A password will be e-mailed to you.

New Device Emulates Human Synapses

Summary: Researchers have developed a new nanodevice for computer microprocessors that can mimic the function of a biological synapse.

Source: UMass Amherst.

Engineers at the University of Massachusetts Amherst are leading a research team that is developing a new type of nanodevice for computer microprocessors that can mimic the functioning of a biological synapse—the place where a signal passes from one nerve cell to another in the body. The work is featured in the advance online publication of Nature Materials.

Such neuromorphic computing in which microprocessors are configured more like human brains is one of the most promising transformative computing technologies currently under study.

J. Joshua Yang and Qiangfei Xia are professors in the electrical and computer engineering department in the UMass Amherst College of Engineering. Yang describes the research as part of collaborative work on a new type of memristive device.

Image shows an illustration of a synapse.

The researchers say they proposed and demonstrated a bio-inspired solution to the diffusive dynamics that is fundamentally different from the standard technology for integrated circuits while sharing great similarities with synapses. NeuroscienceNews.com image is in the public domain.

Memristive devices are electrical resistance switches that can alter their resistance based on the history of applied voltage and current. These devices can store and process information and offer several key performance characteristics that exceed conventional integrated circuit technology.

“Memristors have become a leading candidate to enable neuromorphic computing by reproducing the functions in biological synapses and neurons in a neural network system, while providing advantages in energy and size,” the researchers say.

Neuromorphic computing—meaning microprocessors configured more like human brains than like traditional computer chips—is one of the most promising transformative computing technologies currently under intensive study. Xia says, “This work opens a new avenue of neuromorphic computing hardware based on memristors.”

They say that most previous work in this field with memristors has not implemented diffusive dynamics without using large standard technology found in integrated circuits commonly used in microprocessors, microcontrollers, static random access memory and other digital logic circuits.

The researchers say they proposed and demonstrated a bio-inspired solution to the diffusive dynamics that is fundamentally different from the standard technology for integrated circuits while sharing great similarities with synapses. They say, “Specifically, we developed a diffusive-type memristor where diffusion of atoms offers a similar dynamics and the needed time-scales as its bio-counterpart, leading to a more faithful emulation of actual synapses, i.e., a true synaptic emulator.”

The researchers say, “The results here provide an encouraging pathway toward synaptic emulation using diffusive memristors for neuromorphic computing.”

About this neuroscience research article

The title of the article is “Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing.” In addition to Xia and Yang, the authors include Zhongrui Wang, Saumil Joshi, Hao Jiang, Rivu Midya, Peng Lin, of the UMass Amherst electrical and computer engineering department; Sergey E. Savel’ev of the department of physics, Loughborough University in the U.K.; Miao Hu, Ning Ge, John Paul Strachan, Zhiyong Li, and R. Stanley Williams of the Hewlett Packard Labs, Palo Alto, Calif.; Qing Wu and Mark Barnell of the Air Force Research Lab, Information Directorate, Rome, New York; GengLin Li of the UMass Amherst department of biology, and Huolin L. Xin of the Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York.

Source: Patrick J. Callahan – UMass Amherst
Image Source: NeuroscienceNews.com image is in the public domain.
Original Research: Abstract for “Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing” by Zhongrui Wang, Saumil Joshi, Sergey E. Savel’ev, Hao Jiang, Rivu Midya, Peng Lin, Miao Hu, Ning Ge, John Paul Strachan, Zhiyong Li, Qing Wu, Mark Barnell, Geng-Lin Li, Huolin L. Xin, R. Stanley Williams, Qiangfei Xia and J. Joshua Yang in Nature Materials. Published online September 26 2016 doi:10.1038/nmat4756

Cite This NeuroscienceNews.com Article
UMass Amherst. “New Device Emulates Human Synapses.” NeuroscienceNews. NeuroscienceNews, 29 September 2016.
<http://neurosciencenews.com/human-synapse-emulation-5157/>.
UMass Amherst. (2016, September 29). New Device Emulates Human Synapses. NeuroscienceNew. Retrieved September 29, 2016 from http://neurosciencenews.com/human-synapse-emulation-5157/
UMass Amherst. “New Device Emulates Human Synapses.” http://neurosciencenews.com/human-synapse-emulation-5157/ (accessed September 29, 2016).

Abstract

Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing

The accumulation and extrusion of Ca2+ in the pre- and postsynaptic compartments play a critical role in initiating plastic changes in biological synapses. To emulate this fundamental process in electronic devices, we developed diffusive Ag-in-oxide memristors with a temporal response during and after stimulation similar to that of the synaptic Ca2+ dynamics. In situ high-resolution transmission electron microscopy and nanoparticle dynamics simulations both demonstrate that Ag atoms disperse under electrical bias and regroup spontaneously under zero bias because of interfacial energy minimization, closely resembling synaptic influx and extrusion of Ca2+, respectively. The diffusive memristor and its dynamics enable a direct emulation of both short- and long-term plasticity of biological synapses, representing an advance in hardware implementation of neuromorphic functionalities.

“Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing” by Zhongrui Wang, Saumil Joshi, Sergey E. Savel’ev, Hao Jiang, Rivu Midya, Peng Lin, Miao Hu, Ning Ge, John Paul Strachan, Zhiyong Li, Qing Wu, Mark Barnell, Geng-Lin Li, Huolin L. Xin, R. Stanley Williams, Qiangfei Xia and J. Joshua Yang in Nature Materials. Published online September 26 2016 doi:10.1038/nmat4756

Feel free to share this Neuroscience News.
No more articles