Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

by   Sebastian Glatz, et al.

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available.


Towards neuromorphic control: A spiking neural network based PID controller for UAV

In this work, we present a spiking neural network (SNN) based PID contro...

Robust robotic control on the neuromorphic research chip Loihi

Neuromorphic hardware has several promising advantages compared to von N...

Spiking Neural Network based Region Proposal Networks for Neuromorphic Vision Sensors

This paper presents a three layer spiking neural network based region pr...

LIPSFUS: A neuromorphic dataset for audio-visual sensory fusion of lip reading

This paper presents a sensory fusion neuromorphic dataset collected with...

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks

Inspired by the connectivity mechanisms in the brain, neuromorphic compu...

Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian

Developing intelligent neuromorphic solutions remains a challenging ende...

Time-coded Spiking Fourier Transform in Neuromorphic Hardware

After several decades of continuously optimizing computing systems, the ...

Please sign up or login with your details

Forgot password? Click here to reset