Graded Patterns in Attractor Networks explores how noise can exist in large neural networks like the brain. The study introduces graded firing patterns, where neuron firing rates vary across populations, rather than being uniform. Simulations found graded patterns decreased reaction times and increased variability compared to uniform patterns. This suggests graded firing represents increased noise but may play a functional role in neural processing like memory retrieval.
1) A novel method is presented for image processing and pattern recognition using Discrete Fourier Transformations on the global pulse signal of a pulse-coupled neural network (PCNN).
2) The PCNN transforms images by removing unimportant details while improving quality without losing shape or pattern information.
3) We analyze the PCNN pulse to achieve better quality image processing, scale and translation-independent recognition of isolated objects.
Image Compression Using Wavelet Packet TreeIDES Editor
Methods of compressing data prior to storage and
transmission are of significant practical and commercial
interest. The necessity in image compression continuously
grows during the last decade. The image compression includes
transform of image, quantization and encoding. One of the
most powerful and perspective approaches in this area is
image compression using discrete wavelet transform. This
paper describes a new approach called as wavelet packet tree
for image compression. It constructs the best tree on the basis
of Shannon entropy. This new approach checks the entropy of
decomposed nodes (child nodes) with entropy of node, which
has been decomposed (parent node) and takes the decision of
decomposition of a node. In addition, authors have proposed
an adaptive thresholding for quantization, which is based on
type of wavelet used and nature of image. Performance of the
proposed algorithm is compared with existing wavelet
transform algorithm in terms of percentage of zeros and
percentage of energy retained and signals to noise ratio.
This document discusses using a 2D dual-tree complex discrete wavelet transform (2D-DTDWT) for image denoising. It begins by explaining issues with the discrete wavelet transform and how the dual-tree complex wavelet transform addresses these. It then describes how the 2D-DTDWT works by applying 1D dual-tree transforms along rows and columns. For denoising, different threshold values are applied to the wavelet coefficients before reconstructing the image. The algorithm is tested on images corrupted with noise and performance is evaluated using metrics like PSNR for different thresholds.
This document proposes a cognitive-inspired model for self-organizing networks. It begins with motivation for developing self-organizing overlay networks that can optimize their structure without global information. It then describes a scenario where nodes in a connected network want to retrieve items from each other using a limited number of links. The document goes on to present the cognitive-inspired hub detection algorithm, which uses concepts like diffusion, competitive interaction, and cognitive dissonance to identify hub nodes. It evaluates the algorithm through numerical simulations that aim to maximize the number of reachable items or minimize the energy used. The results show the cognitive approach outperforms a randomized algorithm.
Poster Toward a realistic retinal simulatorHassan Nasser
The document discusses improving the statistical realism of a retina simulator called VirtualRetina by implementing additional retinal circuitry features. While VirtualRetina can accurately model individual retinal ganglion cell responses, it does not capture the synchronization and correlations seen in real retinal data. Implementing gap junction connections between retinal ganglion cells and modeling feedback from amacrine cells could help VirtualRetina better match real data statistics. The goal is to produce a statistically plausible retinal output that can serve as realistic input for models of the visual cortex.
AACIMP 2010 Summer School lecture by Anton Chizhov. "Physics, Chemistry and Living Systems" stream. "Neuron-Computer Interface in Dynamic-Clamp Experiments. Models of Neuronal Populations and Visual Cortex" course. Part 3.
More info at http://summerschool.ssa.org.ua
This document proposes semantically integrating laser and vision for pedestrian detection. The goals are to detect objects using laser and vision, provide a proof-of-concept for pedestrian detection that can be applied to other objects, recover object localization, and perform fusion in a context-aware manner without entirely relying on laser. The proposed method uses laser segmentation, laser-image registration, sensor-driven detectors including an HLSM-FINT ensemble, and semantic fusion through a Markov random field to infer detections and make decisions. Experimental results demonstrate the sensor calibration and detectors.
