Scientists Develop Low-Power Artificial Neurons Mimicking Locust Brain Capable Of Obstacle Detection

In a recent study, researchers from the Indian Institute of Technology Bombay (IIT Bombay) and King’s College London, United Kingdom have designed and built an ultra-low power transistor, which when incorporated into their artificial neuron circuit design, is capable of obstacle detection. The circuit mimics the spiking neuron model of biological neurons.

The researchers mimicked the collision detection neuron named lobula giant movement detector (LGMD), found in the locust which helps in avoiding collision with objects in their path. In the current study, the team has designed a new type of low-power artificial neuron circuit that closely mimics the behavior of this collision-detecting neuron found in locusts.

Designing of artificial neuron circuit

The novel artificial neuron circuit is designed by incorporating the models of a new subthreshold transistor built using a two-dimensional (2D) material channel. The use of ultra-thin 2D materials allows reconfigurable and low-power operation, making it suitable for energy-efficient applications. The transistor was carefully designed and fabricated to replicate sodium channel behaviour in biological neurons besides operating under a low-current regime, which enhances its energy efficiency.

Prof Saurabh Lodha, from the Department of Electrical Engineering, says that the research is motivated by the unique feature of the brain which consumes extremely low power for memory and computing. Low power consumption is a key requirement for neuromorphic electronics which are modeled based on the human brain. Although conventional semiconductors such as silicon can be thinned down as well, they lose their performance dramatically at scaled thicknesses, unlike 2D materials, said he.

The researchers demonstrated that the artificial neuron circuit closely matches the essential computational features of the LGMD neuron. It can generate LGMD-like spiking behavior, wherein voltage spikes are produced in response to an input current signal, and detect obstacles at a low energy cost. The energy per spike of the artificial neuron is estimated to be around 3.5 picojoules (pJ), which makes it highly energy efficient compared to existing biomimetic spiking neurons.

Challenges of the current study

Kartikey Thakar, the first author of the recent study notes that the main challenge of the study was to achieve all essential features and spike times to match the biological LGMD neuron response. According to him, one other challenge faced by the team was the minimization of the total energy dissipation of the entire circuit to the best-in-class among other 2D material-based reports. The careful design of the 2D subthreshold transistor characteristics played a critical role in achieving both of these results, which helped the work stand out among its peers.

How does the LGMD-like neuron circuit work?

When the circuit is provided with inputs mimicking collisions, it is able to accurately detect looming objects, signaling a potential collision, at an energy cost of less than 100 pJ. Furthermore, the circuit could distinguish between looming and receding objects, providing a selective response to approaching objects in the direct collision path. This selectivity is crucial for prioritizing the system’s response to potential threats. The artificial neuron also continues to function reliably even when there are variations in the current or noise in the input, making it robust and reliable for real-world applications.

Future implications of the research

The results of this research have important implications in the field of autonomous robotics and vehicle navigation. The ultra-low power spiking neuron circuit could be seamlessly integrated into existing systems, enabling accurate and energy-efficient obstacle detection. This could greatly enhance the safety and reliability of autonomous vehicles operating in unknown or dynamic environments.

One of the key challenges in autonomous vehicles is the ability to accurately and quickly detect moving obstacles. The existing obstacle detection systems, based on complex algorithms and vision systems, are often inefficient in terms of energy consumption and size.

Prof Bipin Rajendran, Department of Engineering, King’s College London, and a co-author of this study, says “We demonstrated that this spiking neuron circuit can be used for obstacle detection. However, the circuit can be used in other neuromorphic (systems mimicking the human brain) applications based on analog or mixed signal technology that requires a low-energy spiking neuron.”

Overall, this research represents a significant advancement in the field of neuromorphic engineering and autonomous robotics using 2D materials in the development of low-power spiking neuron circuits. The research findings can potentially revolutionize obstacle detection and avoidance and pave the way for further exploration of advanced neuromorphic systems and their integration into real-world applications.