ArticleFrontiers in neuroscience2021
Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Chaotic dynamics analysis and digital hardware design of the Izhikevich neuron model.Scientific reports · 2025Article
- Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding.Frontiers in neuroscience · 2025Article
- Article
- Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics.Patterns (New York, N.Y.) · 2022Article
- Adaptive control of a wheelchair mounted robotic arm with neuromorphically integrated velocity readings and online-learning.Frontiers in neuroscience · 2022Article
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2 authors.
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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.
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