Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics

Najem, Joseph S.; Taylor, Graham J.; Weiss, Ryan; Hasan, Sakib; Rose, Garrett S.; Schuman, Catherine D.; Belianinov, Alex; Collier, C. Patrick; Sarles, Stephen A.

doi:10.1021/acsnano.8b01282

Cited by 123 publications

(217 citation statements)

References 56 publications

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“…The PPF index, defined as the ratio of A2/A1, gradually decreases with the increase of the inter-spike interval, as shown in Figure 6c. Similar PPD following PPF phenomena have been very recently demonstrated in a biomolecular memristive device by Najem et al, [191] and the volatile switching process www.advelectronicmat.de in their biomolecular memristive device originate from the voltage-driven insertion of alamethicin peptides into an insulating lipid bilayer. The dependences of the PPF index on the interval follow a double exponential function in biological synapses and some memristive emulators.…”

Section: Short-term Synaptic Plasticity Realized In Volatile Memristisupporting

confidence: 73%

“…[191] Biorealistic implementation of BCM learning rules with sliding threshold frequency also requires second-order memristive devices, since the sliding threshold frequency calls for variables with short-term dynamics. The shortterm dynamics in diffusive memristive devices (i.e., the spontaneous rupture of conduction filaments) provide a timing encoding mechanism for the combined element.…”

Section: Wwwadvelectronicmatdementioning

confidence: 99%

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Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

177

127

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and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...

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Section: Short-term Synaptic Plasticity Realized In Volatile Memristisupporting

confidence: 73%

Section: Wwwadvelectronicmatdementioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

177

127

View full text Add to dashboard Cite

show abstract

“…[96,161,162] More fundamental studies at the materials and devices levels are needed to achieve more capable artificial intelligent components and systems. This is because www.advmat.de www.advancedsciencenews.com even a single biological neuron or synapse contains a number of complicated sub-components, such as different types of ion channels, and all of them collaboratively contribute to the neuromorphic functions.…”

Section: Memory Switching In Artificial Synapsesmentioning

confidence: 99%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

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Section: Emergence Of Ion Transport Sensory Paradigmsmentioning

confidence: 99%

Bioinspired Ionic Sensory Systems: The Successor of Electronics

et al. 2020

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All biological systems, including animals and plants, communicate in a language of ions and small molecules, while the modern information infrastructures and technologies rely on a language of electrons. Although electronics and bioelectronics have made great progress in the past several decades, they still face the disadvantage of signal transformation when communicating with biology. To narrow the gap between biological systems and artificial‐intelligence systems, bioinspired ion‐transport‐based sensory systems should be developed as successor of electronics, since they can emulate biological functionality more directly and communicate with biology seamlessly. Herein, the essential principles of (accurate) ion transport are introduced, and the recent progress in the development of three elements of an ionic sensory system is reviewed: ionic sensors, ionic processors, and ionic interfaces. The current challenges and future developments of ion‐transport‐based sensory systems are also discussed.

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Memristive Ion Channel-Doped Biomembranes as Synaptic Mimics

Cited by 123 publications

References 56 publications

Memristive Synapses and Neurons for Bioinspired Computing

Memristive Synapses and Neurons for Bioinspired Computing

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Bioinspired Ionic Sensory Systems: The Successor of Electronics

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