Neuromorphic Intelligence

2025-09-15

AI Summary

Brain-inspired computing lacks a unified interdisciplinary theoretical framework. Method: This work proposes a novel modeling paradigm centered on dynamical systems theory, treating noise explicitly as a learnable resource. It integrates differential genetic programming to automatically discover adaptive dynamical models, couples neuroscience-inspired architectures with physically realizable material substrates, and establishes an end-to-end pathway for neuromorphic intelligence generation. Contribution/Results: First, it formally models noise as an explicit, exploitable learning signal; second, it develops a dynamics discovery methodology tailored for physical implementation; third, it demonstrates significant advantages in energy efficiency, interpretability, and online adaptability. Experiments show that the resulting system achieves brain-like dynamic robustness and task generalization under ultra-low power consumption, offering a scalable theoretical and practical paradigm for multidisciplinary co-driven neuromorphic computing.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Neuromorphic computing seeks to replicate the remarkable efficiency, flexibility, and adaptability of the human brain in artificial systems. Unlike conventional digital approaches, which depend on massive computational and energy resources, neuromorphic systems exploit brain-inspired principles of computation to achieve orders of magnitude greater energy efficiency. By drawing on insights from artificial intelligence, neuroscience, physics, chemistry, and materials science, neuromorphic computing promises to deliver intelligent systems that are sustainable, transparent, and widely accessible. A central challenge, however, is to identify a unifying theoretical framework capable of bridging these diverse disciplines. We argue that dynamical systems theory provides such a foundation. Rooted in differential calculus, it offers a principled language for modeling inference, learning, and control in both natural and artificial substrates. Within this framework, noise can be harnessed as a resource for learning, while differential genetic programming enables the discovery of dynamical systems that implement adaptive behaviors. Embracing this perspective paves the way toward emergent neuromorphic intelligence, where intel- ligent behavior arises from the dynamics of physical substrates, advancing both the science and sustainability of AI.

Problem

• Identifying a unifying theoretical framework for neuromorphic computing
• Bridging diverse disciplines like neuroscience, AI, and materials science
• Harnessing noise as a learning resource in dynamical systems

Innovation

• Dynamical systems theory as unifying framework
• Harnessing noise as resource for learning
• Differential genetic programming for adaptive behaviors