Resonant oscillators generate adaptive search and tracking behaviors
Emergent Intelligence: Resonant Oscillators Produce Proactive Adaptive Behavior
Neural and Evolutionary Computing
Summary
Most artificial intelligence systems react to inputs they receive, but real intelligent behavior often means acting before having full information. The authors show a simple neural circuit made of oscillators that spontaneously switches between exploring and focusing on signals without any training. This behavior emerges from the timing differences in how the oscillators respond, resembling basic curiosity and adaptation. Such circuits could underlie simple animal behavior and inspire new AI designs that explore rather than just predict.
What this means in practice
- •For roboticists: Design autonomous robots that switch between exploring unknown environments and exploiting known signals without requiring training data or complex controllers.
- •For game ai developers: Implement game agents capable of spontaneous curiosity-driven exploration and adaptive target tracking using minimal neural architectures.
Authors
Alex Fedosov, Maxim Yakimenko, Sander Stepanov
Abstract
Most artificial neural systems are built to map given inputs to outputs. Adaptive agents face a prior problem: they must act without enough evidence, seek encounters with the world, and revise behavior when evidence appears. We propose another starting point for intelligent neural networks: proactive search without signals, curiosity at its most basic. We ask whether it can come from a minimal untrained circuit. The spiking unit studied here inverts its response to input: with no signal in its window it fires faster; once signals arrive it switches to a slower, inverted regime. Search needs three or more such oscillators in counter-phase, each reading the same input in a different time window. With no training, supervision, parameter tuning, or controller, the composite switches on its own between exploratory spiral search and exploitative tracking, finding both first-degree symmetry and second-degree groups. The switch comes from temporal disagreement between its fast and slow readings of the same signal. We view the circuit as evolutionarily trained: its abilities come from structure, not experience. Ablation over 63 configurations and 63,000 trials shows the switch needs both temporal staggering and counter-phase opposition, neither enough alone: the behavior is emergent, not programmed. The spiral persists at zero rotational diffusion, so it is structural, and degrades gently under perturbation. More oscillators improve spiral regularity but cut resource capture, so the smallest sufficient circuit wins. We propose that this principle underlies search in simple organisms, navigation and decisions in complex ones, and, being so simple and common, goes unnoticed unless you strip the logic bare. Eventually, networks of such proactive primitives may offer another foundation for AI architectures that explore our world rather than merely predict the next symbol in a sequence.