Computational Model for Variable Time Delay Estimation in Sensorimotor Systems
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Solution Overview
Problem
Current time-delay estimation techniques are inadequate for simulating biological sensorimotor control systems, which experience variability, nonlinearity, and uncertainty, and lack predictive capabilities for compensating delays in motor control.
Innovation Solution
A computational model that estimates variable time delays and predicts sensory states in real-time, using a time-delay estimator circuit and state predictor to simulate the brain's ability to compensate for delays, as demonstrated in the horizontal Vestibulo-Ocular Reflex system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current time-delay estimation techniques are used, then linear systems with constant or random time delays can be analyzed, but biological sensorimotor systems with variability, nonlinearity, and uncertainty cannot be accurately simulated
Solution Approach 1:
The patent applies dynamics by transitioning from static or simple random delay models to dynamic time-varying delay models that capture the nonlinearity and variability of biological sensorimotor systems. The computational model estimates time delays that change over time rather than remaining constant, allowing accurate representation of physiological conditions.
Solution Approach 2:
The patent changes the parameters of the delay estimation approach by moving from fixed or statistically simple delay parameters to time-varying, state-dependent delay parameters. This allows the model to adapt to changing system conditions and accurately represent the complex behavior of biological systems under various input conditions.
2Measurement precision
If traditional delay estimation methods are applied, then past or current delay values can be determined, but future sensory states cannot be predicted for compensating delays in motor control
Solution Approach 1:
The patent applies preliminary action by predicting future sensory states before the actual motor output occurs. The computational model uses current and past state information to estimate what the sensory state will be in the future, allowing the system to compensate for delays proactively rather than reactively.
Solution Approach 2:
The patent implements feedback by using the predicted future sensory states to adjust and optimize motor control commands. The model continuously compares predicted states with actual states and uses this information to refine delay compensation strategies, creating a closed-loop predictive control system.
3Adaptability or versatility
If complex Hilbert-Huang Transform-based methods are used for delay estimation, then practical applicability to motor control improves, but computational complexity increases
Solution Approach 1:
The patent extracts the essential delay estimation function from complex transform methods and implements it through a streamlined computational model. By focusing on the core functionality of estimating time-varying delays and predicting states, the model achieves practical applicability without the excessive computational burden of full Hilbert-Huang Transform implementations.
Data Source
AI summary
Computational models and methods and systems of using the model to estimate variable time delay in the sensorimotor system of a subject are provided. The computational model can estimate variable time delays in the sensorimotor system, predict sensory states based on delayed sensory feedback, and/or control the system in real time. The subject can be a human or a primate. Simulation experiments can show how the model can explain a sensorimotor system's ability to compensate for delays during online learning and control.