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An Actor-Critic architecture and simulator for goal-directed Brain-Machine Interfaces.

Babak Mahmoudi¹, Jose C Principe, Justin C Sanchez

¹Department of Biomedical Engineering, University of Florida, 130 BME Building, Gainesville, FL 32611, USA. babakm@ufl.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 8, 2009

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This study introduces a Brain-Machine Interface using the Perception-Action Cycle (PAC) and an Actor-Critic algorithm. The novel system accurately decodes neural motor commands for target navigation, achieving 98% accuracy despite noisy signals.

Area of Science:

Neuroscience
Computational Neuroscience
Biomedical Engineering

Background:

The Perception-Action Cycle (PAC) is crucial for goal-directed behavior, linking internal states to environmental actions.
Brain-Machine Interfaces (BMIs) aim to decode neural signals for controlling external devices.
Developing robust BMI architectures is essential for applications like navigation and motor control.

Purpose of the Study:

To design and analyze a novel BMI control architecture inspired by the PAC.
To utilize an Actor-Critic algorithm for decoding neural motor commands and goal information.
To evaluate the performance of the BMI decoder using a biologically realistic simulator.

Main Methods:

Developed a biologically realistic simulator for BMI performance analysis.

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Employed an Actor-Critic algorithm, a PAC-based framework, for neural decoding.

Varied signal-to-noise ratios to assess the impact on learning rate and initial conditions.

Main Results:

The Actor-Critic decoder demonstrated effective navigation to novel targets.
The system achieved 98% accuracy in target acquisition.
Performance was analyzed in terms of convergence and robustness to noisy neural and error signals.

Conclusions:

The PAC-inspired BMI architecture with an Actor-Critic algorithm shows significant promise for neural decoding.
The simulator provides valuable insights for parameter selection and understanding BMI behavior.
The developed BMI decoder is robust to noise, achieving high accuracy in motor control tasks.