Neuromorphic computing

Neuromorphic computing is a computing approach inspired by the human brain's structure and function.^[1][2] It uses artificial neurons to perform computations, mimicking neural systems for tasks such as perception, motor control, and multisensory integration.^[3] These systems, implemented in analog, digital, or mixed-mode VLSI, prioritize robustness, adaptability, and learning by emulating the brain’s distributed processing across small computing elements.^[4] This interdisciplinary field integrates biology, physics, mathematics, computer science, and electronic engineering to develop systems that emulate the brain’s morphology and computational strategies.^[5] Neuromorphic systems aim to enhance energy efficiency and computational power for applications including artificial intelligence, pattern recognition, and sensory processing.

Carver Mead proposed one of the first applications for neuromorphic engineering in the late 1980s.^[6] In 2006, researchers at Georgia Tech developed a field programmable neural array, a silicon-based chip modeling neuron channel-ion characteristics.^[7] In 2011, MIT researchers created a chip mimicking synaptic communication using 400 transistors and standard CMOS techniques.^[8][9]

In 2012 HP Labs researchers reported that Mott memristors exhibit volatile behavior at low temperatures, enabling the creation of neuristors that mimic neuron behavior and support Turing machine components.^[10] Also in 2012, Purdue University researchers presented a neuromorphic chip design using lateral spin valves and memristors, noted for energy efficiency.^[11]

The 2013 Blue Brain Project creates detailed digital models of rodent brains.^[12]

Neurogrid, developed by Brains in Silicon at Stanford University, used 16 NeuroCore chips to emulate 65,536 neurons with high energy efficiency in 2014.^[13] The 2014 BRAIN Initiative and IBM’s TrueNorth chip contributed to neuromorphic advancements.^[14]

The 2016 BrainScaleS project, a hybrid neuromorphic supercomputer at University of Heidelberg, operated 864 times faster than biological neurons.^[15]

In 2017, Intel unveiled its Loihi chip, using an asynchronous artificial neural network for efficient learning and inference.^[16] Also in 2017 IMEC’s self-learning chip, based on OxRAM, demonstrated music composition by learning from minuets.^[17]

In 2022, MIT researchers developed artificial synapses using protons for analog deep learning.^[18] In 2019, the European Union funded neuromorphic quantum computing to explore quantum operations using neuromorphic systems.^[19] Also in 2022, researchers at the Max Planck Institute for Polymer Research developed an organic artificial spiking neuron for in-situ neuromorphic sensing and biointerfacing.^[20]

Researchers reported in 2024 that chemical systems in liquid solutions can detect sound at various wavelengths, offering potential for neuromorphic applications.^[21]

Neuromorphic engineering emulates the brain’s structure and operations, focusing on the analog nature of biological computation and the role of neurons in cognition. The brain processes information via neurons using chemical signals, abstracted into mathematical functions. Neuromorphic systems distribute computation across small elements, similar to neurons, using methods guided by anatomical and functional neural maps from electron microscopy and neural connection studies.^[22][23]

Neuromorphic systems employ hardware such as oxide-based memristors, spintronic memories, threshold switches, and transistors.^[24][25] Software implementations train spiking neural networks using error backpropagation.^[26][27]

Neuromemristive systems use memristors to implement neuroplasticity, focusing on abstract neural network models rather than detailed biological mimicry.^[28] These systems enable applications in speech recognition, face recognition, and object recognition, and can replace conventional digital logic gates.^[29] The Caravelli-Traversa-Di Ventra equation describes memristive memory evolution, revealing tunneling phenomena and Lyapunov functions.^[30]

Neuromorphic principles extend to sensors, such as the retinomorphic sensor or event camera, which mimic human vision by registering brightness changes individually, optimizing power consumption.^[31]

An example of this applied to detecting light is the retinomorphic sensor or, when employed in an array, an event camera.

Neuromorphic systems raise the same ethical questions as those for other approaches to artificial intelligence. Daniel Lim argued that advanced neuromorphic systems could lead to machine consciousness, raising concerns about whether civil rights and other protocols should be extended to them.^[32] Legal debates, such as in Acohs Pty Ltd v. Ucorp Pty Ltd, question ownership of work produced by neuromorphic systems, as non-human-generated outputs may not be copyrightable.^[33]

Wikimedia Commons has media related to Neuromorphic engineering.

• Telluride Neuromorphic Engineering Workshop
• CapoCaccia Cognitive Neuromorphic Engineering Workshop
• Institute of Neuromorphic Engineering
• INE news site.
• Frontiers in Neuromorphic Engineering Journal
• Computation and Neural Systems department at the California Institute of Technology.
• Building a Silicon Brain: Computer chips based on biological neurons may help simulate larger and more-complex brain models