Unified Complex-valued Neural Network: A Magnitude-Phase Computational Model for Event-Driven Neuromorphic Learning
arXiv:2606.29099v1 Announce Type: cross Abstract: Artificial neural networks (ANN) provide accurate continuous-valued representation, whereas spiking neural networks (SNN) offer event-driven temporal processing, yet both paradigms face limitations when value encoding and timing dynamics must be learned within a single computational structure. This paper introduces a network based on Unified Complex-valued Neuron (UCN), a new neural computational model that integrates continuous activation and phase-driven event generation through an asymmetric complex-valued state. In the UCN, magnitude encode
This paper leverages recent advancements in understanding neuromorphic computing and complex-valued representations to address current limitations in AI models for event-driven learning.
This breakthrough offers a potential path to more efficient and biologically plausible AI models, bridging the gap between continuous and event-driven processing, which could lead to fundamental improvements in AI hardware and software.
Traditional neural network architectures may evolve to incorporate asymmetrical complex-valued states, enabling more integrated value encoding and timing dynamics within single computational structures.
- · Neuromorphic computing industry
- · AI hardware manufacturers
- · Machine learning researchers
- · Edge AI developers
- · Traditional ANN architectures with limited temporal processing
- · Computational paradigms reliant solely on continuous-valued deep learning
More powerful and energy-efficient AI models emerge, capable of processing information in a manner closer to biological brains.
This could lead to breakthroughs in areas like real-time autonomous systems, sensory processing, and low-power AI applications.
The development of truly 'event-driven' general AI, enabling new forms of artificial intelligence that are more reactive and adaptive to dynamic environments.
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Read at arXiv cs.AI