1) A novel method is presented for image processing and pattern recognition using Discrete Fourier Transformations on the global pulse signal of a pulse-coupled neural network (PCNN).
2) The PCNN transforms images by removing unimportant details while improving quality without losing shape or pattern information.
3) We analyze the PCNN pulse to achieve better quality image processing, scale and translation-independent recognition of isolated objects.
Image Compression Using Wavelet Packet TreeIDES Editor
Methods of compressing data prior to storage and
transmission are of significant practical and commercial
interest. The necessity in image compression continuously
grows during the last decade. The image compression includes
transform of image, quantization and encoding. One of the
most powerful and perspective approaches in this area is
image compression using discrete wavelet transform. This
paper describes a new approach called as wavelet packet tree
for image compression. It constructs the best tree on the basis
of Shannon entropy. This new approach checks the entropy of
decomposed nodes (child nodes) with entropy of node, which
has been decomposed (parent node) and takes the decision of
decomposition of a node. In addition, authors have proposed
an adaptive thresholding for quantization, which is based on
type of wavelet used and nature of image. Performance of the
proposed algorithm is compared with existing wavelet
transform algorithm in terms of percentage of zeros and
percentage of energy retained and signals to noise ratio.
This document discusses using a 2D dual-tree complex discrete wavelet transform (2D-DTDWT) for image denoising. It begins by explaining issues with the discrete wavelet transform and how the dual-tree complex wavelet transform addresses these. It then describes how the 2D-DTDWT works by applying 1D dual-tree transforms along rows and columns. For denoising, different threshold values are applied to the wavelet coefficients before reconstructing the image. The algorithm is tested on images corrupted with noise and performance is evaluated using metrics like PSNR for different thresholds.
This document proposes a cognitive-inspired model for self-organizing networks. It begins with motivation for developing self-organizing overlay networks that can optimize their structure without global information. It then describes a scenario where nodes in a connected network want to retrieve items from each other using a limited number of links. The document goes on to present the cognitive-inspired hub detection algorithm, which uses concepts like diffusion, competitive interaction, and cognitive dissonance to identify hub nodes. It evaluates the algorithm through numerical simulations that aim to maximize the number of reachable items or minimize the energy used. The results show the cognitive approach outperforms a randomized algorithm.
Poster Toward a realistic retinal simulatorHassan Nasser
The document discusses improving the statistical realism of a retina simulator called VirtualRetina by implementing additional retinal circuitry features. While VirtualRetina can accurately model individual retinal ganglion cell responses, it does not capture the synchronization and correlations seen in real retinal data. Implementing gap junction connections between retinal ganglion cells and modeling feedback from amacrine cells could help VirtualRetina better match real data statistics. The goal is to produce a statistically plausible retinal output that can serve as realistic input for models of the visual cortex.
AACIMP 2010 Summer School lecture by Anton Chizhov. "Physics, Chemistry and Living Systems" stream. "Neuron-Computer Interface in Dynamic-Clamp Experiments. Models of Neuronal Populations and Visual Cortex" course. Part 3.
More info at http://summerschool.ssa.org.ua
This document proposes semantically integrating laser and vision for pedestrian detection. The goals are to detect objects using laser and vision, provide a proof-of-concept for pedestrian detection that can be applied to other objects, recover object localization, and perform fusion in a context-aware manner without entirely relying on laser. The proposed method uses laser segmentation, laser-image registration, sensor-driven detectors including an HLSM-FINT ensemble, and semantic fusion through a Markov random field to infer detections and make decisions. Experimental results demonstrate the sensor calibration and detectors.
Dynamic Kohonen Network for Representing Changes in InputsJean Fecteau
The document describes a system that uses a self-organizing map (Kohonen network) to dynamically represent changes to a set of inputs over time. The system is able to recognize new inputs, remove unlikely inputs, and merge similar inputs while continuously updating its representation. It was tested on simulated 3D color vector inputs with added noise. The system generally converged quickly and accurately but was sensitive to noise and struggled with similar inputs due to its binary region definitions. While flawed, it demonstrated the ability to adapt its knowledge to changes in inputs without reinitializing.
