New Delhi [India], In a study, researchers have developed ultra-low power artificial neurons capable of detecting obstacles by mimicking the brains of locusts. According to a study, this innovation has been done under the leadership of scientists from the Indian Institute of Technology Bombay (IIT Bombay). King's College London, United Kingdom, could revolutionize autonomous robotics and vehicle navigation. The study, published in the journal Nature Electronics, shows the creation of two-dimensional material-based transistors, which have been used to create artificial neuron circuits. These circuits work closely.Spiking neuron models mimic biological neurons and are specifically designed for obstacle detection. Inspired by the brain's unique ability to efficiently process information, the researchers took inspiration from the behavior of a collision-detecting neuron found in locusts, known as the lobula giant movement detector. LGMD), this neuron plays an important role in helping locusts avoid collisions with objects in their path. The researchers aimed to replicate this mechanism in artificial neurons for energy-efficient obstacle detection. Explaining the rationale behind the research, Professor Saurabh Lodha of II Bombay said, “Unlike modern computers, the human brain consumes extremely low power for memory and computing.Therefore, low power consumption is an essential requirement for neuromorphic (modeled after the human brain) electronics. He added, "2D materials are ideally suited for this purpose due to their atomically thin nature that allows excellent electrostatic control for low-power operation. Although conventional semiconductors such as silicon can also be thinned , but they lose their performance dramatically as the thickness of the scale increases, unlike 2D materials." Newly developed artificial neuron circuits include models of subthreshold transistors constructed using two-dimensional (2D) material channels. This transistor, carefully designed and fabricated, replicates the behavior of sodium channels in biological neurons while operating under low-current regime to increase energy efficiency.Karthikeya Thakar, first author of the study, highlighted the challenges faced during the research, including achieving the necessary features and spike timing to match the biological LGMD neuron response and minimize total energy dissipation. However, the 2D subthreshold transistor characteristics Careful design of FTII plays a vital role in overcoming these challenges. The results of the study showed that the artificial neuron circuit accurately detected potential objects. Collisions, at an energy cost of less than 100 picojoules (pJ), Furthermore, the circuit distinguishes between emerging and receding objects, providing a selective response to threats in the direct collision path. Professor Bipin Rajendran of King's College London emphasized the versatility of spiking. neuron circuit, suggesting that it could be used in a variety of neuromorphic applications that require low-energy spiking neurons beyond obstacle detection.Looking ahead, Professor Saurabh Lodha discussed the potential market implications, saying that the semiconductor industry has shown increasing interest in the 2 materials for future transistor development. Widespread adoption of these materials will depend on solutions to technical challenges and their compatibility with existing technology platforms. Overall, this research represents a significant advancement in the field of neuromorphic engineering and autonomous robotics. The ultra-low power spiking neuron circuits developed in this study significantly enhance obstacle detection and avoidance in real-world applications, paving the way for further exploration of advanced neuromorphic systems.