scholarly journals Flexible and Energy-efficient Synaptic Transistor with Quasi-linear Weight Update Protocol by Inkjet Printing of Orientated Polar-electret/High-k Oxide Hybrid Dielectric

2021 ◽  
Author(s):  
Yushan Li ◽  
Wei Cai ◽  
Ruiqiang Tao ◽  
Wentao Shuai ◽  
Jingjing Rao ◽  
...  
Keyword(s):  
Inkjet Printing ◽  
Low Voltage ◽  
Learning Experience ◽  
Low Cost ◽  
Learning Rule ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Dipole Orientation ◽  
Synaptic Dynamics ◽  
Conductance States

Abstract Artificial synapse by inkjet printing is promising in cost-effective and flexible applications, but remains challenging in emulating synaptic dynamics with a sufficient number of stable and effective conductance states under ultra-low voltage spiking operation. Hence, for the first time, a synaptic transistor gated by inkjet-printed hybrid dielectric of electret polyvinyl pyrrolidone (PVP) and high-k Zirconia oxide (ZrOx) is proposed and thus synthesized to solve this issue. Quasi-linear potentiation/depression characteristics with large variation margin of conductance states are obtained through the coupling of these two dielectric components and the facilitating of dipole orientation, which can be attributed to the orderly arranged molecule chains induced by the carefully designed microfluidic flows in droplets. Crucial features of biological synapses including long-term potentiation/depression (LTP/D), spike-timing-dependence-plasticity (STDP) learning rule, “Learning-Experience” behavior, and ultralow energy consumption (< 10 fJ/pulse) are successfully implemented on the device. Simulation results exhibit an excellent image recognition accuracy (97.1 %) after 15 training epochs, which is the highest for printed synaptic transistors. Moreover, the device sustained excellent endurance against bending tests with radius down to 8 mm. This work presents a very viable solution for constructing the futuristic flexible and low-cost neural systems.

How the Shape of Pre- and Postsynaptic Signals Can Influence STDP: A Biophysical Model

2004 ◽  
Vol 16 (3) ◽  
pp. 595-625 ◽  
Author(s):  
Ausra Saudargiene ◽  
Bernd Porr ◽  
Florentin Wörgötter
Keyword(s):  
Membrane Potential ◽  
Weight Change ◽  
Hebbian Learning ◽  
Dendritic Tree ◽  
Learning Rule ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Biophysical Model ◽  
Nmda Channel

Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), when the temporal order is reversed. In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductance with the derivative of the membrane potential at the synapse as the postsynaptic signal. The model is able to reproduce the generic STDP weight change characteristic. We find that (1) The actual shape of the weight change curve strongly depends on the NMDA channel characteristics and on the shape of the membrane potential at the synapse. (2) The typical antisymmetrical STDP curve (LTD and LTP) can become similar to a standard Hebbian characteristic (LTP only) without having to change the learning rule. This occurs if the membrane depolarization has a shallow onset and is long lasting. (3) It is known that the membrane potential varies along the dendrite as a result of the active or passive backpropagation of somatic spikes or because of local dendritic processes. As a consequence, our model predicts that learning properties will be different at different locations on the dendritic tree. In conclusion, such site-specific synaptic plasticity would provide a neuron with powerful learning capabilities.

scholarly journals Bipolar Analog Memristors as Artificial Synapses for Neuromorphic Computing

Materials ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2102 ◽  
Author(s):  
Rui Wang ◽  
Tuo Shi ◽  
Xumeng Zhang ◽  
Wei Wang ◽  
Jinsong Wei ◽  
...  
Keyword(s):  
Complementary Metal Oxide Semiconductor ◽  
Building Blocks ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Oxide Semiconductor ◽  
Switching Behavior ◽  
Neuromorphic Computing ◽  
Training Scheme ◽  
Conductance States ◽  
Linear Conductance

Synaptic devices with bipolar analog resistive switching behavior are the building blocks for memristor-based neuromorphic computing. In this work, a fully complementary metal-oxide semiconductor (CMOS)-compatible, forming-free, and non-filamentary memristive device (Pd/Al2O3/TaOx/Ta) with bipolar analog switching behavior is reported as an artificial synapse for neuromorphic computing. Synaptic functions, including long-term potentiation/depression, paired-pulse facilitation (PPF), and spike-timing-dependent plasticity (STDP), are implemented based on this device; the switching energy is around 50 pJ per spike. Furthermore, for applications in artificial neural networks (ANN), determined target conductance states with little deviation (<1%) can be obtained with random initial states. However, the device shows non-linear conductance change characteristics, and a nearly linear conductance change behavior is obtained by optimizing the training scheme. Based on these results, the device is a promising emulator for biology synapses, which could be of great benefit to memristor-based neuromorphic computing.

