Using Optomemristors To Light Up Artificial Neural Networks

This Article Is Based On The Research Article 'Chalcogenide optomemristors for multi-factor neuromorphic computation'. All Credit For This Research Goes To The Researchers Of This Paper 👏👏👏

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Artificial intelligence and machine learning hardware research have concentrated on building photonic synapses and neurons and combining them to do fundamental forms of neural-type processing. However, complex processing methods found in human brains—such as reinforcement learning and dendritic computation—are more challenging to replicate directly in hardware.

A new study contributes to closing the “hardware gap” by creating an “Optomemristor” device that responds to numerous electronic and photonic inputs at the same time. The diverse biophysical mechanisms that govern the functions of the brain’s neurons and synapses allow for complex learning and processing in the mammalian brain.

The chalcogenide thin-film technology interacts with both light and electrical impulses to mimic multifactor biological computations in mammalian brains while spending very little energy.

Multifactorial computation—three-factor learning—allows the brain to learn efficiently using positive and negative stimuli, such as playing a sport or traversing a maze. The optomemristor method makes such a three-factor approach possible.

Optomemristor can also be used for maze solving. This research demonstrates a practical hardware technique to quickly simulate reinforcement learning, a type of machine learning that enables an artificial rodent to learn to navigate around a maze.

Brain functions based on the interaction of many signals may be carried out using relatively cheap hardware. This is explained by demonstrating a linearly non-separable classification problem (XOR), which requires numerous layers of conventional artificial neurons to solve, unlike the brain, which only uses a single biological neuron.

In fact, it demonstrates how the Optomemristor may effectively give a single-neuron solution for challenging computing problems by replicating the so-called ‘shunting inhibition’ function of dendrites of biological neurons. 

The demos are still in the proof-of-concept stage. When it comes to scaling up such notions and combining them with other hardware blocks, there are a few crucial considerations to consider. Despite this, the crew is ecstatic.