The document discusses machine learning techniques. It describes how machines can learn from examples, through experience and adaptation. It evaluates methods for acquiring and representing knowledge, including decision trees, neural networks and genetic algorithms. While machine learning techniques have benefits like learning from experience and generalizing, they also have drawbacks such as not knowing whether the learned knowledge is completely correct.
Here are the completed statements with appropriate infinitives:
1. The purpose of the study is to investigate the effectiveness of the campaign.
2. The objective of the investigation is to determine the cause of the road accidents.
3. The aim of the report is to examine the effects of stress on employees.
4. The study intends to identify the cause of the landslide.
5. The research hopes to identify the reasons behind students’ lack of moral values.
This document presents a computational model and simulation of place cells using a continuous attractor neural network (CANN). The simulation implements a virtual robot and four environments. Various conditions are applied to the simulation to observe the activation patterns produced by the CANN. The results are compared to biological studies on rat place cells. The model demonstrates place cell behavior consistent with biological studies but requires further development to provide full robot navigation capabilities.
This document explains how to view and edit different versions of a wiki assignment in Moodle. It describes clicking on the history tab to see past versions, and using the browse, fetch-back, and diff commands to view specific versions, edit an earlier version, and compare changes between versions. Color coding is used to identify deleted and new text when comparing versions.
My Three Ex’s: A Data Science Approach for Applied Machine LearningDaniel Tunkelang
The document discusses machine learning and introduces "three ex's" - Express, Explain, and Experiment - as a framework.
Express involves defining an objective function and collecting training data to understand utility and inputs. Explain focuses on model explainability over accuracy, using linear models or decision trees initially before upgrading models. Experiment emphasizes that experiments are now cheap, but to test variables disciplined and optimize the speed of learning.
This document provides an overview of neural networks. It discusses that neural networks are composed of interconnected processing units similar to neurons in the brain. Neural networks can learn patterns from examples through training and are well-suited for problems that are difficult to solve with traditional algorithms. The document outlines common neural network architectures like feedforward and feedback networks. It also discusses neural network learning methods and applications.
Entropy based algorithm for community detection in augmented networksJuan David Cruz-Gómez
The document proposes an entropy-based algorithm to detect communities in augmented social networks. It begins with an introduction that motivates using both the graph structure and node attributes to find communities. It then outlines the clustering algorithm, which first uses modularity optimization on the graph to generate an initial partition, and then performs entropy optimization on the partition using the node attributes. Experimental results on student networks show that using attributes leads to different community configurations than using the graph alone, and that the algorithm runs in linear time and memory usage.
This document provides an overview of artificial neural networks and supervised learning. It discusses how artificial neural networks are modeled after biological neural networks in the brain. The basic building block of both biological and artificial neural networks is the neuron. A single neuron is then described as a simple computing element that takes weighted inputs and compares them to a threshold. The perceptron, one of the earliest and simplest types of artificial neural networks, is then introduced and its learning process via weight adjustments is explained.
This document provides an overview of artificial neural networks. It begins with definitions of artificial neural networks and how they are analogous to biological neural networks. It then discusses the basic structure of artificial neural networks, including different types of networks like feedforward, recurrent, and convolutional networks. Key concepts in artificial neural networks like neurons, weights, forward/backward propagation, and overfitting/underfitting are also explained. The document concludes with limitations of neural networks and references.
The document discusses adaptive channel equalization using neural networks. It provides an overview of neural networks and their application to channel equalization. Specifically, it summarizes various neural network architectures that have been used for equalization, including multilayer perceptrons, functional link artificial neural networks, Chebyshev neural networks, and radial basis function networks. It compares the bit error rate performance of these different neural network equalizers with traditional linear equalizers such as LMS and RLS. Overall, the document finds that neural network equalizers can better handle nonlinear channel distortions compared to linear equalizers and that radial basis function networks provide particularly good performance for channel equalization applications.
