scholarly journals Mapping Hebbian Learning Rules to Coupling Resistances for Oscillatory Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Corentin Delacour ◽  
Aida Todri-Sanial
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Hebbian Learning ◽  
Hopfield Neural Network ◽  
Analytical Framework ◽  
Relaxation Oscillators ◽  
Oscillatory Neural Network ◽  
Network Functionality ◽  
Fully Connected ◽  
Neuromorphic Architecture

Oscillatory Neural Network (ONN) is an emerging neuromorphic architecture with oscillators representing neurons and information encoded in oscillator's phase relations. In an ONN, oscillators are coupled with electrical elements to define the network's weights and achieve massive parallel computation. As the weights preserve the network functionality, mapping weights to coupling elements plays a crucial role in ONN performance. In this work, we investigate relaxation oscillators based on VO2 material, and we propose a methodology to map Hebbian coefficients to ONN coupling resistances, allowing a large-scale ONN design. We develop an analytical framework to map weight coefficients into coupling resistor values to analyze ONN architecture performance. We report on an ONN with 60 fully-connected oscillators that perform pattern recognition as a Hopfield Neural Network.

Experimental Demonstration of Hopfield Neural Network using DNA molecules

2011 ◽  
Vol 1346 ◽  
Author(s):  
Hayri E. Akin ◽  
Dundar Karabay ◽  
Allen P. Mills ◽  
Cengiz S. Ozkan ◽  
Mihrimah Ozkan
Keyword(s):  
Neural Network ◽  
Data Storage ◽  
Large Scale ◽  
Fault Tolerant ◽  
Hamiltonian Path ◽  
Hopfield Neural Network ◽  
Dna Molecules ◽  
Processing Power ◽  
Dna Strands ◽  
Molecular Reactions

ABSTRACTDNA Computing is a rapidly-developing interdisciplinary area which could benefit from more experimental results to solve problems with the current biological tools. In this study, we have integrated microelectronics and molecular biology techniques for showing the feasibility of Hopfield Neural Network using DNA molecules. Adleman’s seminal paper in 1994 showed that DNA strands using specific molecular reactions can be used to solve the Hamiltonian Path Problem. This accomplishment opened the way for possibilities of massively parallel processing power, remarkable energy efficiency and compact data storage ability with DNA. However, in various studies, small departures from the ideal selectivity of DNA hybridization lead to significant undesired pairings of strands and that leads to difficulties in schemes for implementing large Boolean functions using DNA. Therefore, these error prone reactions in the Boolean architecture of the first DNA computers will benefit from fault tolerance or error correction methods and these methods would be essential for large scale applications. In this study, we demonstrate the operation of six dimensional Hopfield associative memory storing various memories as an archetype fault tolerant neural network implemented using DNA molecular reactions. The response of the network suggests that the protocols could be scaled to a network of significantly larger dimensions. In addition the results are read on a Silicon CMOS platform exploiting the semiconductor processing knowledge for fast and accurate hybridization rates.

scholarly journals On permutation symmetries of hopfield model neural network

2001 ◽  
Vol 6 (2) ◽  
pp. 129-136 ◽  
Author(s):  
Jiyang Dong ◽  
Shenchu Xu ◽  
Zhenxiang Chen ◽  
Boxi Wu
Keyword(s):  
Neural Network ◽  
Synaptic Weight ◽  
Hebbian Learning ◽  
Learning Algorithm ◽  
Point Group ◽  
Hopfield Neural Network ◽  
Weight Matrix ◽  
Hopfield Model

Discrete Hopfield neural network (DHNN) is studied by performing permutation operations on the synaptic weight matrix. The storable patterns set stored with Hebbian learning algorithm in a network without losing memories is studied, and a condition which makes sure all the patterns of the storable patterns set have a same basin size of attraction is proposed. Then, the permutation symmetries of the network are studied associating with the stored patterns set. A construction of the storable patterns set satisfying that condition is achieved by consideration of their invariance under a point group.

scholarly journals Optimal CNN–Hopfield Network for Pattern Recognition Based on a Genetic Algorithm

Algorithms ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 11
Author(s):  
Fekhr Eddine Keddous ◽  
Amir Nakib
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Hopfield Neural Network ◽  
Representation Learning ◽  
Problem Formulation ◽  
Hopfield Network ◽  
Learning Capabilities ◽  
A Chain ◽  
Fully Connected ◽  
Real World Problems

