Numerical Simulation of Neuron Models with Spatial Structure on Graphics Processing Units

Computer simulation of multi-compartment neuron models is difficult, because writing the computer program is tedious but complicated, and it requires sophisticated numerical methods to solve partial differential equations (PDEs) that describe the current flow in a neuron robustly. For this reason, dedicated simulation software such as NEURON and GENESIS have been used widely. However, these simulators do not support hardware acceleration using graphics processing units (GPUs). In this study, we implemented a conjugate gradient (CG) method to solve linear equations efficiently on a GPU in our own software. CG methods are known much faster and more efficient than the Gaussian elimination, when the matrix is huge and sparse. As a result, our software succeeded to carry out a simulation of Purkinje cells developed by De Schutter and Bower (1994) on a GPU. The GPU (Tesla K40c) version realized 3 times faster computation than that a single-threaded CPU version for 15 Purkinje cells.

An actor-critic model of saccade adaptation

The basal ganglia and the cerebellum are subcortical structures indispensable for voluntary motor control and motor learning. They are thought to perform reinforcement learning and supervised learning, respectively, and interact with each other [1]. Yet, how these structures and their learning mechanisms interact remains unknown. n nIn this study, we propose a model of interaction between the basal ganglia and the cerebellum for voluntary motor control and motor learning. We consider that the basal ganglia performs temporal difference (TD) learning. Specifically, according to the electrophysiological experiments [2], we assume that neurons in ventral tegmental area (VTA), a part of the basal ganglia, represent the value of delta, the prediction error of TD-learning. On the other hand, we consider that the cerebellum generates motor commands through supervised learning, for which the inferior olive (IO) provides teacher signals. Here, based on the anatomical findings of dopaminergic inputs from VTA to the IO [3], we assume that the cerebellum can receive the information of TD-prediction error as teacher signals via the IO. In the end, we propose a scheme of the interaction between the basal ganglia and the cerebellum as an actor-critic model in reinforcement learning (Figure u200b(Figure1A,1A, [4]). n n n nFigure 1 n n(A) Proposed scheme of interaction between the basal ganglia and the cerebellum as an actor-critic model. (B) Illustration of direction-adaptation of saccades. n n n nWe adopt the proposed scheme to double-step adaptation of saccade, which is voluntary eye movement and is mediated by a distributed network including both the basal ganglia and the cerebellum. A double-step saccade adaptation paradigm called direction adaptation goes as follows (Figure u200b(Figure1B).1B). Initially, the eye is fixated at the center position. Next, a target appears at a certain position, and the eye moves to the target (first saccade). When the saccade starts, the target is immediately removed and reappears to another position. In turn, the eye moves to the second target (corrective saccade). By repeating many trials, when the first target appears, the eye moves to the position of the expected second target. Our proposed model reproduces this direction-adaptation of saccades. These results suggest that the interaction between the basal ganglia and the cerebellum as an actor-critic model provides a powerful motor control and learning mechanism.