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements similar to neurons. The key aspects covered are:
- Neural networks can perform tasks that are difficult for traditional algorithms, such as pattern recognition.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections and can adapt based on training data.
- Training involves presenting examples to the network and adjusting the weighted connections between nodes until the network outputs the desired targets.
- Once trained, a neural network can be used to analyze new input data in a similar way to the brain.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
Artificial neural networks (ANNs) are inspired by biological neural networks and are composed of interconnected processing elements called neurons. ANNs are configured through a learning process to solve problems like pattern recognition or data classification. Early research in the 1940s and 1950s laid the foundations, like McCulloch and Pitts developing the first neural network model and Hebb developing the first learning rule. ANNs use weighted connections and activation functions to learn from examples through training. Feedforward and feedback networks differ in whether signals travel in one or both directions between layers of neurons. Perceptrons were influential early neural network models that could perform tasks linear programs could not.
Neural networks are mathematical models inspired by biological neural networks. They are useful for pattern recognition and data classification through a learning process of adjusting synaptic connections between neurons. A neural network maps input nodes to output nodes through an arbitrary number of hidden nodes. It is trained by presenting examples to adjust weights using methods like backpropagation to minimize error between actual and predicted outputs. Neural networks have advantages like noise tolerance and not requiring assumptions about data distributions. They have applications in finance, marketing, and other fields, though designing optimal network topology can be challenging.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
Neural networks of artificial intelligencealldesign
An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs, performs calculations, and outputs a value. ANNs can be trained to learn patterns from data through examples to perform tasks like classification, prediction, clustering, and association. Common ANN architectures include multilayer perceptrons, convolutional neural networks, and recurrent neural networks.
Artificial Neural Networks ppt.pptx for final sem cseNaveenBhajantri1
This document provides an overview of artificial neural networks. It discusses the biological inspiration from neurons in the brain and how artificial neural networks mimic this structure. The key components of artificial neurons and various network architectures are described, including fully connected, layered, feedforward, and modular networks. Supervised and unsupervised learning approaches are covered, with backpropagation highlighted as a commonly used supervised algorithm. Applications of neural networks are mentioned in areas like medicine, business, marketing and credit evaluation. Advantages include the ability to handle complex nonlinear problems and noisy data.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Dynamic Kohonen Network for Representing Changes in InputsJean Fecteau
The document describes a system that uses a self-organizing map (Kohonen network) to dynamically represent changes to a set of inputs over time. The system is able to recognize new inputs, remove unlikely inputs, and merge similar inputs while continuously updating its representation. It was tested on simulated 3D color vector inputs with added noise. The system generally converged quickly and accurately but was sensitive to noise and struggled with similar inputs due to its binary region definitions. While flawed, it demonstrated the ability to adapt its knowledge to changes in inputs without reinitializing.
The document discusses machine learning techniques. It describes how machines can learn from examples, through experience and adaptation. It evaluates methods for acquiring and representing knowledge, including decision trees, neural networks and genetic algorithms. While machine learning techniques have benefits like learning from experience and generalizing, they also have drawbacks such as not knowing whether the learned knowledge is completely correct.
Here are the completed statements with appropriate infinitives:
1. The purpose of the study is to investigate the effectiveness of the campaign.
2. The objective of the investigation is to determine the cause of the road accidents.
3. The aim of the report is to examine the effects of stress on employees.
4. The study intends to identify the cause of the landslide.
5. The research hopes to identify the reasons behind students’ lack of moral values.
This document presents a computational model and simulation of place cells using a continuous attractor neural network (CANN). The simulation implements a virtual robot and four environments. Various conditions are applied to the simulation to observe the activation patterns produced by the CANN. The results are compared to biological studies on rat place cells. The model demonstrates place cell behavior consistent with biological studies but requires further development to provide full robot navigation capabilities.
