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Physics for neuromorphic computing

An Author Correction to this article was published on 21 July 2021

This article has been updated


Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware for information processing, capable of highly sophisticated tasks. Systems built with standard electronics achieve gains in speed and energy by mimicking the distributed topology of the brain. Scaling-up such systems and improving their energy usage, speed and performance by several orders of magnitude requires a revolution in hardware. We discuss how including more physics in the algorithms and nanoscale materials used for data processing could have a major impact in the field of neuromorphic computing. We review striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies. We discuss the paths that could lead these approaches to maturity, towards low-power, miniaturized chips that could infer and learn in real time.

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Fig. 1: Hardware for deep neural networks.
Fig. 2: Materials and physics for neuromorphic computing.
Fig. 3: Biologically inspired neuromorphic computing.
Fig. 4: Toy neuromorphic systems with spintronics.

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This work was supported as part of the Q-MEEN-C, an Energy Frontier Research Center funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under award DE-SC0019273 and by the European Research Council ERC under grant bioSPINspired (682955) and NANOINFER (715872). A.M. received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement number 824103 (NEUROTECH).

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Correspondence to Damien Querlioz or Julie Grollier.

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Cells in the brain that assist neurons for blood and metabolism regulation. Evidence points to their role in communication and processing.


Nerve fibres that conduct the action potentials away from the soma to other neurons.

Boltzmann machines

Stochastic Hopfield networks that use a Boltzmann distribution inspired by statistical physics in their sampling function.


Branched extensions of neurons, which conduct stimulation received from another neuron towards the neuron soma.


Typical number of connections spreading from a given point in a circuit. In the brain, one neuron is connected to 10,000 others, that is, it has a 10,000 fan-out.

Hopfield networks

Specific type of recurrent neural network (neural network containing recurrent loops) that has neurons functioning as binary threshold nodes.

Kerr effect

Change of the refractive index of a material due to an applied electric field, proportional to the square of the field amplitude.

Modified National Institute of Standards and Technology database

(MNIST). Dataset of 28 × 28 pixel images of handwritten digits, widely used as a benchmark for image classification.

Recurrent loops

Connections from neurons to themselves or to neurons in preceding layers (that is, on the input side) of the network. These loops are key for processing time-varying inputs.

Reservoir computing

Specific type of neural network for which an assembly of neurons — the reservoir — has fixed random recurrent connections, and only connections from the reservoir to the output are trained.


Cell bodies of neurons, containing the nucleus. They are considered a key processing part of the neuron.

Spatial light modulators

Components, for example, based on liquid crystals, used in optical computing to induce a spatially varying modulation on a beam of light.


Short peaks of electrical potential at the membrane of a neuron, used to encode and communicate information. Also known as action potentials.


Spintronics is the field of study of systems in which information is encoded using the magnetic properties of electrons. The name is a contraction of ‘spin’ and ‘electronics’.

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Marković, D., Mizrahi, A., Querlioz, D. et al. Physics for neuromorphic computing. Nat Rev Phys 2, 499–510 (2020).

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