Synaptic plasticity, metaplasticity and memory effects in hybrid organic–inorganic bismuth-based materials

Nanoscale ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 1080-1090 ◽  
Author(s):  
Tomasz Mazur ◽  
Piotr Zawal ◽  
Konrad Szaciłowski
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Plasticity ◽  
Memory Effects ◽  
Bismuth Iodide ◽  
Artificial Neural ◽  
Thin Film Devices

Herein, we present memristive, thin film devices made of methylammonium bismuth iodide that exhibit a wide variety of neuromorphic effects simultaneously. Described materials have the potential to become universal cells in artificial neural networks.

Neuromorphic Functional Modules of Spiking Neural Network

2021 ◽  
Vol 23 (6) ◽  
pp. 317-326
Author(s):  
E.A. Ryndin ◽  
◽  
N.V. Andreeva ◽  
V.V. Luchinin ◽  
K.S. Goncharov ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Plasticity ◽  
Data Processing ◽  
Circuit Design ◽  
Spiking Neural Networks ◽  
Spiking Neural Network ◽  
Wide Range ◽  
Artificial Neural

In the current era, design and development of artificial neural networks exploiting the architecture of the human brain have evolved rapidly. Artificial neural networks effectively solve a wide range of common for artificial intelligence tasks involving data classification and recognition, prediction, forecasting and adaptive control of object behavior. Biologically inspired underlying principles of ANN operation have certain advantages over the conventional von Neumann architecture including unsupervised learning, architectural flexibility and adaptability to environmental change and high performance under significantly reduced power consumption due to heavy parallel and asynchronous data processing. In this paper, we present the circuit design of main functional blocks (neurons and synapses) intended for hardware implementation of a perceptron-based feedforward spiking neural network. As the third generation of artificial neural networks, spiking neural networks perform data processing utilizing spikes, which are discrete events (or functions) that take place at points in time. Neurons in spiking neural networks initiate precisely timing spikes and communicate with each other via spikes transmitted through synaptic connections or synapses with adaptable scalable weight. One of the prospective approach to emulate the synaptic behavior in hardware implemented spiking neural networks is to use non-volatile memory devices with analog conduction modulation (or memristive structures). Here we propose a circuit design for functional analogues of memristive structure to mimic a synaptic plasticity, pre- and postsynaptic neurons which could be used for developing circuit design of spiking neural network architectures with different training algorithms including spike-timing dependent plasticity learning rule. Two different circuits of electronic synapse were developed. The first one is an analog synapse with photoresistive optocoupler used to ensure the tunable conductivity for synaptic plasticity emulation. While the second one is a digital synapse, in which the synaptic weight is stored in a digital code with its direct conversion into conductivity (without digital-to-analog converter andphotoresistive optocoupler). The results of the prototyping of developed circuits for electronic analogues of synapses, pre- and postsynaptic neurons and the study of transient processes are presented. The developed approach could provide a basis for ASIC design of spiking neural networks based on CMOS (complementary metal oxide semiconductor) design technology.

Estimation of optical constants of thin film by the use of artificial neural networks

1996 ◽  
Vol 35 (25) ◽  
pp. 5035 ◽  
Author(s):  
Yuan-sheng Ma ◽  
Xu Liu ◽  
Pei-fu Gu ◽  
Jin-fa Tang
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optical Constants ◽  
Artificial Neural

scholarly journals Precision Modeling: Application of Metaheuristics on Current–Voltage Curves of Superconducting Films

Electronics ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 138 ◽  
Author(s):  
Syed Naqvi ◽  
Tallha Akram ◽  
Sajjad Haider ◽  
Muhammad Kamran ◽  
Aamir Shahzad ◽  
...  
Keyword(s):  
Thin Film ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Critical Parameters ◽  
Superconducting Thin Film ◽  
Current Voltage ◽  
Wide Range ◽  
Artificial Neural

Contemplating the importance of studying current–voltage curves in superconductivity, it has been recently and rightly argued that their approximation, rather than incessant measurements, seems to be a more viable option. This especially becomes bona fide when the latter needs to be recorded for a wide range of critical parameters including temperature and magnetic field, thereby becoming a tedious monotonous procedure. Artificial neural networks have been recently put forth as one methodology for approximating these so-called electrical measurements for various geometries of antidots on a superconducting thin film. In this work, we demonstrate that the prediction accuracy, in terms of mean-squared error, achieved by artificial neural networks is rather constrained, and, due to their immense credence on randomly generated networks’ coefficients, they may result in vastly varying prediction accuracies for different geometries, experimental conditions, and their own tunable parameters. This inconsistency in prediction accuracies is resolved by controlling the uncertainty in networks’ initialization and coefficients’ generation by means of a novel entropy based genetic algorithm. The proposed method helps in achieving a substantial improvement and consistency in the prediction accuracy of current–voltage curves in comparison to existing works, and is amenable to various geometries of antidots, including rectangular, square, honeycomb, and kagome, on a superconducting thin film.

Sign in / Sign up

Export Citation Format

Share Document