This document explains how to view and edit different versions of a wiki assignment in Moodle. It describes clicking on the history tab to see past versions, and using the browse, fetch-back, and diff commands to view specific versions, edit an earlier version, and compare changes between versions. Color coding is used to identify deleted and new text when comparing versions.
My Three Ex’s: A Data Science Approach for Applied Machine LearningDaniel Tunkelang
The document discusses machine learning and introduces "three ex's" - Express, Explain, and Experiment - as a framework.
Express involves defining an objective function and collecting training data to understand utility and inputs. Explain focuses on model explainability over accuracy, using linear models or decision trees initially before upgrading models. Experiment emphasizes that experiments are now cheap, but to test variables disciplined and optimize the speed of learning.
This document provides an overview of neural networks. It discusses that neural networks are composed of interconnected processing units similar to neurons in the brain. Neural networks can learn patterns from examples through training and are well-suited for problems that are difficult to solve with traditional algorithms. The document outlines common neural network architectures like feedforward and feedback networks. It also discusses neural network learning methods and applications.
Entropy based algorithm for community detection in augmented networksJuan David Cruz-Gómez
The document proposes an entropy-based algorithm to detect communities in augmented social networks. It begins with an introduction that motivates using both the graph structure and node attributes to find communities. It then outlines the clustering algorithm, which first uses modularity optimization on the graph to generate an initial partition, and then performs entropy optimization on the partition using the node attributes. Experimental results on student networks show that using attributes leads to different community configurations than using the graph alone, and that the algorithm runs in linear time and memory usage.
This document provides an overview of artificial neural networks and supervised learning. It discusses how artificial neural networks are modeled after biological neural networks in the brain. The basic building block of both biological and artificial neural networks is the neuron. A single neuron is then described as a simple computing element that takes weighted inputs and compares them to a threshold. The perceptron, one of the earliest and simplest types of artificial neural networks, is then introduced and its learning process via weight adjustments is explained.
This document provides an overview of artificial neural networks. It begins with definitions of artificial neural networks and how they are analogous to biological neural networks. It then discusses the basic structure of artificial neural networks, including different types of networks like feedforward, recurrent, and convolutional networks. Key concepts in artificial neural networks like neurons, weights, forward/backward propagation, and overfitting/underfitting are also explained. The document concludes with limitations of neural networks and references.
The document discusses adaptive channel equalization using neural networks. It provides an overview of neural networks and their application to channel equalization. Specifically, it summarizes various neural network architectures that have been used for equalization, including multilayer perceptrons, functional link artificial neural networks, Chebyshev neural networks, and radial basis function networks. It compares the bit error rate performance of these different neural network equalizers with traditional linear equalizers such as LMS and RLS. Overall, the document finds that neural network equalizers can better handle nonlinear channel distortions compared to linear equalizers and that radial basis function networks provide particularly good performance for channel equalization applications.
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements similar to neurons. The key aspects covered are:
- Neural networks can perform tasks that are difficult for traditional algorithms, such as pattern recognition.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections and can adapt based on training data.
- Training involves presenting examples to the network and adjusting the weighted connections between nodes until the network outputs the desired targets.
- Once trained, a neural network can be used to analyze new input data in a similar way to the brain.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
Artificial neural networks (ANNs) are inspired by biological neural networks and are composed of interconnected processing elements called neurons. ANNs are configured through a learning process to solve problems like pattern recognition or data classification. Early research in the 1940s and 1950s laid the foundations, like McCulloch and Pitts developing the first neural network model and Hebb developing the first learning rule. ANNs use weighted connections and activation functions to learn from examples through training. Feedforward and feedback networks differ in whether signals travel in one or both directions between layers of neurons. Perceptrons were influential early neural network models that could perform tasks linear programs could not.
