# Super-Turing synaptic resistor circuits for intelligent morphing wing

## TL;DR

A new circuit inspired by brain processes enables a morphing wing to learn and adapt in real-time, improving performance in complex environments.

## Contribution

A Super-Turing synaptic resistor circuit is introduced for concurrent learning and inference in AI systems.

## Key findings

- The circuit reduces drag-to-lift force ratio and recovers from stalls in aerodynamic environments.
- It outperforms artificial neural networks and human operators in speed, adaptability, and power efficiency.
- The design enables error correction and agile adaptation in dynamically changing conditions.

## Abstract

Neurobiological circuits in the brain, operating in Super-Turing mode, process information while simultaneously modifying their synaptic connections through learning, allowing them to dynamically adapt to changes. In contrast, artificial intelligence systems based on computers operate in Turing mode and lack the ability to concurrently infer and learn, making them vulnerable to failure under dynamically changing conditions. Here we show a synaptic resistor circuit that operates in Super-Turing mode, enabling concurrent learning and inference. The circuit controls a morphing wing to reduce its drag-to-lift force ratio and recover from stalls in complex aerodynamic environments. The synaptic resistor circuit demonstrates superior performance, faster learning speeds, enhanced adaptability, and reduced power consumption compared to artificial neural networks and human operators on the same task. By overcoming the fundamental limitations of computers, synaptic resistor circuits offer high-speed concurrent learning and inference, ultra-low power consumption, error correction, and agile adaptability for artificial intelligence systems.

Atharva Deo and colleagues present a Super-Turing synaptic resistor circuit to control a morphing wing in complex aerodynamic environments. The circuit features high-speed concurrent learning and inference, ultra-low power consumption, and agile adaptability for AI systems.