Our update for the beginning of 2014, about self-directed evolution from the constraint of biology to a substrate-independent mind (SIM) and personality, a process alluded to in science fiction with the oft-confusing term "uploading". In this talk, I present the most realistic development route to SIM via whole brain emulation (WBE), neural prostheses and neural interfaces. I describe how I contribute to make this happen, as effectively as I can, through my work as it is presented at carboncopies.org. Then, I draw your attention to the most significant development in the field at this moment, an opportunity for a widely applicable Platform for high resolution neural interfaces. That platform has the potential in the near-term to provide the access needed for true brain machine interfaces, cognitive neural prostheses and the type of data acquisition that is essential for whole brain emulation.
1) A wearable brain cap is presented that can measure EEG signals without requiring electrical contact with the head using integrated contactless electrodes.
2) The cap is made of flexible polymeric material and the contactless electrodes may be obtained using a new electroactive gel that can read the EEG signals.
3) This cap aims to overcome the discomfort of typical EEG caps and electrodes that require electrolytic gel and time-consuming attachment by providing a fully wearable and portable system for brain-computer interface applications.
Computer vision aims to make sense of the vast amounts of visual data on the internet. It has applications for autonomous vehicles to interpret images of the road. The human visual system has over 100 billion neurons and 1000 trillion connections that allow us to perceive the world. Computer vision systems draw inspiration from the human visual cortex, with convolutional neural networks that mimic the visual hierarchy in the brain. While systems have improved at tasks like image classification, computers still lack the human ability to understand context and assign meaning based on surroundings.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
The document discusses Loihi, a neuromorphic manycore processor chip that implements spiking neural networks (SNN) in hardware. Some key features of Loihi include programmable synaptic learning rules, hierarchical connectivity, and dendritic compartments. Loihi supports SNN learning through localized learning rules and additional variables per synapse. The learning engine in Loihi implements spike-timing dependent plasticity using a pairwise approach without the need for reverse lookup tables between cores.
Neuromorphic circuits are typically used to emulate cortical structures and to explore principles of computation of the brain. But they can also be used to implement convolutional and deep networks. Here we demonstrate a proof of concept, using our latest multi-core and on-line learning reconfigurable spiking neural network chips.
Our update for the beginning of 2014, about self-directed evolution from the constraint of biology to a substrate-independent mind (SIM) and personality, a process alluded to in science fiction with the oft-confusing term "uploading". In this talk, I present the most realistic development route to SIM via whole brain emulation (WBE), neural prostheses and neural interfaces. I describe how I contribute to make this happen, as effectively as I can, through my work as it is presented at carboncopies.org. Then, I draw your attention to the most significant development in the field at this moment, an opportunity for a widely applicable Platform for high resolution neural interfaces. That platform has the potential in the near-term to provide the access needed for true brain machine interfaces, cognitive neural prostheses and the type of data acquisition that is essential for whole brain emulation.
1) A wearable brain cap is presented that can measure EEG signals without requiring electrical contact with the head using integrated contactless electrodes.
2) The cap is made of flexible polymeric material and the contactless electrodes may be obtained using a new electroactive gel that can read the EEG signals.
3) This cap aims to overcome the discomfort of typical EEG caps and electrodes that require electrolytic gel and time-consuming attachment by providing a fully wearable and portable system for brain-computer interface applications.
Computer vision aims to make sense of the vast amounts of visual data on the internet. It has applications for autonomous vehicles to interpret images of the road. The human visual system has over 100 billion neurons and 1000 trillion connections that allow us to perceive the world. Computer vision systems draw inspiration from the human visual cortex, with convolutional neural networks that mimic the visual hierarchy in the brain. While systems have improved at tasks like image classification, computers still lack the human ability to understand context and assign meaning based on surroundings.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
The document discusses Loihi, a neuromorphic manycore processor chip that implements spiking neural networks (SNN) in hardware. Some key features of Loihi include programmable synaptic learning rules, hierarchical connectivity, and dendritic compartments. Loihi supports SNN learning through localized learning rules and additional variables per synapse. The learning engine in Loihi implements spike-timing dependent plasticity using a pairwise approach without the need for reverse lookup tables between cores.