Neural networks are mathematical models inspired by biological neural networks. They are useful for pattern recognition and data classification through a learning process of adjusting synaptic connections between neurons. A neural network maps input nodes to output nodes through an arbitrary number of hidden nodes. It is trained by presenting examples to adjust weights using methods like backpropagation to minimize error between actual and predicted outputs. Neural networks have advantages like noise tolerance and not requiring assumptions about data distributions. They have applications in finance, marketing, and other fields, though designing optimal network topology can be challenging.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
Neural networks of artificial intelligencealldesign
An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs, performs calculations, and outputs a value. ANNs can be trained to learn patterns from data through examples to perform tasks like classification, prediction, clustering, and association. Common ANN architectures include multilayer perceptrons, convolutional neural networks, and recurrent neural networks.
Artificial Neural Networks ppt.pptx for final sem cseNaveenBhajantri1
This document provides an overview of artificial neural networks. It discusses the biological inspiration from neurons in the brain and how artificial neural networks mimic this structure. The key components of artificial neurons and various network architectures are described, including fully connected, layered, feedforward, and modular networks. Supervised and unsupervised learning approaches are covered, with backpropagation highlighted as a commonly used supervised algorithm. Applications of neural networks are mentioned in areas like medicine, business, marketing and credit evaluation. Advantages include the ability to handle complex nonlinear problems and noisy data.
Similar to Graded Patterns in Attractor Networks (13)
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
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* What is Vector Search?
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* Live demos with code snippets
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#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Monitoring Java Application Security with JDK Tools and JFR Events
Graded Patterns in Attractor Networks
1. Graded Patterns in Attractor Networks
Tristan Webb Supervisor: Jianfeng Feng Co-Supervisor: Edmund Rolls
Complexity Science DTC, Computational Biology Research Group
University of Warwick
Summary
We demonstrate how noise can exist in a neural network as large as the brain. Graded firing patterns allow us to tune noise levels in the
engineering of neural networks. The levels of noise in the brain may change with age and play a functional role in the retrieval of memory.
Attractor Neural Networks Graded Patterns
Neural coding, and its relationship to behavior, is heavily researched The network was simulated numerically for a time period of four sec-
in many areas of neuroscience. Attractor networks are a demonstra- onds. We present the network two different periods of different exter-
tion of how decisions, memories, and other cognitive representations nal stimulus levels: first a base period, and later a cue period. During
can be encoded in a firing pattern (or set of active neurons) in a neu- the cue period the qualitative firing pattern in the network is sporadic
ral network. and uneven. When cues are applied, the firing rate for the neurons in
An attractor network receives sensory information to the network a winning decision pool is raised to through positive feedback, while
through connections known as synapses. The network is character- the other pool is suppressed through increased inhibition.
ized by recurrent collateral synapses providing feedback to neurons. Uniform Graded
Recurrent synaptic activity will cause the firing patterns in the network Final Second Mean Neuron Rates Final Second Mean Neuron Rates
60 60
to persist even after the input is removed. Winning Pool Winning Pool
Learning occurs through the modification of synaptic strengths (wij , 50 Losing Pool 50 Losing Pool
where i is the ith neuron and j is the jth synapse). An associative 40 40
Firing Rate (Hz)
Firing Rate (Hz)
learning (Hebbian) rule can create the correct structure for the re- 30 30
call of information. This type of learning strengthens connections 20 20
between neurons that are simultaneously active.
