Locust Brain Mimicry

In the ever-evolving landscape of artificial intelligence (AI), groundbreaking developments continue to shape the way we interact with technology. From the emergence of chatbots driven by sophisticated language models to the advent of autonomous vehicles and self-driving cars, the realm of AI is teeming with innovation. This era presents a thrilling phase where we witness the unfolding impact of these advancements on our daily lives.

While renowned companies like Tesla have introduced self-driving cars in various markets worldwide, India has also made significant strides in AI technology. The Pragyan rover, developed by the Indian Space Research Organisation (ISRO), successfully navigated the uncharted terrain of the moon, showcasing India's prowess in the field of autonomous systems.

One of the primary challenges in the realm of autonomous vehicles lies in the swift and accurate detection of moving obstacles. Traditional obstacle detection systems, relying on intricate algorithms and vision technologies, often fall short in terms of energy efficiency and compactness. A recent collaborative study between researchers at the Indian Institute of Technology Bombay (IIT Bombay) and King’s College London has yielded a breakthrough in this domain.

The research team has devised an ultra-low power transistor that, when integrated into an artificial neuron circuit, demonstrates remarkable obstacle detection capabilities. Inspired by the spiking neuron model observed in biological neurons, this circuit mirrors the functionality of a collision-detecting neuron identified in locusts, known as the lobula giant movement detector (LGMD).

By leveraging cutting-edge two-dimensional (2D) materials, the researchers have crafted a novel artificial neuron circuit that emulates the energy-efficient information processing mechanisms of the brain. The transistor, meticulously engineered to replicate the behavior of sodium channels in biological neurons, operates under a low-current regime, enhancing its energy efficiency and reconfigurability for diverse applications.

Professor Bipin Rajendran from the Department of Engineering at King’s College London, a co-author of the study, emphasizes the versatility of this spiking neuron circuit beyond obstacle detection. He envisions its potential application in various neuromorphic systems that mimic human brain functions, particularly in analog or mixed signal technologies requiring low-energy consumption.