Abstract
Brain-inspired hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for artificial intelligence. The Neural Engineering Framework (NEF) brings forth a theoretical framework for representing high-dimensional mathematical constructs with spiking neurons to implement functional large-scale neural networks. Here, we present OZ, a programable analog implementation of NEF-inspired spiking neurons. OZ neurons can be dynamically programmed to feature varying high-dimensional response curves with positive and negative encoders for a neuromorphic distributed representation of normalized input data. Our hardware design demonstrates full correspondence with NEF across firing rates, encoding vectors, and intercepts. OZ neurons can be independently configured in real-time to allow efficient spanning of a representation space, thus using fewer neurons and therefore less power for neuromorphic data representation.
Highlights
Albeit artificial intelligence has emerged as the focal point for countless state-of-the-art developments, in many ways, it is nullified when compared with biological intelligence, in terms of energy efficiency
Neural Engineering Framework (NEF)-inspired neurons were implemented on a digital Field-Programmable Gate Array (FPGA)-circuit and used for pattern recognition (Wang et al, 2017)
The simulator is based on the open-sourced SPICE framework (Nagel and Pederson, 1973), which utilizes the numerical Newton–Raphson method to analyze non-linear systems (Nichols et al, 1994)
Summary
Albeit artificial intelligence has emerged as the focal point for countless state-of-the-art developments, in many ways, it is nullified when compared with biological intelligence, in terms of energy efficiency. NEF is one of the most utilized theoretical frameworks in neuromorphic computing It was adopted for various neuromorphic tasks, ranging from neuro-robotics (DeWolf et al, 2020) to high-level cognition (Eliasmith et al, 2012). NEF-inspired neurons were implemented on a digital Field-Programmable Gate Array (FPGA)-circuit and used for pattern recognition (Wang et al, 2017). It is not clear if such implementations can approximate the density, energy efficiency, and resilience of large-scale neuromorphic systems (Indiveri et al, 2011). Current analog implementations of NEF-inspired neurons rely on the circuit fabrication’s stochasticity to constitute the variational activity patterns required to span a representation space. OZ utilizes several of the most wellknown building blocks for analog spiking neurons to provide a design with a programable high-dimensional response curve and a temporally integrated output
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