10 10
The network dynamics can be thought of as a gradient descent to-
wards a local minimum in an energy landscape. When the network 00 5 10 15 20 25 30 35 40 00 5 10 15 20 25 30 35 40
Neuron Number Neuron Number
has reached this minimum the learned pattern is recalled. The en-
ergy is defined as We imposed uniform and graded firing patterns on the network by
1 External Inputs selecting the distribution of the recurrent weight for each of the deci-
E =− (yi − < y >)(yj − < y >) sion pools. To achieve a uniform firing pattern, weights were all set
2
ij Recurrent firing
yj
Dendrites to the same value w+ = 2.1. Graded firing patterns were achieved
Recurrent
where yi is the firing of the ith neu- wij collateral
by conforming weights to a discrete exponential-like distribution with
ron, < y > is the population’s mean fir- synapses mean value w+ ≈ 2.1.
ing rate. Fixed points in attractor net- Cell bodies
works can correspond to a spontaneous Output firing
Results
state (where all neurons have a low fir- yi
Graded simulations were more likely to jump to a decision
ing rate), or a persistent state in which a early. This could be caused by decreased stability of the
subset of neurons have a high firing rate. spontaneous state. Changes in reaction time distributions
are statistically significant and the decrease in reaction time
Network Dynamics is robust across different firing rates of the winning pool.
Variability in the system increases when
Neurons in simulations use Integrate-and-Fire (IF) dynamics to de- Reaction Times vs Firing Rates
1100 graded patterns are introduced. Here
scribe the membrane potential of neurons. We chose biologically
1000 we use the Fano factor to compute trial
realistic constants to obtain firing rates that are comparable to ex-
Reaction Time (msec)
900 to trial variability of membrane potentials
perimental measurements of neural activity. IF neurons integrate 800 across simulations. The Fano factor is
synaptic current into a membrane potential, and then fire when the 700 calculated from the variance in the poten-
membrane potential reaches a voltage threshold. 600 Graded Simulations
Uniform Simulations
tial measured in a window with temporal
The synaptic current flowing into each neuron is described in terms 500
26 27 28 29 30 31 32 33 34
Winning Pool Final Second Firing Rate (Hz) length T and expressed as a function of
of neurotransmitter components. The four families of receptors used
time,
are GABA, NDMA, AMPArec , and AMPAext . The neurotransmitter re- Average Fano Factor of Membrane Potential
0.005 Tr
leased from a presynaptic excitatory neuron are AMPArec and NMDA, [Vi,n (T ) − Vi (T ) ]2
while inhibitory neurons transmit GABA currents. Each neuron re- 0.004
F (T ) = n ,
ceives external input through a spike train modeled by a Poisson pro- 0.003
Fano Factor
Vi (T )
cess with rate λi = 3.0Hz. 0.002
where Vi (T ) is the average potential of
Synaptic current flowing into a neuron is given by the following equa- 0.001 Graded Simulations neuron i in the time window, and Tr is the
tion, where each term on the RHS refers to the current from one class Uniform Simulations
0.000 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 number of trials.
of neurotransmitter, Time (seconds)
Isyn (t) = IGABA(t) + INDMA(t) + IAMPA,rec (t) + IAMPA,ext (t) Conclusion
The transition time to an attractor state, or reaction time, is
Architecture decreased when neurons fire in a more biologically realistic
We structure the network by establishing the strength of interactions pattern.
between two decision pools, D1 & D2, to be values that could occur There is greater variability in the system’s states over time when
through associative learning. graded patterns are introduced.
We state that increased variance in synaptic input to each neuron can
Non-Specific 1 be thought of as increased noise in the system. Conceptually, graded
Inhibitory Excitatory
Neurons Neurons
Blowup showing sub-populations
of exictatory neurons
patterns are more noisy because recurrent synaptic input to neurons
w+
D1
w−
D2
w+
will vary across the population.
As neural networks become larger, noise will invariably become
lower. However, when we consider the situation in brain, even though
Neurons in the same decision pool are connected to each other with the network is large, there is still significant noise in the system. We
an strong average weight w+, and are connected to the other excita- present the hypothesis that this noise is due in part to graded firing
tory pools with an weak average weight w−. pattens. Further work will explore this analytically.
Complexity DTC - University of Warwick - Coventry, UK Mail: tristan.webb@warwick.ac.uk WWW: http://warwick.ac.uk/go/tristanwebb