Speakers
Description
We present a biologically grounded spiking closed-loop controller for investigating adaptive motor control. The controller was designed to actuate a robotic arm through the interaction of multiple brain-inspired modules, including a premotor planning module for trajectory generation, a primary motor cortex module for motor-command production and feedback correction, state estimation, and cerebellar forward and inverse internal models. In this framework, motor control emerges from the coordinated interaction between planning, cortical transformation of movement goals into motor commands, sensory feedback, and cerebellar prediction.
The premotor module generates desired joint trajectories and provides the reference signal. The primary motor cortex module transforms these trajectories into motor commands. This module includes a recurrent spiking network trained through eligibility-propagation (e-prop) learning, a biologically plausible mechanism in which synaptic updates depend on local activity traces and error-related learning signals.
The cerebellar component is composed of two biophysically grounded spiking microcircuits implementing complementary internal models, with online learning. The forward model receives efference copies of motor commands and learns to predict their sensory consequences, supporting faster and more reliable state estimation under delayed feedback. The inverse model receives desired trajectory and current state estimates, and contributes to refining motor output. Both cerebellar networks rely on biologically inspired plasticity driven by error signals.
The controller was tested with a virtual single-joint robotic arm simulated in a physics-based environment and interfaced (throughout the NeuroRobotics Platform) with neural simulations implemented in NEST simulator. Results show that the baseline controller (without plastic cerebellar models) can generate stable movements using delayed sensory feedback, although state estimation remains temporally lagged. The addition of cerebellar prediction improves state estimation, while inverse-model adaptation supports motor refinement once reliable state information is available. Preliminary perturbation experiments further highlight the potential of the framework for studying robustness and adaptive learning under altered environment/plant dynamics.
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