Neuromorphic circuits are typically used to emulate cortical structures and to explore principles of computation of the brain. But they can also be used to implement convolutional and deep networks. Here we demonstrate a proof of concept, using our latest multi-core and on-line learning reconfigurable spiking neural network chips.
Conversion of Artificial Neural Networks (ANN) To Autonomous Neural NetworksIJMER
This document discusses ways to improve artificial neural networks (ANNs) to make them more autonomous like the human brain. It notes that current ANNs require human intervention for tasks like setting learning parameters and rates. The document proposes giving ANNs memory, the ability to prioritize and select tasks, set processing targets, address any problem, and adjust synaptic weights without human intervention. This would allow ANNs to function autonomously like the human brain, which learns on its own from experiences stored in memory to make independent decisions.
The document discusses fundamentals of neural networks and artificial intelligence. It provides an overview of topics covered in lectures 37 and 38, including the biological neuron model, artificial neuron model, neural network architectures, learning methods in neural networks, single-layer neural network systems, and applications of neural networks. It also includes details on the McCulloch-Pitts neuron model and the basic elements of an artificial neuron, such as weights, thresholds, and activation functions.
The Human Brain Project aims to build advanced informatics and modeling technologies to simulate and understand the human brain through establishing multidisciplinary programs and facilities for gathering and analyzing brain data, developing exascale supercomputing capabilities, deriving novel technologies, and addressing related ethical issues. The goal is to gain insights into brain function and diseases, develop new clinical tools, and create a new generation of intelligent technologies by gaining a deeper understanding of the brain's organizing principles through highly detailed brain simulations and models.
A brain-computer interface (BCI), sometimes called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain-machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[1][2] The papers published after this research also mark the first appearance of the expression brain-computer interface in scientific literature.The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[3] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
(1) Consensus learning aims to improve problem-solving by combining the knowledge and predictions of multiple machine learning models or agents.
(2) It is motivated by distributed artificial intelligence, where multi-agent systems need to learn and adapt to complex environments.
(3) The consensus approach aggregates the opinions of different models/agents to reach a general agreement, with the goal of producing better and more robust predictions than any single model.
With the introduction of Blue Brain technology, which is a reverse engineering, we can overcome all the brain disorders and diseases. Blue Brain is the name of the world’s first virtual brain which makes a machine, function as a human brain. Even after the death of the person the complete functional attribute of a human brain can be stored in that and can be used for further development.
This document summarizes a biologically-inspired active vision system that uses a neural network and genetic algorithm to evolve object recognition abilities. The system mimics biological vision by taking in small parts of a scene at a time and moving between parts rapidly. Prior work on smaller systems is expanded through a scalable GPU implementation. Preliminary experiments evolved controllers that could recognize five objects within 20 time steps despite variations in illumination, rotation, position and scale.
Vicarious Systems at Singularity Summit 2011Scott Brown
A makes more sense than B. Using a shirt to dry one's feet after getting them wet is a common and sensible thing to do, while using glasses to dry one's feet does not really make logical sense and would not be an effective way to dry one's feet.
The document discusses the concept of "digital immortality" (DI) which aims to reconstruct a person's identity based on indirect data captured about them. It proposes that by analyzing a person's input data (what they sensed) and output data (their reactions), one could theoretically simulate their brain and restore their structure and functioning. However, current technology is not advanced enough to do full identity reconstruction from indirect data alone. The document suggests focusing on developing tools to directly capture data about individuals through sensors, with the goal of applying that data to future DI efforts once algorithms and computing power are more advanced.
A framework for approaches to transfer of mind substrateKarlos Svoboda
This document outlines a framework for discussing approaches to transferring a mind's substrate. It summarizes recent developments in neural prosthesis that could allow functional replacement of brain parts, potentially leading to a form of "mind-substrate transfer." It reviews two main proposed approaches to mind-substrate transfer: 1) Reconstruction from a brain scan, which would involve scanning the brain at high resolution and simulating its functioning. 2) Reconstruction from behavior, which would involve collecting behavioral information about an individual to parametrize a generic substrate. It argues that an underlying question is what constitutes a person's identity and whether identity could be transferred between original and synthetic substrates.
