Ultracompact Graphene Multigate Variable Resistor for Neuromorphic Computing

Darwish, M.; Calayir, Vehbi; Pileggi, Lawrence T.; Weldon, Jeffrey A.

doi:10.1109/tnano.2016.2525039

Cited by 10 publications

(9 citation statements)

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“…In addition, organic core–sheath nanowires with ionic gating gel have been shown to mimic synaptic behavior with ultralow energy consumption rivaling that of biological synapses . Transistor-based synapses have also been explored with channel materials made from carbon nanotubes, black phosphorus, and graphene . Although artificial synaptic transmission has been realized in the recent past, there exists no report of a device that accurately mimics the typical behaviors of neurotransmitters.…”

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confidence: 99%

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Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS₂ Transistors

et al. 2017

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Neurotransmitter release in chemical synapses is fundamental to diverse brain functions such as motor action, learning, cognition, emotion, perception, and consciousness. Moreover, improper functioning or abnormal release of neurotransmitter is associated with numerous neurological disorders such as epilepsy, sclerosis, schizophrenia, Alzheimer's disease, and Parkinson's disease. We have utilized hysteresis engineering in a back-gated MoS field effect transistor (FET) in order to mimic such neurotransmitter release dynamics in chemical synapses. All three essential features, i.e., quantal, stochastic, and excitatory or inhibitory nature of neurotransmitter release, were accurately captured in our experimental demonstration. We also mimicked an important phenomenon called long-term potentiation (LTP), which forms the basis of human memory. Finally, we demonstrated how to engineer the LTP time by operating the MoS FET in different regimes. Our findings could provide a critical component toward the design of next-generation smart and intelligent human-like machines and human-machine interfaces.

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confidence: 99%

“…25 Transistor-based synapses have also been explored with channel materials made from carbon nanotubes, 26 black phosphorus, 27 and graphene. 28 Although artificial synaptic transmission has been realized in the recent past, there exists no report of a device that accurately mimics the typical behaviors of neurotransmitters.…”

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Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS₂ Transistors

et al. 2017

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“…Neuromorphic systems have more recently incorporated graphene to produce more compact circuits. It has been used in neuromorphic implementations as well as full synapse implementations [141,142] for transistors [137][138][139] and resistors [140].…”

Section: Materials and Devicesmentioning

confidence: 99%

Neuromorphic Computing between Reality and Future Needs

Ahmed¹,

Shereif²

2023

Artificial Intelligence

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Neuromorphic computing is a one of computer engineering methods that to model their elements as the human brain and nervous system. Many sciences as biology, mathematics, electronic engineering, computer science and physics have been integrated to construct artificial neural systems. In this chapter, the basics of Neuromorphic computing together with existing systems having the materials, devices, and circuits. The last part includes algorithms and applications in some fields.

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“…Graphene has more recently been incorporated in neuromorphic systems in order to achieve more compact circuits. It has been utilized for both transistors [2280]- [2282] and resistors [2283] for neuromorphic implementations and in full synapse implementations [2284], [2285].…”

Section: Materials For Neuromorphic Systemsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

176

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Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brainlike ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

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Ultracompact Graphene Multigate Variable Resistor for Neuromorphic Computing

Cited by 10 publications

References 53 publications

Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS₂ Transistors

Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS₂ Transistors

Neuromorphic Computing between Reality and Future Needs

A Survey of Neuromorphic Computing and Neural Networks in Hardware

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Ultracompact Graphene Multigate Variable Resistor for Neuromorphic Computing

Cited by 10 publications

References 53 publications

Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS2 Transistors

Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS2 Transistors

Neuromorphic Computing between Reality and Future Needs

A Survey of Neuromorphic Computing and Neural Networks in Hardware

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Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS₂ Transistors

Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS₂ Transistors