Integrating spiking neural networks and deep learning algorithms on the neurorobotics platform

Stentiford, Rachael; Knowles, Thomas C; Feldoto, Benedikt; Ergene, Deniz; Morin, Fabrice O; Pearson, Martin J

Abstract

We present a neurorobotic model that can associate self motion (odometry) with vision to correct for drift in a spiking neural network model of head direction based closely on known rodent neurophys-iology. We use a deep predictive coding network to learn the generative model of representations of head direction from the spiking neural network to views of naturalistic scenery from a simulated mobile robot. This model has been deployed onto the Neurorobotics Platform of the Human Brain Project which allows full closed loop experiments with spiking neu-ral network models simulated using NEST, a biomimetic robot platform called WhiskEye in Gazebo robot simulator, and a Deep Predictive Coding network implemented in Tensorflow.

Citation

Stentiford, R., Knowles, T. C., Feldoto, B., Ergene, D., Morin, F. O., & Pearson, M. J. (2022, July). Integrating spiking neural networks and deep learning algorithms on the neurorobotics platform. Paper presented at Living Machines 2022, Case Western Reserve University, Cleveland, Ohio, United States

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: Living Machines 2022
• Conference Location: Case Western Reserve University, Cleveland, Ohio, United States
• Start Date: Jul 19, 2022
• End Date: Jul 22, 2022
• Deposit Date: Nov 11, 2022
• Publicly Available Date: Nov 11, 2022
• Keywords: Neurorobotics Platform, Predictive coding, Spiking Neural Network, pyNEST, NRP, WhiskEye, Head Direction
• Public URL: https://uwe-repository.worktribe.com/output/10133695
• Related Public URLs: https://livingmachinesconference.eu/2022/