Analog Neuromorphic Module Based on Carbon Nanotube Synapses

Shen, Alex Ming; Chen, Chia-Ling; Kim, Kyung-Hyun; Cho, Byungjin; Tudor, Andrew; Chen, Yong

doi:10.1021/nn401946s

Cited by 86 publications

(74 citation statements)

References 21 publications

(40 reference statements)

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“…Different synaptic functions such as spike-timing dependent plasticity (STDP) and memory consolidation have been demonstrated in various memristors or memristive devices, such as oxides62122, chalcogenides823242526, ferroelectronics2728, field effect transistors2930 and carbon nanotubes3132. In these implementations, two terminals of the device are always regarded as the pre- and postsynaptic sites when inputting the voltage spikes, and the device conductance or resistance represents the synaptic weight that reflects the connection strength of neurons.…”

mentioning

confidence: 99%

Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems

Zhong

Zhang

et al. 2014

Sci Rep

264

202

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Nanoscale inorganic electronic synapses or synaptic devices, which are capable of emulating the functions of biological synapses of brain neuronal systems, are regarded as the basic building blocks for beyond-Von Neumann computing architecture, combining information storage and processing. Here, we demonstrate a Ag/AgInSbTe/Ag structure for chalcogenide memristor-based electronic synapses. The memristive characteristics with reproducible gradual resistance tuning are utilised to mimic the activity-dependent synaptic plasticity that serves as the basis of memory and learning. Bidirectional long-term Hebbian plasticity modulation is implemented by the coactivity of pre- and postsynaptic spikes, and the sign and degree are affected by assorted factors including the temporal difference, spike rate and voltage. Moreover, synaptic saturation is observed to be an adjustment of Hebbian rules to stabilise the growth of synaptic weights. Our results may contribute to the development of highly functional plastic electronic synapses and the further construction of next-generation parallel neuromorphic computing architecture.

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mentioning

confidence: 99%

Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems

Zhong

Zhang

et al. 2014

Sci Rep

264

202

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“…Perhaps most enticingly, some physical properties of these devices that depend on their previous states can be successively tailored by the external stimulus and be stored even after power-off, very analogous to the behaviours of the biological synapse. As a result, several nanoscale electronic devices that include metal oxides [22, 23], solid-electrolytes [24, 25], carbon nanotubes [26, 27], organic electronics [28, 29], spin transfer torque MRAM [30, 31], and phase-change memory (PCM) [32] based on the aforementioned technologies have been proposed to systematically mimic the biological function of the human brain, in particular the learning and memorization capabilities govern by the synapse. It should be noticed that due to the extremely complexity of the human brain, none of these available electronic devices has exhibited the promising potential to prevail or even meet the physical performances of the biological synapse to date.…”

Section: Reviewmentioning

confidence: 99%

Recent Advances on Neuromorphic Systems Using Phase-Change Materials

Wang

Wen

2017

Nanoscale Res Lett

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Realization of brain-like computer has always been human’s ultimate dream. Today, the possibility of having this dream come true has been significantly boosted due to the advent of several emerging non-volatile memory devices. Within these innovative technologies, phase-change memory device has been commonly regarded as the most promising candidate to imitate the biological brain, owing to its excellent scalability, fast switching speed, and low energy consumption. In this context, a detailed review concerning the physical principles of the neuromorphic circuit using phase-change materials as well as a comprehensive introduction of the currently available phase-change neuromorphic prototypes becomes imperative for scientists to continuously progress the technology of artificial neural networks. In this paper, we first present the biological mechanism of human brain, followed by a brief discussion about physical properties of phase-change materials that recently receive a widespread application on non-volatile memory field. We then survey recent research on different types of neuromorphic circuits using phase-change materials in terms of their respective geometrical architecture and physical schemes to reproduce the biological events of human brain, in particular for spike-time-dependent plasticity. The relevant virtues and limitations of these devices are also evaluated. Finally, the future prospect of the neuromorphic circuit based on phase-change technologies is envisioned.

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“…Shen et al (2013) recently reported an analog neuromorphic module consisting of an integrate-and-fire circuit and p -type carbon nanotubes (CNTs) synapses. The CNTs synapse resembles a FET structure using as its gate an aluminum oxide dielectric layer implanted with indium ions and as its channel a random CNTs network.…”

Section: Nanomaterialsmentioning

confidence: 99%

Nanostructures: a platform for brain repair and augmentation

Vidu

Rahman

Mahmoudi

et al. 2014

Front. Syst. Neurosci.

104

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Nanoscale structures have been at the core of research efforts dealing with integration of nanotechnology into novel electronic devices for the last decade. Because the size of nanomaterials is of the same order of magnitude as biomolecules, these materials are valuable tools for nanoscale manipulation in a broad range of neurobiological systems. For instance, the unique electrical and optical properties of nanowires, nanotubes, and nanocables with vertical orientation, assembled in nanoscale arrays, have been used in many device applications such as sensors that hold the potential to augment brain functions. However, the challenge in creating nanowires/nanotubes or nanocables array-based sensors lies in making individual electrical connections fitting both the features of the brain and of the nanostructures. This review discusses two of the most important applications of nanostructures in neuroscience. First, the current approaches to create nanowires and nanocable structures are reviewed to critically evaluate their potential for developing unique nanostructure based sensors to improve recording and device performance to reduce noise and the detrimental effect of the interface on the tissue. Second, the implementation of nanomaterials in neurobiological and medical applications will be considered from the brain augmentation perspective. Novel applications for diagnosis and treatment of brain diseases such as multiple sclerosis, meningitis, stroke, epilepsy, Alzheimer's disease, schizophrenia, and autism will be considered. Because the blood brain barrier (BBB) has a defensive mechanism in preventing nanomaterials arrival to the brain, various strategies to help them to pass through the BBB will be discussed. Finally, the implementation of nanomaterials in neurobiological applications is addressed from the brain repair/augmentation perspective. These nanostructures at the interface between nanotechnology and neuroscience will play a pivotal role not only in addressing the multitude of brain disorders but also to repair or augment brain functions.

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Analog Neuromorphic Module Based on Carbon Nanotube Synapses

Cited by 86 publications

References 21 publications

Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems

Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems

Recent Advances on Neuromorphic Systems Using Phase-Change Materials

Nanostructures: a platform for brain repair and augmentation

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