The Blue Brain project aims to create a virtual brain by digitally simulating the neocortical column through detailed modeling of neurons, connections, and circuitry. This will help researchers understand brain function and potentially lead to treatments for brain disorders by providing insights into neural coding and information processing. If successful, the long term goal is to upload the human brain into a computer system using nanobots to allow continued existence after death of the physical body.
Introduction to Spiking Neural Networks: From a Computational Neuroscience pe...Jason Tsai
The document provides an introduction to spiking neural networks (SNNs) and neuromorphic computing. It discusses the characteristics and advantages of SNNs, including their spatio-temporal nature, asynchronous processing, sparsity, and energy efficiency. It also covers basic neuroscience concepts like neurons, action potentials, synaptic plasticity, and learning rules like STDP. Common SNN models and neural encoding schemes are described. Examples of SNN applications in visual processing and pattern generation are presented. Finally, neuromorphic hardware platforms like Intel's Loihi chip are introduced.
The document discusses the Blue Brain Project, which aims to create a virtual brain through detailed computer simulations. It is attempting to simulate the human brain by replicating it at the molecular level in a supercomputer. The project's objectives are to understand how human memory and thinking work, with the hopes of gaining insights that could help cure diseases like Parkinson's. The project requires powerful supercomputing hardware and complex neural simulation software to model the brain's neurons and synapses at a high level of detail. If successful, the Blue Brain could allow intelligence and memories to be preserved after death and help create new technologies like prosthetics.
This document outlines the course details for an "Intelligent Systems" course including 16 lectures and 8 practical works covering topics such as knowledge representation methods, expert systems, machine learning, natural language processing, intelligent robots, and the future of artificial intelligence. The course is taught by Professor Dr. Andrey V. Gavrilov and will provide students with basic concepts of different intelligent systems development methods and tools. Grades will be based on a midterm exam worth 50% and a final exam worth 50% of the total grade.
The document discusses the structure and function of the brain as a complex network. It notes that the brain exhibits both segregation and integration at multiple scales from neurons to regions. The structural connectivity of the brain forms a small-world network that allows for both specialized processing within clusters and integrated processing between regions via short path lengths. Computational models can capture large-scale brain activity and dynamics based on the underlying structural connectivity.
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs can learn complex patterns and make predictions based on large amounts of data. The document discusses the basic structure and functioning of ANNs, including their ability to learn through adjustment of synaptic weights between neurons. It also describes several common types of ANNs, focusing on perceptrons and multi-layer perceptrons.
This document summarizes the ICOM project which researched computational intelligence, its principles, and applications. The project developed and implemented neural, symbolic, and hybrid systems including theory refinement systems, ANN compilers, genetic algorithms, and applications in various domains. Key developments included the CIL2P system which combines logic programming and neural networks, and rule extraction methods to explain neural network decisions. The combinatorial neural model was also investigated as a way to integrate neural and symbolic processing for classification tasks.
The document discusses a proposed brain simulator. It aims to provide information on how Alzheimer's disease affects brain structure and function. The simulator is based on models of brain network development, aging, and damage. It will integrate data from tools like fMRI, EEG, and psychometrics. The simulator works by modeling features of the brain including self-organization, degeneracy, and small-world topology. It also models disease progression over time using stages of Alzheimer's and damage models. The talk outlines previous research on analyzing brain networks and disconnection syndromes in relation to the proposed simulator.
Conversion of Artificial Neural Networks (ANN) To Autonomous Neural NetworksIJMER
This document discusses ways to improve artificial neural networks (ANNs) to make them more autonomous like the human brain. It notes that current ANNs require human intervention for tasks like setting learning parameters and rates. The document proposes giving ANNs memory, the ability to prioritize and select tasks, set processing targets, address any problem, and adjust synaptic weights without human intervention. This would allow ANNs to function autonomously like the human brain, which learns on its own from experiences stored in memory to make independent decisions.
