Researchers mimic nature to create a 'bio-inspired brain' for robots

One key goal of the research is the application of the electronic neural device, called a hardware spiking neural network, to the control of autonomous robots which can operate independently in remote, unsupervised environments, such as remote search and rescue applications, and in space exploration.

According to Dr Fearghal Morgan, Director of the Bio-Inspired Electronics and Reconfigurable Computing (BIRC) research group, at NUI Galway: "Electronic neurons, implemented using silicon integrated circuit technology, cannot exactly replicate the complexity of neurons found in the human brain, or the massive number of connections between neurons.

"However, inspired by the operation and structure of the brain, we have successfully developed a hardware spiking neural network and have used this device for robotics control. The electronic device interprets the state of the robot's environment through signals received from sensing devices such as cameras and ultrasonic sensors, which act as the eyes and ears of the robot.

"The neural network then modifies the behaviour of the robot accordingly, by sending signals to the robot's limbs to enable activity such as walking, grasping and obstacle avoidance." Dr Morgan explains: "Our research is focussed on mimicking evolution in nature. The latest hardware neural network currently in development will contain thousands of small electronic neuron-like devices which interoperate concurrently, in a similar way to neurons in the biological brain. The device can be trained to perform a particular function, and can be retrained many times for various applications.

"The training process resembles the training of the brain, by making, strengthening and weakening the links between neurons and defining the conditions which cause a neuron to fire, sending signals to all of the attached neurons. As in the brain, the collection of interconnected neurons makes decisions on incoming data to cause an action in the controlled system.

"Until now, the robotics arena has focused on electronic controllers which incorporate one or more microprocessors, which typically execute instructions in sequence and, while performing tasks quickly, are limited by the instruction processing speed. Power is also a consideration. While the human brain on average only requires 10 watts of power, a typical PC requires 300 watts.

"We believe that a small embedded hardware neural network device has the potential to perform effective robotics control, at low power, while also incorporating fault detection and self-repair behaviour. Our aim is to develop a robust, intelligent hardware neural network robotics controller which can autonomously maintain robot behaviour, even when its environment changes or a fault occurs within the robotics system."

Dr Jim Harkin, from the School of Computing and Intelligent Systems at the University of Ulster's Magee campus), said: "The constant miniaturisation of silicon technology to increase performance introduces inherent reliability issues which must be overcome. Ultimately, the hardware neural network or robot 'brain' will be able to detect and overcome electronic faults that occur within itself, and continue to function effectively without human intervention."