Computers are often perceived as more efficient than humans in various tasks, such as solving complex math equations or retrieving forgotten information about actors. However, human brains possess remarkable capabilities, processing intricate information rapidly, accurately, and with minimal energy expenditure. For instance, humans can recognize a face after seeing it just once or distinguish between a mountain and an ocean instantaneously.
Developing energy-efficient computers that mimic the functionality of the human brain would revolutionize modern life. Quantum Materials for Energy Efficient Neuromorphic Computing (Q-MEEN-C), a nationwide consortium led by the University of California San Diego, has been at the forefront of this research.
Q-MEEN-C’s co-director, Assistant Professor of Physics Alex Frañó from UC San Diego, envisions the center’s work in phases. In the initial phase, Frañó collaborated closely with Professor of Physics Robert Dynes, the President Emeritus of the University of California, and Shriram Ramanathan, a Professor of Engineering at Rutgers. Together, their teams successfully explored ways to replicate or mimic the properties of a single brain element, such as a neuron or synapse, in a quantum material.
In the second phase, Q-MEEN-C published new research in Nano Letters, demonstrating that electrical stimuli passed between neighboring electrodes can also affect non-neighboring electrodes. This discovery, known as non-locality, marks a crucial milestone in advancing devices that replicate brain functions, known as neuromorphic computing.
“Non-local interactions occur frequently and effortlessly in the brain,” stated Frañó, a co-author of the study. “While such behaviors are essential to brain function, they are rare in synthetic materials.”
During the pandemic, the team conducted calculations on arrays containing multiple devices to simulate the brain’s multiple neurons and synapses when physical lab spaces were closed. These simulations indicated the possibility of non-locality in quantum materials.
When labs reopened, the team further refined their ideas and enlisted the expertise of Associate Professor Duygu Kuzum from the UC San Diego Jacobs School of Engineering. Kuzum’s work in electrical and computer engineering enabled the transformation of the simulation into an actual device.
This involved using a thin film of nickelate, a ceramic quantum material with rich electronic properties, and inserting hydrogen ions. A metal conductor was then placed on top of the nickelate, and a wire was attached to allow for the transmission of electrical signals. The signal caused the gel-like hydrogen atoms to move into a specific configuration, which remained even after the signal was removed.
“This configuration functions as a memory,” explained Frañó. “The device retains the perturbation you applied to the material. By manipulating the ions’ positions, you can create more conductive pathways for electricity to flow through.”
Traditionally, creating networks capable of transporting sufficient electricity to power devices like laptops required complex circuits with continuous connection points, resulting in inefficiency and high costs. Q-MEEN-C’s design concept is much simpler, as the non-local behavior in the experiment eliminates the need for all wires in a circuit to be physically connected. It can be compared to how a movement in one part of a spider web can be felt throughout the entire web.
This non-linear behavior of the brain enables complex pattern recognition tasks that are currently only simulated through computer software. Artificial intelligence (AI) programs like ChatGPT and Bard utilize complex algorithms to imitate brain activities like thinking and writing. Although these programs are highly proficient, their capabilities are limited by the hardware supporting them. Hence, a hardware revolution is crucial for advancing AI technology.
Frañó eagerly anticipates a hardware revolution to complement the ongoing software revolution. The research conducted by Q-MEEN-C, showcasing the replication of non-local behavior in a synthetic material, brings scientists one step closer to this goal. The next phase will involve creating more complex arrays with additional electrodes arranged in elaborate configurations.
“This research represents a significant step towards understanding and simulating brain functions,” stated Dynes, another co-author of the study. “Non-local interactions in a system lead us closer to comprehending how our brains function. Although our brains are much more complex, a physical system capable of learning must exhibit high interactivity, and this study serves as an important foundation. It allows us to consider longer-range coherence in space and time.”
“For this technology to truly flourish,” stated Frañó, “we must enhance the hardware, creating physical machines that perform tasks in conjunction with software. This will usher in a new paradigm in the realm of artificial intelligence.”
Ravindra Singh Bisht et al, Spatial Interactions in Hydrogenated Perovskite Nickelate Synaptic Networks, Nano Letters (2023). DOI: 10.1021/acs.nanolett.3c02076
Quantum material exhibits ‘non-local’ behavior that mimics brain function (2023, August 8)
retrieved 8 August 2023
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Shambhu Kumar is a science communicator, making complex scientific topics accessible to all. His articles explore breakthroughs in various scientific disciplines, from space exploration to cutting-edge research.