A Forward Model at Purkinje Cell Synapses Facilitates Cerebellar Anticipatory Control

  title={A Forward Model at Purkinje Cell Synapses Facilitates Cerebellar Anticipatory Control},
  author={Ivan Herreros-Alonso and Xerxes D. Arsiwalla and Paul Verschure},
How does our motor system solve the problem of anticipatory control in spite of a wide spectrum of response dynamics from different musculo-skeletal systems, transport delays as well as response latencies throughout the central nervous system? To a great extent, our highly-skilled motor responses are a result of a reactive feedback system, originating in the brain-stem and spinal cord, combined with a feed-forward anticipatory system, that is adaptively fine-tuned by sensory experience and… 

Cerebellar-inspired learning rule for gain adaptation of feedback controllers

This work frames a model-reference adaptive control problem and derives an adaptive control scheme treating the gains of a feedback controller as if they were the weights of an adaptive linear unit, and demonstrates that the approach of controlling plasticity with a forward model of the subsystem controlled can provide a solution to a wide set of adaptive control problems.

A Cerebellum-Inspired Learning Approach for Adaptive and Anticipatory Control

A novel bio-inspired modular control architecture that merges a recurrent cerebellar-like loop for adaptive control and a Smith predictor controller is proposed to provide accurate anticipatory corrections to the generation of the motor commands in spite of sensory delays and to validate the robustness of the proposed control method to input and physical dynamic changes.

The Perceptual Shaping of Anticipatory Actions

This work proposes and validate the alternative hypothesis that anticipatory control can be realized through a cascade of purely sensory predictions that drive the motor system, reflecting the causal sequence of the perceptual events preceding the error.

A cerebellar-based solution to the nondeterministic time delay problem in robotic control

A Cerebellar-like spiking neural network (SNN) controller is implemented that is adaptive, compliant, and robust to variable sensorimotor delays by replicating the cerebellar mechanisms that embrace the presence of biological delays and allow motor learning and adaptation.

Adaptively Learning Levels of Coordination from One's, Other's and Task Related Errors Through a Cerebellar Circuit: A Dual Cart-Pole Setup

The results confirm experimentally that anticipating the error in the task including inputs extracted from the behavior of the other, further improves precision in the realization.

Robust Postural Stabilization with a Biomimetic Hierarchical Control Architecture

It is proved that the proposed hierarchical control architecture can deal with different types of alterations in the causal structure of the environment, therefore extending the limits of performance.

Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus

Control Architecture for Human-Like Motion With Applications to Articulated Soft Robots

A control framework that ensures natural movements in articulated soft robots, implementing specific functionalities of the human central nervous system, i.e., learning by repetition, after-effect on known and unknown trajectories, anticipatory behavior, its reactive re-planning, and state covariation in precise task execution is introduced.

Modeling the formation of social conventions from embodied real-time interactions

This work proposes a computational model that matches human behavioral data in a social decision-making game that was analyzed both in discrete-time and continuous-time setups and shows that CRL is able to reach human-level performance on standard game-theoretic metrics such as efficiency in acquiring rewards and fairness in reward distribution.

Modeling the Formation of Social Conventions in Multi-Agent Populations

This work proposes a novel Control-based Reinforcement Learning architecture (CRL) that can account for the acquisition of social conventions in multi-agent populations that are solving a benchmark social decision-making problem.



A cerebellar model for predictive motor control tested in a brain-based device.

A computer model based on the anatomy and dynamics of the cerebellum was constructed and it was found that the Cerebellar circuit selectively responded to motion cues in specific receptive fields of simulated middle temporal visual areas.

A hierarchical neural-network model for control and learning of voluntary movement

A hierarchical neural network model which accounts for the learning and control capability of the CNS and provides a promising parallel-distributed control scheme for a large-scale complex object whose dynamics are only partially known is proposed.

The cerebellum in action: a simulation and robotics study

A computational model is presented pursuing whether and how the suggested underlying mechanisms could give rise to behavioural phenomena in cerebellar‐mediated conditioning and it is demonstrated that the model supports adaptively timed responses under real‐world conditions.

Learning to predict the future: the cerebellum adapts feedforward movement control

  • A. Bastian
  • Psychology, Biology
    Current Opinion in Neurobiology
  • 2006

Adaptive filter model of the cerebellum

The Marr-Albus model of the cerebellum has been reformulated with linear system analysis and will give an account for the phenomena which have been termed “cerebellar compensation”.

Prediction of complex two-dimensional trajectories by a cerebellar model of smooth pursuit eye movement.

A neural network model based on the anatomy and physiology of the cerebellum is presented that can generate both simple and complex predictive pursuit, while also responding in a feedback mode to visual perturbations from an ongoing trajectory, which suggests that both the model and the eye make short-term predictions about future events to compensate for visual feedback delays in receiving information about the direction of a target moving along a changing trajectory.

Neuronlike adaptive elements that can solve difficult learning control problems

It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem and the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.

Visual motion processing and sensory-motor integration for smooth pursuit eye movements.

It is argued that the visual inputs are transmitted through a simple sensory motor interface in the pons, to the efferent limb in the brain stem and cerebellum, and evidence is presented that the velocity memory is provided, at least in part, by eye velocity positive feedback between the flocculus of the Cerebellum and thebrain stem.