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Developing New AI Systems Via Synthetic Neural Network Using Nanotechnology

Neuromorphic Network

The University of Sydney Nano Institute and School of Physics is working with international researchers on a synthetic neural network using nanotechnology, with the potential to develop new systems in machine learning and artificial intelligence (AI).

The project is led and funded by the International Centre for Materials Nanoarchitectonics at the National Institute of Materials Science in Japan.

Also working on the project is the California NanoSystems Institute at the University of California in Los Angeles.

About the Study

As reported, the team discovered that this “neuromorphic network” exhibited emergent brain-like behaviour resembling cognitive functions such as learning, memorisation and forgetting when electrically stimulated.

A neuromorphic network is an artificial, large-scale system that mimics the biological functions of the nervous system, particularly its neurons.

Professor Zdenka Kuncic from Sydney Nano and the School of Physics explained that this opens up the possibility of processing dynamically changing data, which existing machine learning and AI methods cannot handle.

The research was recently published in Springer Nature’s Scientific Reports.

Benefits of the Study

The discovery has implications for the development of artificial intelligence (AI) networks.

Although AI is brain-inspired, the underlying mechanism by which the brain processes information remains elusive.

Therefore, creating novel materials and systems, which mimic functions similar to the brain, and understanding the mechanisms of those functions may open up new possibilities for neuromorphic information processing technologies.

How it Works

The neuromorphic network was created by self-assembled nanowires, which form numerous contacts between adjacent nanowires.

Each contact exhibits a synaptic-like response to electrical stimulation. A synapse is a junction between nerve cells.

Nanowires are measured in nanometres, which are a billionth of a metre in size.

The nanowires used were made of a silver and polymer composite material. The average diameter of the nanowires used was 360 nanometres, or 0.00000036 metres wide.

A human hair is about 100,000 nanometres wide.

The synthetic synapses, used collectively, regulated the overall functionality of the entire network.

The research team investigated the process of electrical signal transmission across preferred paths in the complex network by time-resolved electrical resistance measurements.

Spontaneous Adaptation

It revealed continuous fluctuations that enable electrical signals to exploit multiple transport pathways across the network and spontaneously adapt to changing transmission routes.

This process leads to emergent network properties related to learning, memorisation and forgetting of input signals.

Based on this discovery, the research team is now developing next-generation memory devices and neuromorphic information processing systems using nanowire networks.

While current AI technology is based on an assumed model of brain-type information processing, this research provides a glimpse into what brain-type information processing actually is.

The team hopes that the outcomes of this research will lead to new data processing capabilities beyond the reach of AI.

At Sydney Nano, Professor Kuncic is working with colleagues on a Grand Challenge project to unlock the neural interface.

Along with Professor Gregg Suaning in the School of Biomedical Engineering, the multidisciplinary team is looking to unlock the neural code through the convergence of neural biology and electrical stimulation with nanotechnology.

The aim of the project is to harness the combined capacity of neural biology, electrical stimulation and nanotechnology to transform and restore neurons from a state of disease or dysfunction to a state of robust performance indistinguishable from normal function.

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