Audio Localization for Robots Using Parallel Cerebellar Models (2018)
Journal Article
Baxendale, M., Pearson, M., Nibouche, M., Secco, E., & Pipe, T. (2018). Audio Localization for Robots Using Parallel Cerebellar Models. IEEE Robotics and Automation Letters, 3(4), 3185-3192. https://doi.org/10.1109/LRA.2018.2850447

© 2016 IEEE. A robot audio localization system is presented that combines the outputs of multiple adaptive filter models of the Cerebellum to calibrate a robot's audio map for various acoustic environments. The system is inspired by the MOdular Selec... Read More about Audio Localization for Robots Using Parallel Cerebellar Models.

Self-adaptive context aware audio localization (2017)
Book Chapter
Baxendale, M., Pearson, M. J., Nibouche, M., Secco, M., & Pipe, A. G. (2017). Self-adaptive context aware audio localization. In C. Lekakou, Y. Jin, S. Fallah, & G. Yang (Eds.), Towards Autonomous Robotic Systems (66-78). LNCS 10454: Springer link

An audio sensor system is presented that uses multiple cere- bellar models to determine the acoustic environment in which a robot is operating, allowing the robot to select appropriate models to calibrate its audio-motor map for the detected environ... Read More about Self-adaptive context aware audio localization.

Implementing spiking neural networks for real-time signal-processing and control applications: A model-validated FPGA approach (2007)
Journal Article
Pearson, M., Pipe, A. G., Mitchinson, B., Gurney, K., Melhuish, C., Gilhespy, I., & Nibouche, M. (2007). Implementing spiking neural networks for real-time signal-processing and control applications: A model-validated FPGA approach. IEEE Transactions on Neural Networks, 18(5), 1472-1487. https://doi.org/10.1109/TNN.2007.891203

In this paper, we present two versions of a hardware processing architecture for modeling large networks of leaky-integrate-and-fire (LIF) neurons; the second version provides performance enhancing features relative to the first. Both versions of the... Read More about Implementing spiking neural networks for real-time signal-processing and control applications: A model-validated FPGA approach.