A carbon nanotube spiking cortical neuron with tunable refractory period and spiking duration

Joshi, Jaya; Parker, Alice C.; Hsu, Chih-Chieh

doi:10.1109/lascas.2010.7410229

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…The carbon nanotube is another material under consideration for various neuromorphic applications. A variety of neuromorphic components, including dendrites on neurons [143][144][145][146][147], synapses [148][149][150][151][152][153][154][155][156][157][158][159][160][161][162][163][164][165], and spiking neurons [166][167][168][169], have been proposed to use carbon nanotubes. Carbon nanotubes have been used because they can provide the scale (number of neurons and synapses) and density (in terms of synapses) of neuromorphic systems that may be necessary for replicating or imitating biological neural systems.…”

Section: Materials and Devicesmentioning

confidence: 99%

Neuromorphic Computing between Reality and Future Needs

Ahmed¹,

Shereif²

2023

Neuromorphic Computing

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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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Section: Materials and Devicesmentioning

confidence: 99%

Neuromorphic Computing between Reality and Future Needs

Ahmed¹,

Shereif²

2023

Neuromorphic Computing

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“…Another material considered for some neuromorphic implementations is the carbon nanotube. Carbon nanotubes have been proposed for use in a variety of neuromorphic components, including dendrites on neurons [2286]- [2290], synapses [235], [2291]- [2307], and spiking neurons [139], [2308]- [2310]. The reasons that carbon nanotubes have been utilized are that they can produce both the scale of neuromorphic systems (number of neurons and synapses) and density (in terms of synapses) that may be required for emulating or simulating biological neural systems.…”

Section: Materials For Neuromorphic Systemsmentioning

confidence: 99%

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Schuman¹,

Potok²,

Patton³

et al. 2017

Preprint

172

180

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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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“…2(b) is designed to discharge the total EPSP voltage at the axon hillock circuit [10], which resets the firing phase of the neuron circuit through X11 or X12. Transistor X11 is activated for a short time to stop the firing of the neuron.…”

Section: B the Extrasynaptic Nmdar Circuit For Sic Generationmentioning

confidence: 99%

Astrocyte on neuronal phase synchrony in CMOS

Irizarry-Valle

Parker

2014

2014 IEEE International Symposium on Circuits and Systems (ISCAS)

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CMOS neuromorphic circuits are proposed to emulate the role of astrocytes in phase synchronization of neuronal activity. We emulate, to a first order, the ability of slow inwards currents (SICs) evoked by the astrocyte, acting on extrasynaptic N-methyl-D-aspartate receptors (NMDAR) of adjacent neurons, as a mechanism for phase synchronization. We do an experiment incorporating two small networks of neurons interacting with astrocytic microdomains. Upon enough synaptic activity, the microdomains interact with each other, generating SIC events on synapses of adjacent neurons. Since the amplitude of SICs is several orders larger compared to synaptic currents, a SIC event drastically enhances the excitatory postsynaptic potential on adjacent neurons simultaneuously. This causes neurons to fire synchronously in phase. Phase synchrony holds for a duration of time proportional to the time constant of SIC decay.

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A carbon nanotube spiking cortical neuron with tunable refractory period and spiking duration

Cited by 8 publications

References 20 publications

Neuromorphic Computing between Reality and Future Needs

Neuromorphic Computing between Reality and Future Needs

A Survey of Neuromorphic Computing and Neural Networks in Hardware

Astrocyte on neuronal phase synchrony in CMOS

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