Fabrication of 3D Temperature Sensor Using Magnetostrictive Inkjet Printhead

2020 ◽  
Vol 64 (5) ◽  
pp. 50405-1-50405-5
Author(s):  
Young-Woo Park ◽  
Myounggyu Noh
Keyword(s):  
Flexible Electronics ◽  
Inkjet Printing ◽  
Temperature Sensor ◽  
Computer Aided Design ◽  
Low Cost ◽  
Three Dimensional ◽  
Drop On Demand ◽  
Aided Design ◽  
Nanoparticle Ink ◽  
Inkjet Printhead

Abstract Recently, the three-dimensional (3D) printing technique has attracted much attention for creating objects of arbitrary shape and manufacturing. For the first time, in this work, we present the fabrication of an inkjet printed low-cost 3D temperature sensor on a 3D-shaped thermoplastic substrate suitable for packaging, flexible electronics, and other printed applications. The design, fabrication, and testing of a 3D printed temperature sensor are presented. The sensor pattern is designed using a computer-aided design program and fabricated by drop-on-demand inkjet printing using a magnetostrictive inkjet printhead at room temperature. The sensor pattern is printed using commercially available conductive silver nanoparticle ink. A moving speed of 90 mm/min is chosen to print the sensor pattern. The inkjet printed temperature sensor is demonstrated, and it is characterized by good electrical properties, exhibiting good sensitivity and linearity. The results indicate that 3D inkjet printing technology may have great potential for applications in sensor fabrication.

Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

2006 ◽  
Vol 18 (10) ◽  
pp. 2414-2464 ◽  
Author(s):  
Peter A. Appleby ◽  
Terry Elliott
Keyword(s):  
Synaptic Plasticity ◽  
Stochastic Model ◽  
Higher Order ◽  
Spike Timing ◽  
Competitive Dynamics ◽  
Spike Timing Dependent Plasticity ◽  
Interaction Function ◽  
Dependent Plasticity ◽  
Order Interaction ◽  
Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

scholarly journals Effects of memristive synapse radiation interactions on learning in spiking neural networks

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Sumedha Gandharava Dahl ◽  
Robert C. Ivans ◽  
Kurtis D. Cantley
Keyword(s):  
Action Potentials ◽  
Radiation Effects ◽  
Synaptic Weight ◽  
Learning Rule ◽  
Spike Timing ◽  
Spiking Neural Network ◽  
Synaptic Action ◽  
Drift Model ◽  
Spatio Temporal ◽  
Post Synaptic Neuron

AbstractThis study uses advanced modeling and simulation to explore the effects of external events such as radiation interactions on the synaptic devices in an electronic spiking neural network. Specifically, the networks are trained using the spike-timing-dependent plasticity (STDP) learning rule to recognize spatio-temporal patterns (STPs) representing 25 and 100-pixel characters. Memristive synapses based on a TiO2 non-linear drift model designed in Verilog-A are utilized, with STDP learning behavior achieved through bi-phasic pre- and post-synaptic action potentials. The models are modified to include experimentally observed state-altering and ionizing radiation effects on the device. It is found that radiation interactions tend to make the connection between afferents stronger by increasing the conductance of synapses overall, subsequently distorting the STDP learning curve. In the absence of consistent STPs, these effects accumulate over time and make the synaptic weight evolutions unstable. With STPs at lower flux intensities, the network can recover and relearn with constant training. However, higher flux can overwhelm the leaky integrate-and-fire post-synaptic neuron circuits and reduce stability of the network.

scholarly journals Artificial synapses with a sponge-like double-layer porous oxide memristor

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Qin Gao ◽  
Anping Huang ◽  
Jing Zhang ◽  
Yuhang Ji ◽  
Jingjing Zhang ◽  
...  
Keyword(s):  
Double Layer ◽  
Layer Structure ◽  
Learning Experience ◽  
Rapid Development ◽  
Spike Timing ◽  
Synaptic Functions ◽  
Intelligence Studies ◽  
Bionic Structure ◽  
Porous Oxide ◽  
The Stability

AbstractClosely following the rapid development of artificial intelligence, studies of the human brain and neurobiology are focusing on the biological mechanisms of neurons and synapses. Herein, a memory system employing a nanoporous double-layer structure for simulation of synaptic functions is described. The sponge-like double-layer porous (SLDLP) oxide stack of Pt/porous LiCoO2/porous SiO2/Si is designed as presynaptic and postsynaptic membranes. This bionic structure exhibits high ON–OFF ratios up to 108 during the stability test, and data can be maintained for 105 s despite a small read voltage of 0.5 V. Typical synaptic functions, such as nonlinear transmission characteristics, spike-timing-dependent plasticity, and learning-experience behaviors, are achieved simultaneously with this device. Based on the hydrodynamic transport mechanism of water molecules in porous sponges and the principle of water storage, the synaptic behavior of the device is discussed. The SLDLP oxide memristor is very promising due to its excellent synaptic performance and potential in neuromorphic computing.

Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning

2001 ◽  
Vol 13 (10) ◽  
pp. 2221-2237 ◽  
Author(s):  
Rajesh P. N. Rao ◽  
Terrence J. Sejnowski
Keyword(s):  
Cortical Neuron ◽  
Action Potentials ◽  
Learning Rule ◽  
Spike Timing ◽  
Biophysical Model ◽  
Excitatory Synapses ◽  
Temporal Difference ◽  
Temporal Difference Learning ◽  
Neocortical Neurons ◽  
Hebbian Plasticity

A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window of Hebbian plasticity observed physiologically. Furthermore, the size and shape of the window vary with the distance of the synapse from the soma. Using a simple example, we show how a spike-timing-based temporal difference learning rule can allow a network of neocortical neurons to predict an input a few milliseconds before the input's expected arrival.

scholarly journals Event-Driven Simulation Scheme for Spiking Neural Networks Using Lookup Tables to Characterize Neuronal Dynamics

2006 ◽  
Vol 18 (12) ◽  
pp. 2959-2993 ◽  
Author(s):  
Eduardo Ros ◽  
Richard Carrillo ◽  
Eva M. Ortigosa ◽  
Boris Barbour ◽  
Rodrigo Agís
Keyword(s):  
Synaptic Plasticity ◽  
High Speed ◽  
Large Scale ◽  
Computational Cost ◽  
Brain Structures ◽  
Spike Timing ◽  
Neuronal Dynamics ◽  
Event Driven ◽  
Synaptic Dynamics

Nearly all neuronal information processing and interneuronal communication in the brain involves action potentials, or spikes, which drive the short-term synaptic dynamics of neurons, but also their long-term dynamics, via synaptic plasticity. In many brain structures, action potential activity is considered to be sparse. This sparseness of activity has been exploited to reduce the computational cost of large-scale network simulations, through the development of event-driven simulation schemes. However, existing event-driven simulations schemes use extremely simplified neuronal models. Here, we implement and evaluate critically an event-driven algorithm (ED-LUT) that uses precalculated look-up tables to characterize synaptic and neuronal dynamics. This approach enables the use of more complex (and realistic) neuronal models or data in representing the neurons, while retaining the advantage of high-speed simulation. We demonstrate the method's application for neurons containing exponential synaptic conductances, thereby implementing shunting inhibition, a phenomenon that is critical to cellular computation. We also introduce an improved two-stage event-queue algorithm, which allows the simulations to scale efficiently to highly connected networks with arbitrary propagation delays. Finally, the scheme readily accommodates implementation of synaptic plasticity mechanisms that depend on spike timing, enabling future simulations to explore issues of long-term learning and adaptation in large-scale networks.

Characterization of Brightness of Electroluminescent Device Using Powder Phosphor Composite with ZnO or TiO2

2008 ◽  
Vol 55 ◽  
pp. 150-153
Author(s):  
Mun Ja Kim ◽  
Sung Min Park ◽  
Tae Young Lee ◽  
Sang Hyun Park ◽  
Jin Young Kim ◽  
...  
Keyword(s):  
Low Voltage ◽  
Low Cost ◽  
Frequency Variation ◽  
Driving Voltage ◽  
Powder Phosphor ◽  
Previous Researcher ◽  
The Poor ◽  
Device Reliability ◽  
Coating Method

For the growth of Electroluminescent (EL) device market, the attention of many researchers is centered on improving the properties such as brightness, power consumption, device reliability, etc. The powder EL device is one of solutions for the easy mass production, the simplification of structure, and low cost. Although the powder process is the solution, that has the problem with the poor brightness than the film process. So, we focused on increasing the brightness of powder EL device. The emissive layer was made up the composites adding metal oxide nanopowder such as TiO2 and ZnO to powder phosphors. As the data of previous researcher, the TiO2 and ZnO had the different dominating traps by photovoltage measure, that is, TiO2 show hole traps, ZnO show electron traps [1]. The brightness of powder EL device proportions to the high electricfield formation. The TiO2 or ZnO in the powder phosphor composite can help the emission that may be advantageous to form high electricfield at low voltage. The EL devices with green ZnS phosphor were fabricated using spin coating method. The effect of TiO2 and ZnO on the luminescent property of EL device was investigated. The brightness was obtained as applied driving voltage at 400 Hz and frequency variation at 50 V.

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