Oscillating Power: Resonance, AC Systems, and Feedback Loops

Oscillating Transmission: AC, Gradients, and Brain Waves

My alternating current system transmits power through oscillation—60 Hz sine waves carry energy more efficiently than any static voltage could. The principle: information rides on the carrier wave. Neural networks discovered this independently. Watch gradient descent navigate a loss landscape and you see oscillating trajectories—not smooth descent but rhythmic fluctuations around the path to convergence. The loss doesn’t decrease monotonically; it oscillates toward minima through countless small adjustments, each training step a half-cycle in parameter space.

The brain operates identically. Hippocampal theta rhythm—8 Hz oscillations—serves as temporal carrier for memory formation. Place cells don’t simply fire when an animal reaches a location; they fire at specific phases within the theta cycle, encoding past-present-future as a compressed sequence within each oscillation. Information arrives not as static signal but as timing relative to the oscillatory reference frame. Different brain regions synchronize by locking to shared theta phase, binding distributed neural assemblies into unified episodic memories.

Why oscillations? Static signals interfere destructively when multiple channels operate simultaneously. Oscillations enable frequency-division multiplexing—my radio transmitters proved this. Different frequencies coexist without mutual destruction. The brain uses multiple oscillatory bands (delta, theta, alpha, beta, gamma) for parallel information streams. Neural networks achieve similar multiplexing through oscillating activation patterns: forward pass oscillations carry information upward through layers, backward pass oscillations propagate error gradients downward. Both directions operate simultaneously through alternating dynamics.

Resonance Catastrophe: When Oscillation Runs Wild

But oscillation harbors danger. Resonance—when driving frequency matches natural frequency—amplifies small perturbations into catastrophic oscillations. The Tacoma Narrows Bridge collapsed from wind-induced oscillations at resonant frequency; minor periodic forces grew exponentially until structural failure.

Neural networks face identical hazards. Poor initialization places parameters where gradient updates resonate with landscape geometry—exploding gradients, divergent oscillations in parameter space, training collapse. Too-high learning rates create runaway oscillations; the system overshoots minima repeatedly, never settling. The network has architectural capacity for solutions but cannot reach them from unfortunate starting conditions. Initialization sensitivity determines whether gradient descent paths lead through favorable landscape regions or dead-end in ReLU zero-zones where gradients vanish.

Brain resonance manifests as epileptic seizure—runaway synchronization where excitatory feedback overwhelms regulatory mechanisms. Normal theta rhythm becomes pathological oscillation, spreading across cortex unchecked. The system designed for controlled oscillation enters resonant catastrophe.

Feedback Stabilization: Controlling Dynamic Systems

How do oscillating systems avoid resonant destruction? Feedback control. My AC generators maintain stable frequency through automatic load adjustment—voltage changes trigger compensating parameter shifts. Neural networks implement gradient clipping and adaptive learning rates—feedback mechanisms detecting dangerous oscillations and damping them before divergence. The brain balances excitation with inhibitory interneurons—medial septal pacemakers drive hippocampal theta, but local inhibitory circuits prevent excessive synchronization.

Feedback transforms oscillation from liability to robust adaptive mechanism. Static systems are fragile—any perturbation causes deviation. Oscillating systems with feedback absorb disturbances through phase and amplitude adjustment. The same dynamics that enable efficient transmission also provide inherent stability when properly regulated.

Controlled oscillation appears fundamental to complex systems. Power transmission, gradient-based learning, memory encoding—all use oscillatory dynamics with feedback stabilization. Perhaps this is not coincidence but necessity: systems processing multiple information streams require oscillatory carriers, and oscillatory systems require feedback control. Oscillation plus feedback equals adaptive robustness.