A breakthrough at the University of Twente brings new brain-like computers one step closer. An international group of researchers led by Prof. Dr. Christian Nijhuis has developed a new type of molecular switch that can learn from past behaviour. The researchers published their results today in the scientific journal Nature Materials, the university said in a press release. “These molecules learn in the same way as our brains,” says Nijhuis.
Computers, data centers, and other electronics use enormous amounts of energy. We are now constructing huge wind farms to meet that energy demand. But according to Prof. Dr. Christian Nijhuis, we can also focus our attention on making our electronics more efficient. “Our brains are the most efficient computers we know of. They use ten thousand times less energy than the most efficient computers,” says Nijhuis.
Brain-computer interface must end the isolation of patients with Locked-in syndrome
Locked-in syndrome is a rare neurological disorder. It is often caused by amyotrophic lateral sclerosis (ALS), an incurable degenerative disease of the motor nervous system.
Efficient brain
This is because our brains process data in a completely different way. Where computers process binary information streams – with zeros and ones – our brains work analogously by means of time-dependent pulses. “Our brains process information from millions of nerve cells from all our senses without any problem. In contrast to traditional electronics, they only use the brain cells and synapses through which pulses pass,” says Nijhuis. Because energy is only consumed during a pulse, our brains can process a lot of data at the same time much more efficiently.
Artificial intelligence hardware
The molecules Nijhuis and his team developed can perform all the Boolean logic gate circuits required for ‘deep learning’. “Deep learning is a form of machine learning based on artificial neural networks and is widely used in the automatic recognition of images and speech, but also in the search for new medicines and, more recently, in making art. All things that are much more difficult for a computer than for our brain,” says Nijhuis. Researchers are making great strides in the field of artificial intelligence software, but these molecules are now also bringing artificial intelligence hardware closer.
The art neuron
To mimic the dynamic behavior of the synapses at the molecular level, the researchers combined fast electron transfer with slow proton coupling limited by diffusion. This resembles the rapid pulses and slow uptake of neurotransmitters from the neurons in your brain. The molecules can adjust the strength and duration of the pulses. In doing so, they show a form of classical conditioning. The molecules adapt their behavior to the stimuli they have previously received. A form of learning. In the future, such molecules may also respond to other stimuli such as light.
Many new applications
This breakthrough makes it possible to develop a whole new range of adaptable and reconfigurable systems. These in turn can lead to new multifunctional adaptive systems that simplify artificial neural networks considerably. Nijhuis: “This drastically reduces the energy consumption of our electronics.” Multifunctional molecules that are also light-sensitive or can detect other molecules can lead to new types of neural networks or sensors.
Online publication
Christian Nijhuis leads the group ‘Hybrid Materials for Opto-Electronics’ (HMOE; Faculty of Applied Sciences), part of the UT’s MESA+ Institute for Nanotechnology. He is also Principal Investigator of the Computing Molecules & (Opto)Electronics research area within the Molecules Center of MESA+. This research was carried out in collaboration with Damien Thompson, Professor of Molecular Modeling and Director of SSPC (Science Foundation Ireland Research Center for Pharmaceuticals at the University of Limerick) Enrique del Barco, Pegsus Professor at the University of Central Florida.
The publication titled ‘Dynamic molecular switches with hysteretic negative differential conductance emulating synaptic behaviour’ was published in the scientific journal Nature Materials. Nature Materials is a top-3 journal in the field of chemistry, physics and materials science. The publication can be read online.