Convolutional neural networks (CNNs) have powerful representation learning capabilities by automatically learning and extracting features directly from inputs. In classification applications, CNN models are typically composed of: convolutional layers, pooling layers, and fully connected (FC) layer(s). In a chain-based deep neural network, the FC layers contain most of the parameters of the network, which affects memory occupancy and computational complexity. For many real-world problems, speeding up inference time is an important matter because of the hardware design implications. To deal with this problem, we propose the replacement of the FC layers with a Hopfield neural network (HNN). The proposed architecture combines both a CNN and an HNN: A pretrained CNN model is used for feature extraction, followed by an HNN, which is considered as an associative memory that saves all features created by the CNN. Then, to deal with the limitation of the storage capacity of the HNN, the proposed work uses multiple HNNs. To optimize this step, the knapsack problem formulation is proposed, and a genetic algorithm (GA) is used solve it. According to the results obtained on the Noisy MNIST Dataset, our work outperformed the state-of-the-art algorithms.

scholarly journals Automated Chest Radiographs Triage Reading by a Deep Learning Referee Network

2021 ◽  
Author(s):  
Rafael Lopez-Gonzalez ◽  
Jose Sanchez-Garcia ◽  
Belen Fos-Guarinos ◽  
Fabio Garcia-Castro ◽  
Angel Alberich-Bayarri ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Language Processing ◽  
Large Scale ◽  
Area Under The Curve ◽  
Chest Radiographs ◽  
Radiology Reports ◽  
Chest X Ray ◽  
Diagnosis Tool ◽  
Fully Connected

Chest radiographs are often obtained as a screening for early diagnosis tool to rule out abnormalities mainly related to different cardiovascular and respiratory diseases. Reading and reporting numerous chest radiographs is a complex and time-consuming task. This research proposes and evaluates a deep learning (DL) approach based on convolutional neural networks (CNN) combined with a referee fully connected neural network as a computer-aided diagnosis tool in chest X-ray triage and worklist prioritization. The CNN models were trained with a combination of three large scale databases: ChestX-ray14, CheXpert and PadChest. The final database contained 327,176 images labeled with findings obtained by natural language processing (NLP) techniques applied to the radiology reports. The dataset was split in 16 different balanced binary partitions, which were used to train 16 finding-specific classification CNNs. Afterwards, a normal vs abnormal partition of the dataset was created, being abnormal the presence of at least one pathologic change. This final partition was used to train a fully connected neural network as referee that was fed with all the 16 previously trained outcomes. The Area Under the Curve (AUC) analysis evaluated and compared the performance of the models. The system was successfully implemented and evaluated with a test set of 3400 images. The AUC of the normal vs abnormal classification was 0.94. The highest AUC of the finding-specific classifiers was 0.99 for hernia. The proposed system can be used to assist radiologists identifying abnormal exams, allowing a time-efficiency triage approach.

Hopfield neural network in magnetic textures with intrinsic Hebbian learning

2021 ◽  
Vol 104 (18) ◽  
Author(s):  
Weichao Yu ◽  
Jiang Xiao ◽  
Gerrit E. W. Bauer
Keyword(s):  
Neural Network ◽  
Hebbian Learning ◽  
Hopfield Neural Network

Efficient Associative Memory Based on a Nonlinear Function Constitution and Dynamic Synapses

2011 ◽  
Vol 225-226 ◽  
pp. 479-482
Author(s):  
Min Xia ◽  
Ying Cao Zhang ◽  
Xiao Ling Ye
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Hebbian Learning ◽  
Nonlinear Function ◽  
Learning Rule ◽  
Hopfield Neural Network ◽  
Connection Weight ◽  
Outer Product ◽  
Hebbian Learning Rule ◽  
Dynamic Synapses

Nonlinear function constitution and dynamic synapses, against spurious state for Hopfield neural network are proposed. The model of the dynamical connection weight and the updating scheme of the states of neurons are given. Nonlinear function constitution improves the conventional Hebbian learning rule with linear outer product method. Simulation results show that both nonlinear function constitution and dynamic synapses can effectively increase the ability of error tolerance; furthermore, associative memory of neural network with the new method can both enlarge attractive basin and increase storage capacity.

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