Research Overview

Neuromorphic computing is a field of computer science that aims to study computing systems that emulate the functionality of biological neural networks in the human brain. The goal of neuromorphic computing is to create hardware and software systems that can perform complex cognitive tasks more efficiently and effectively than traditional computing systems.

One of the key components of neuromorphic computing is the spiking neural network (SNN), which is a type of artificial neural network that is inspired by the biological processes that occur in the human brain. Unlike traditional artificial neural networks, which are based on continuous signals, spiking neural networks transmit information in the form of discrete spikes or pulses, which more closely resemble the way neurons in the brain communicate with one another.

Spiking neural networks have several advantages over traditional neural networks, including greater power efficiency, faster processing times, and the ability to process temporal information more effectively. This makes them particularly well-suited for applications in areas such as robotics, image and speech recognition, and autonomous systems.

The development of brain-inspired spiking neural networks is an active area of research within neuromorphic computing. We are working to conduct research on new algorithms and architectures that are more closely aligned with the biological processes that occur in the brain. These brain-inspired systems have the potential to revolutionize computing by enabling the creation of more efficient and intelligent machines that can perform complex tasks with greater accuracy and speed.

Ongoing Research

Past Research