Pavlovian conditioning demonstrated with neuromorphic memristive devices

Tan, Zhiqiang; Yin, Xue-Bing; Yang, Rui; Mi, Shao‐Bo; Jia, Chun‐Lin; Guo, Xin

doi:10.1038/s41598-017-00849-7

Cited by 54 publications

(50 citation statements)

References 52 publications

(79 reference statements)

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“…Finally, the famous classical conditioning Pavlov's dog experiments ( Figure ; Note S9 and Figure S9, Supporting Information) were conducted to emulate associative learning in the human brain. In comparison to Pavolovian memristors with temporal dependence of activation signals, this study implements classical conditioning in a thin‐film transistor configuration with intimate coupling between optical and electrical pulses, unlocking novel neural network architectures with light as a global gate.…”

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confidence: 99%

Synergistic Gating of Electro‐Iono‐Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity

et al. 2018

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Emulation of brain-like signal processing with thin-film devices can lay the foundation for building artificially intelligent learning circuitry in future. Encompassing higher functionalities into single artificial neural elements will allow the development of robust neuromorphic circuitry emulating biological adaptation mechanisms with drastically lesser neural elements, mitigating strict process challenges and high circuit density requirements necessary to match the computational complexity of the human brain. Here, 2D transition metal di-chalcogenide (MoS ) neuristors are designed to mimic intracellular ion endocytosis-exocytosis dynamics/neurotransmitter-release in chemical synapses using three approaches: (i) electronic-mode: a defect modulation approach where the traps at the semiconductor-dielectric interface are perturbed; (ii) ionotronic-mode: where electronic responses are modulated via ionic gating; and (iii) photoactive-mode: harnessing persistent photoconductivity or trap-assisted slow recombination mechanisms. Exploiting a novel multigated architecture incorporating electrical and optical biases, this incarnation not only addresses different charge-trapping probabilities to finely modulate the synaptic weights, but also amalgamates neuromodulation schemes to achieve "plasticity of plasticity-metaplasticity" via dynamic control of Hebbian spike-time dependent plasticity and homeostatic regulation. Coexistence of such multiple forms of synaptic plasticity increases the efficacy of memory storage and processing capacity of artificial neuristors, enabling design of highly efficient novel neural architectures.

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mentioning

confidence: 99%

Synergistic Gating of Electro‐Iono‐Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity

et al. 2018

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“…The three terminals are all Pt electrodes, and there are Ag nanoclusters randomly dispersed between pre-/post-synaptic terminals on the dielectric layers of amorphous Si. [204][205][206] In 2017, Yang and his coworkers [200] developed vertical three-terminal memristive devices and realized tunable synaptic modulation dynamics via tailoring modulatory signals, that is, gate inputs. Figure 15e schematically reported the input and output signals when demonstrating heterosynaptic plasticity.…”

Section: Heterosynaptic Plasticitymentioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

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174

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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“…Related to artificial systems, many devices have been realized, inspired by neuronal, synaptic, and psychological behaviors . In particular, classical conditioning has been emulated in machine learning, in electric circuits, using biochemical reaction networks, and in synthetic biology . In the field of machine learning, the classical conditioning process has been simulated by computational models in great extent .…”

Section: Pavlovian Materialsmentioning

confidence: 99%

Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning

et al. 2020

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Herein, it is discussed whether the complex biological concepts of (associative) learning can inspire responsive artificial materials. It is argued that classical conditioning, being one of the most elementary forms of learning, inspires algorithmic realizations in synthetic materials, to allow stimuli‐responsive materials that learn to respond to a new stimulus, to which they are originally insensitive. Two synthetic model systems coined as “Pavlovian materials” are described, whose stimuli‐responsiveness algorithmically mimics programmable associative learning, inspired by classical conditioning. The concepts minimally need a stimulus‐triggerable memory, in addition to two stimuli, i.e., the unconditioned and the originally neutral stimuli. Importantly, the concept differs conceptually from the classic stimuli‐responsive and shape‐memory materials, as, upon association, Pavlovian materials obtain a given response using a new stimulus (the originally neutral one); i.e., the system evolves to a new state. This also enables the functionality to be described by a logic diagram. Ample room for generalization to different stimuli and memory combinations is foreseen, and opportunities to develop future adaptive materials with ever‐more intelligent functions are expected.

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Pavlovian conditioning demonstrated with neuromorphic memristive devices

Cited by 54 publications

References 52 publications

Synergistic Gating of Electro‐Iono‐Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity

Synergistic Gating of Electro‐Iono‐Photoactive 2D Chalcogenide Neuristors: Coexistence of Hebbian and Homeostatic Synaptic Metaplasticity

Memristive Synapses and Neurons for Bioinspired Computing

Viewpoint: Pavlovian Materials—Functional Biomimetics Inspired by Classical Conditioning

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