The document discusses fundamentals of neural networks and artificial intelligence. It provides an overview of topics covered in lectures 37 and 38, including the biological neuron model, artificial neuron model, neural network architectures, learning methods in neural networks, single-layer neural network systems, and applications of neural networks. It also includes details on the McCulloch-Pitts neuron model and the basic elements of an artificial neuron, such as weights, thresholds, and activation functions.
The Human Brain Project aims to build advanced informatics and modeling technologies to simulate and understand the human brain through establishing multidisciplinary programs and facilities for gathering and analyzing brain data, developing exascale supercomputing capabilities, deriving novel technologies, and addressing related ethical issues. The goal is to gain insights into brain function and diseases, develop new clinical tools, and create a new generation of intelligent technologies by gaining a deeper understanding of the brain's organizing principles through highly detailed brain simulations and models.
A brain-computer interface (BCI), sometimes called a mind-machine interface (MMI), or sometimes called a direct neural interface (DNI), synthetic telepathy interface (STI) or a brain-machine interface (BMI), is a direct communication pathway between the brain and an external device. BCIs are often directed at assisting, augmenting, or repairing human cognitive or sensory-motor functions.Research on BCIs began in the 1970s at the University of California Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA.[1][2] The papers published after this research also mark the first appearance of the expression brain-computer interface in scientific literature.The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels.[3] Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s.
(1) Consensus learning aims to improve problem-solving by combining the knowledge and predictions of multiple machine learning models or agents.
(2) It is motivated by distributed artificial intelligence, where multi-agent systems need to learn and adapt to complex environments.
(3) The consensus approach aggregates the opinions of different models/agents to reach a general agreement, with the goal of producing better and more robust predictions than any single model.
With the introduction of Blue Brain technology, which is a reverse engineering, we can overcome all the brain disorders and diseases. Blue Brain is the name of the world’s first virtual brain which makes a machine, function as a human brain. Even after the death of the person the complete functional attribute of a human brain can be stored in that and can be used for further development.
This document summarizes a biologically-inspired active vision system that uses a neural network and genetic algorithm to evolve object recognition abilities. The system mimics biological vision by taking in small parts of a scene at a time and moving between parts rapidly. Prior work on smaller systems is expanded through a scalable GPU implementation. Preliminary experiments evolved controllers that could recognize five objects within 20 time steps despite variations in illumination, rotation, position and scale.
Vicarious Systems at Singularity Summit 2011Scott Brown
A makes more sense than B. Using a shirt to dry one's feet after getting them wet is a common and sensible thing to do, while using glasses to dry one's feet does not really make logical sense and would not be an effective way to dry one's feet.
The document discusses the concept of "digital immortality" (DI) which aims to reconstruct a person's identity based on indirect data captured about them. It proposes that by analyzing a person's input data (what they sensed) and output data (their reactions), one could theoretically simulate their brain and restore their structure and functioning. However, current technology is not advanced enough to do full identity reconstruction from indirect data alone. The document suggests focusing on developing tools to directly capture data about individuals through sensors, with the goal of applying that data to future DI efforts once algorithms and computing power are more advanced.
A framework for approaches to transfer of mind substrateKarlos Svoboda
This document outlines a framework for discussing approaches to transferring a mind's substrate. It summarizes recent developments in neural prosthesis that could allow functional replacement of brain parts, potentially leading to a form of "mind-substrate transfer." It reviews two main proposed approaches to mind-substrate transfer: 1) Reconstruction from a brain scan, which would involve scanning the brain at high resolution and simulating its functioning. 2) Reconstruction from behavior, which would involve collecting behavioral information about an individual to parametrize a generic substrate. It argues that an underlying question is what constitutes a person's identity and whether identity could be transferred between original and synthetic substrates.
The Blue Brain project aims to create a virtual brain by digitally simulating the neocortical column through detailed modeling of neurons, connections, and circuitry. This will help researchers understand brain function and potentially lead to treatments for brain disorders by providing insights into neural coding and information processing. If successful, the long term goal is to upload the human brain into a computer system using nanobots to allow continued existence after death of the physical body.
