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Neuromorphic computing
Inventor
Carver Mead
Neuromorphic computing is a computing approach inspired by the human brain's structure and function.[1][2] It usesartificial 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-modeVLSI, prioritize robustness, adaptability, and learning by emulating the brain’s distributed processing across small computing elements.[4] This interdisciplinary field integratesbiology,physics,mathematics,computer science, andelectronic 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 atGeorgia 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 standardCMOS techniques.[8][9]
In 2012HP Labs researchers reported that Mott memristors exhibit volatile behavior at low temperatures, enabling the creation ofneuristors that mimic neuron behavior and supportTuring machine components.[10] Also in 2012,Purdue University researchers presented a neuromorphic chip design using lateralspin valves andmemristors, noted for energy efficiency.[11]
The 2016 BrainScaleS project, a hybrid neuromorphic supercomputer atUniversity of Heidelberg, operated 864 times faster than biological neurons.[15]
In 2017,Intel unveiled itsLoihi chip, using an asynchronousspiking neural network for efficient learning and inference.[16] Also in 2017IMEC’s self-learning chip, based on OxRAM, demonstrated music composition by learning from minuets.[17]
In 2022, MIT researchers developed artificial synapses usingprotons 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 theMax 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 fromelectron microscopy and neural connection studies.[22][23]
Neuromorphic principles extend to sensors, such as theretinomorphic sensor orevent camera, which mimic human vision by registering brightness changes individually, optimizing power consumption.[31]
Neuromorphic systems raise the same ethical questions as those for other approaches toartificial 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 inAcohs Pty Ltd v. Ucorp Pty Ltd, question ownership of work produced by neuromorphic systems, as non-human-generated outputs may not be copyrightable.[33]
^Rami A. Alzahrani; Alice C. Parker (July 2020).Neuromorphic Circuits With Neural Modulation Enhancing the Information Content of Neural Signaling. International Conference on Neuromorphic Systems 2020. pp. 1–8.doi:10.1145/3407197.3407204.S2CID220794387.
^Boahen, Kwabena (April 24, 2014). "Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations".Proceedings of the IEEE.102 (5):699–716.doi:10.1109/JPROC.2014.2313565.S2CID17176371.
