Avi Hazan1, Elishai Ezra Tsur1*
1 Neuro-Biomorphic Engineering Lab, Department of
Mathematics and Computer Science, The Open University of Israel,
Ra’anana, Israel
* Correspondence: elishai@NBEL-lab.com
Abstract
Neuromorphic hardware designs realize neural principles in electronics
to provide high-performing, energy-efficient frameworks for machine
learning. Here, we propose a neuromorphic analog design for continuous
real-time learning. Our hardware design realizes the underlying
principles of the neural engineering framework (NEF). NEF brings forth a
theoretical framework for the representation and transformation of
mathematical constructs with spiking neurons. Thus, providing efficient
means for neuromorphic machine learning and the design of intricate
dynamical systems. Our analog circuit design implements the neuromorphic
prescribed error sensitivity (PES) learning rule with OZ neurons. OZ is
an analog implementation of a spiking neuron, which was shown to have
complete correspondence with NEF across firing rates, encoding vectors,
and intercepts. We demonstrate PES-based neuromorphic representation of
mathematical constructs with varying neuron configurations, the
transformation of mathematical constructs, and the construction of a
dynamical system with the design of an inducible leaky oscillator. We
further designed a circuit emulator, allowing the evaluation of our
electrical designs on a large scale. We used the circuit emulator in
conjunction with a robot simulator to demonstrate adaptive
learning-based control of a robotic arm with six degrees of freedom.