Introduction to Spiking Neural Networks: From a Computational Neuroscience pe...Jason Tsai
The document provides an introduction to spiking neural networks (SNNs) and neuromorphic computing. It discusses the characteristics and advantages of SNNs, including their spatio-temporal nature, asynchronous processing, sparsity, and energy efficiency. It also covers basic neuroscience concepts like neurons, action potentials, synaptic plasticity, and learning rules like STDP. Common SNN models and neural encoding schemes are described. Examples of SNN applications in visual processing and pattern generation are presented. Finally, neuromorphic hardware platforms like Intel's Loihi chip are introduced.
The document discusses the Blue Brain Project, which aims to create a virtual brain through detailed computer simulations. It is attempting to simulate the human brain by replicating it at the molecular level in a supercomputer. The project's objectives are to understand how human memory and thinking work, with the hopes of gaining insights that could help cure diseases like Parkinson's. The project requires powerful supercomputing hardware and complex neural simulation software to model the brain's neurons and synapses at a high level of detail. If successful, the Blue Brain could allow intelligence and memories to be preserved after death and help create new technologies like prosthetics.
This document outlines the course details for an "Intelligent Systems" course including 16 lectures and 8 practical works covering topics such as knowledge representation methods, expert systems, machine learning, natural language processing, intelligent robots, and the future of artificial intelligence. The course is taught by Professor Dr. Andrey V. Gavrilov and will provide students with basic concepts of different intelligent systems development methods and tools. Grades will be based on a midterm exam worth 50% and a final exam worth 50% of the total grade.
The document discusses the structure and function of the brain as a complex network. It notes that the brain exhibits both segregation and integration at multiple scales from neurons to regions. The structural connectivity of the brain forms a small-world network that allows for both specialized processing within clusters and integrated processing between regions via short path lengths. Computational models can capture large-scale brain activity and dynamics based on the underlying structural connectivity.
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs can learn complex patterns and make predictions based on large amounts of data. The document discusses the basic structure and functioning of ANNs, including their ability to learn through adjustment of synaptic weights between neurons. It also describes several common types of ANNs, focusing on perceptrons and multi-layer perceptrons.
This document summarizes the ICOM project which researched computational intelligence, its principles, and applications. The project developed and implemented neural, symbolic, and hybrid systems including theory refinement systems, ANN compilers, genetic algorithms, and applications in various domains. Key developments included the CIL2P system which combines logic programming and neural networks, and rule extraction methods to explain neural network decisions. The combinatorial neural model was also investigated as a way to integrate neural and symbolic processing for classification tasks.
The document discusses a proposed brain simulator. It aims to provide information on how Alzheimer's disease affects brain structure and function. The simulator is based on models of brain network development, aging, and damage. It will integrate data from tools like fMRI, EEG, and psychometrics. The simulator works by modeling features of the brain including self-organization, degeneracy, and small-world topology. It also models disease progression over time using stages of Alzheimer's and damage models. The talk outlines previous research on analyzing brain networks and disconnection syndromes in relation to the proposed simulator.
ANALYSIS ON MACHINE CELL RECOGNITION AND DETACHING FROM NEURAL SYSTEMSIAEME Publication
This document summarizes an analysis on machine cell recognition and detaching from neural systems. It discusses using artificial neural networks (ANNs) like the backpropagation network, self-organizing map network, and ART1 network to identify machine cells and facilitate cellular manufacturing. The document provides background on neural networks and cellular manufacturing. It discusses using unsupervised ANN methods like FLANN (fast learning artificial neural network) to cluster machines and optimize production. The goal is to minimize the number of parts produced in each cell to improve efficiency.
Ethical issues involved in hybrid bionic systemsKarlos Svoboda
1. The document summarizes a workshop on robo-ethics that discussed two case studies: the CYBERHAND project developing a prosthetic hand connected to the nervous system, and the NEUROBOTICS project investigating hybrid bionic systems.
2. The CYBERHAND project aims to develop a prosthetic hand that provides natural sensory feedback through stimulation of afferent nerves and is controlled naturally by processing efferent neural signals.
