scholarly journals Detecting Generic Music Features with Single Layer Feedforward Network using Unsupervised Hebbian Computation

2020 ◽  
Vol 12 (2) ◽  
pp. 1-20
Author(s):  
Sourav Das ◽  
Anup Kumar Kolya
Keyword(s):  
Neural Network ◽  
Feature Detection ◽  
Feature Recognition ◽  
Hebbian Learning ◽  
Feature Learning ◽  
Single Layer ◽  
Feedforward Network ◽  
New Information ◽  
Unsupervised Training ◽  
And Performance

In this work, the authors extract information on distinct baseline features from a popular open-source music corpus and explore new recognition techniques by applying unsupervised Hebbian learning techniques on our single-layer neural network using the same dataset. They show the detailed empirical findings to simulate how such an algorithm can help a single layer feedforward network in training for music feature learning as patterns. The unsupervised training algorithm enhances the proposed neural network to achieve an accuracy of 90.36% for successful music feature detection. For comparative analysis against similar tasks, they put their results with the likes of several previous benchmark works. They further discuss the limitations and thorough error analysis of the work. They hope to discover and gather new information about this particular classification technique and performance, also further understand future potential directions that could improve the art of computational music feature recognition.

scholarly journals A Neural Model of Schemas and Memory Consolidation

2018 ◽  
Author(s):  
Tiffany Hwu ◽  
Jeffrey L. Krichmar
Keyword(s):  
Neural Network ◽  
Memory Consolidation ◽  
Hebbian Learning ◽  
Dynamic Environment ◽  
Neural Model ◽  
Vital Role ◽  
Learning To Learn ◽  
New Information ◽  
Context Dependent

AbstractThe ability to behave differently according to the situation is essential for survival in a dynamic environment. This requires past experiences to be encoded and retrieved alongside the contextual schemas in which they occurred. The complementary learning systems theory suggests that these schemas are acquired through gradual learning via the neocortex and rapid learning via the hippocampus. However, it has also been shown that new information matching a preexisting schema can bypass the gradual learning process and be acquired rapidly, suggesting that the separation of memories into schemas is useful for flexible learning. While there are theories of the role of schemas in memory consolidation, we lack a full understanding of the mechanisms underlying this function. For this reason, we created a biologically plausible neural network model of schema consolidation that studies several brain areas and their interactions. The model uses a rate-coded multilayer neural network with contrastive Hebbian learning to learn context-specific tasks. Our model suggests that the medial prefrontal cortex supports context-dependent behaviors by learning representations of schemas. Additionally, sparse random connections in the model from the ventral hippocampus to the hidden layers of the network gate neuronal activity depending on their involvement within the current schema, thus separating the representations of new and prior schemas. Contrastive Hebbian learning may function similarly to oscillations in the hippocampus, alternating between clamping and unclamping the output layer of the network to drive learning. Lastly, the model shows the vital role of neuromodulation, as a neuromodulatory area detects the certainty of whether new information is consistent with prior schemas and modulates the speed of memory encoding accordingly. Along with the insights that this model brings to the neurobiology of memory, it further provides a basis for creating context-dependent memories while preventing catastrophic forgetting in artificial neural networks.

On Developing and Performance Evaluation of Adaptive Second Order Neural Network With GA-Based Training (ASONN-GA) for Financial Time Series Prediction

2018 ◽  
pp. 231-263 ◽  
Author(s):  
Sarat Chandra Nayak ◽  
Bijan Bihari Misra ◽  
Himansu Sekhar Behera
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Financial Time Series ◽  
Single Layer ◽  
Second Order ◽  
Forecasting Accuracy ◽  
Training Time ◽  
Financial Time ◽  
And Performance

Financial time series forecasting has been regarded as a challenging issue because of successful prediction could yield significant profit, hence require an efficient prediction system. Conventional ANN based models are not competent systems. Higher order neural networks have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence. With the aim of achieving improved forecasting accuracy, this article develops and evaluates the performance of an adaptive single layer second order neural network with GA based training (ASONN-GA). The global search ability of GA has been incorporated with the better generalization ability of a second order neural network and the model is found quite capable in handling the uncertainties and nonlinearities associated with the financial time series. The model takes minimal input data and considered the partially optimized weight set from previous training, hence a significant reduction in training time. The efficiency of the model has been evaluated by forecasting one-step-ahead closing prices and exchange rates of five real stock markets and it is revealed that the ASONN-GA model achieves better forecasting accuracy over other state of the art models.

scholarly journals Controlling a Small Mobile 3-Pi Robot Movement in a Maze Via the Neural Network Using Back-Propagation Learning Method

2021 ◽  
Vol 29 (49) ◽  
pp. 43-50
Author(s):  
Dušan Horváth ◽  
Zuzana Červeňanská
Keyword(s):  
Neural Network ◽  
Hebbian Learning ◽  
Back Propagation ◽  
Single Layer ◽  
Movement Control ◽  
Feedforward Neural Network ◽  
Neural Network Learning ◽  
Network Learning ◽  
The Neural Network ◽  
Infra Red

