Introduction

While proven incredibly valuable for numerous applications, ranging from robotics and medicine to economy and computational cognition, artificial intelligence (AI), in many ways, is nullified when compared with biological intelligence. For example, the Cockatiel Parrot can navigate and learn unknown environments at 35 km/hr, manipulate objects, and use human language, with a brain consuming merely 50 mW of power. Comparably, an autonomous drone with comparable mass and size consumes 5,000 mW of power while being limited to pretrained flying in a known environment with limited capacity for real-time learning. Deep learning with artificial neural networks (ANNs) is a predominant method in AI. ANNs, however, are limited to slow generalization with massive data, offline training, and batched optimization . In contrast, biological learning is characterized by fast generalization and online incremental learning . Spiking neural networks (SNNs) closely follow the computational characteristics of biological learning and stand as a new frontier of AI . SNNs comprise densely connected, physically implemented “silicon neurons,” which communicate with spikes . SNNs were realized in various hardware designs, including IBM’s TrueNorth , Intel’s Loihi , the NeuroGrid , the SpiNNaker , and the BrainDrop .
Programming a neuromorphic system is a challenging endeavor, as it requires the ability to represent data, manipulate and retrieve it with spike-based computing. One theoretical framework designed to address these challenges is the neural engineering framework (NEF) . NEF brings forth a theoretical framework for representing high-dimensional mathematical constructs with spiking neurons for the implementation of functional large-scale neural networks. It was used to design a broad spectrum of neuromorphic systems ranging from vision processing to robotic control . NEF was compiled to work on each of the neuromorphic hardware designs listed above via Nengo, a Python-based ”neural compiler,” which translates high-level descriptions to low-level neural models .
One of the most promising directions for neuromorphic systems is real-time continuous learning . A neuromorphic continuous learning framework was recently shown to handle temporal dependencies spanning 100,000 time steps, converge rapidly, and use few internal state variables to learn complex functions spanning long time windows, outperforming state-of-the-art ANNs . Neuromorphic systems, however, can realize their full potentials only when deployed on neuromorphic hardware. While NEF was previously adopted for both digital and hybrid (analog/digital) neuromorphic circuitry , we propose a detailed, fully analog design for NEF-based online learning. Our circuit design utilizes OZ, an analog implementation of the NEF-inspired spiking neuron we recently proposed . OZ is a programmable spiking neuron that can support arbitrary response dynamics. We used online learning to represent high-dimensional mathematical constructs (encoding and decoding with spiking neurons), transform one neuromorphic representation to another, and implement complex dynamical behaviors. We further designed a circuit emulator, allowing the evaluation of our electrical designs on a large scale. We used the emulator to demonstrate adaptive learning-based control of a six-degree-of-freedom robotic arm. Our design supports the basic three fundamental principles of NEF (representation, transformation, and dynamics) and can therefore be of potential use for various neuromorphic systems.