Abstract The contribution is focused on technical implementation of controlling a small mobile 3Pi robot in a maze along a predefined guide line where the control of the acquired direction of the robot’s movement was provided by a neural network. The weights (memory) of the neuron were calculated using a feedforward neural network learning via the Back-propagation method. This article fastens on the paper by the title “Movement control of a small mobile 3-pi robot in a maze using artificial neural network”, where Hebbian learning was used for a single-layer neural network. The reflectance infra-red sensors performed as input sensors. The result of this research is the evaluation based on the experiments that served to compare different training sets with the learning methods when moving a mobile robot in a maze.

scholarly journals A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis

2021 ◽  
pp. 1-44
Author(s):  
David Lipshutz ◽  
Yanis Bahroun ◽  
Siavash Golkar ◽  
Anirvan M. Sengupta ◽  
Dmitri B. Chklovskii
Keyword(s):  
Neural Network ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Pyramidal Neurons ◽  
Hebbian Learning ◽  
Single Layer ◽  
Hebbian Plasticity ◽  
Cortical Pyramidal Neurons ◽  
Update Rules

Abstract Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize the correlations between the projections. To this end, we seek a multichannel CCA algorithm that can be implemented in a biologically plausible neural network. For biological plausibility, we require that the network operates in the online setting and its synaptic update rules are local. Starting from a novel CCA objective function, we derive an online optimization algorithm whose optimization steps can be implemented in a single-layer neural network with multicompartmental neu rons and local non-Hebbian learning rules. We also derive an extension of our online CCA algorithm with adaptive output rank and output whitening. Interestingly, the extension maps onto a neural network whose neural architecture and synaptic updates resemble neural circuitry and non-Hebbian plasticity observed in the cortex.

scholarly journals ARTFLOW: A Fast, Biologically Inspired Neural Network That Learns Optic Flow Templates for Self-Motion Estimation

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8217
Author(s):  
Oliver W. Layton
Keyword(s):  
Neural Network ◽  
Motion Estimation ◽  
Optic Flow ◽  
Hebbian Learning ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Hierarchical Architecture ◽  
Biologically Inspired ◽  
Fuzzy Art ◽  
Self Motion

Most algorithms for steering, obstacle avoidance, and moving object detection rely on accurate self-motion estimation, a problem animals solve in real time as they navigate through diverse environments. One biological solution leverages optic flow, the changing pattern of motion experienced on the eye during self-motion. Here I present ARTFLOW, a biologically inspired neural network that learns patterns in optic flow to encode the observer’s self-motion. The network combines the fuzzy ART unsupervised learning algorithm with a hierarchical architecture based on the primate visual system. This design affords fast, local feature learning across parallel modules in each network layer. Simulations show that the network is capable of learning stable patterns from optic flow simulating self-motion through environments of varying complexity with only one epoch of training. ARTFLOW trains substantially faster and yields self-motion estimates that are far more accurate than a comparable network that relies on Hebbian learning. I show how ARTFLOW serves as a generative model to predict the optic flow that corresponds to neural activations distributed across the network.

Digitalization system of ancient architecture decoration art based on neural network and image features

2020 ◽  
pp. 1-12
Author(s):  
Wu Xin ◽  
Qiu Daping
Keyword(s):  
Neural Network ◽  
Construction Industry ◽  
Three Dimensional ◽  
Performance Testing ◽  
Image Features ◽  
Three Dimensional Model ◽  
Performance Effect ◽  
Data Process ◽  
And Performance ◽  
Construction Mode

The inheritance and innovation of ancient architecture decoration art is an important way for the development of the construction industry. The data process of traditional ancient architecture decoration art is relatively backward, which leads to the obvious distortion of the digitalization of ancient architecture decoration art. In order to improve the digital effect of ancient architecture decoration art, based on neural network, this paper combines the image features to construct a neural network-based ancient architecture decoration art data system model, and graphically expresses the static construction mode and dynamic construction process of the architecture group. Based on this, three-dimensional model reconstruction and scene simulation experiments of architecture groups are realized. In order to verify the performance effect of the system proposed in this paper, it is verified through simulation and performance testing, and data visualization is performed through statistical methods. The result of the study shows that the digitalization effect of the ancient architecture decoration art proposed in this paper is good.

scholarly journals Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2648
Author(s):  
Muhammad Aamir ◽  
Tariq Ali ◽  
Muhammad Irfan ◽  
Ahmad Shaf ◽  
Muhammad Zeeshan Azam ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Natural Disasters ◽  
Deep Convolutional Neural Network ◽  
Multispectral Images ◽  
Learning Techniques ◽  
Proposed Model ◽  
Disaster Intensity ◽  
And Performance

Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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