3. The CYBERHAND system includes a biomechatronic hand, biomimetic sensors, regeneration electrodes for connecting to nerves, an implantable system for neural stimulation and recording, and an external unit for decoding intentions and controlling the prosthesis.
An Overview On Neural Network And Its ApplicationSherri Cost
Neural networks are computational models that can learn from large amounts of data to find patterns and make predictions. They are inspired by biological neural networks in the brain. The document provides an overview of how artificial neural networks function by organizing layers of nodes that are trained to process input data. It also discusses applications of neural networks such as classification, prediction, clustering, and associating patterns. Neural networks are well-suited for analyzing big data due to their ability to handle ambiguous or incomplete information.
This document summarizes a presentation on exploring complex networks in the brain. It discusses defining the human connectome at multiple scales from neurons to brain regions. It outlines steps to map the structural and functional connectivity of the brain. It also describes using network measures and models to analyze topological properties of brain networks and detecting community structure. Detecting changes in network measures may help understand diseases.
The document outlines the design of a Brain Simulator. It will include multiple layers that simulate different aspects and functions of the brain at various levels of processing and time scales. The development of the simulator will be an ongoing, incremental process, with each increment adding new functionality and being validated before integration. Requirements may include simulating conditions like Alzheimer's disease or sleep and validating models through data like images or EEG readings. The simulator will use a sparse matrix to optimize memory usage and allow for an overlay of systems like the limbic system.
Artificial Neural Network: A brief studyIRJET Journal
This document provides an overview of artificial neural networks (ANN), including:
- ANNs are computational models inspired by the human brain that are designed to analyze and draw conclusions from experiences. They contain interconnected nodes that work together to solve problems.
- The key components of an ANN include an input layer, one or more hidden layers, and an output layer. Data is fed into the input layer and passes through the hidden layers before emerging as output.
- ANNs can be trained to learn from large datasets using supervised, unsupervised, or reinforcement learning techniques. The weights between nodes are adjusted during training to minimize error between the network's predictions and correct outputs.
- Once trained, ANNs can
From Social Networks to Artificial Neural Networks. How NeuroMorphic Computation will Solve the big problems of Big Data and the Internet of Things, in the age of PostProgrraming
1. A brain-computer interface (BCI) allows direct communication between the brain and external devices, helping people with motor impairments and providing new functionality.
2. BCI can be invasive, using implants in the brain to detect high-quality signals, but these are prone to scar tissue buildup. Non-invasive BCIs use neuroimaging techniques but produce poorer signals.
3. Experiments have used EEG to detect brainwaves and allow people to type or control devices through thought. As detection techniques improve, BCI could provide more alternatives for people to interact with their environment.
The document discusses brain-computer interfaces (BCIs), which allow humans to control external devices using brain signals alone. It provides an overview of BCIs, including their definition, components, training processes, and applications. BCIs could potentially help paralyzed individuals control devices, but face challenges from weak brain signals, complex neural connections, and lack of portability. Future improvements may enable more minimally invasive surgery, improved prosthetics, and calibration with fewer trials.
The National Resource for Network Biology aims to provide freely available, open-source software tools to enable researchers to assemble biological data into networks and pathways and use these networks to better understand biological systems and disease; it pursues this mission through technology research and development projects, driving biological projects, collaboration and service projects, training, and dissemination; key components include the Cytoscape software platform, supercomputing infrastructure, and partnerships with over 30 external research groups.
Analytical Review on the Correlation between Ai and NeuroscienceIOSR Journals
This document discusses the relationship between artificial intelligence and neuroscience. It describes how AI has benefited from studying neuroscience to better understand natural intelligence. Specifically, AI has used insights from neuroscience related to learning, perception, and reasoning by modeling neural mechanisms. The document also provides several examples of how AI and robotics have been influenced by neuroscience, including early robots designed to mimic animal behavior and more recent projects that apply insights about the brain to develop artificial neural networks or brain-inspired devices.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document provides an overview and summary of a student project report on simulating a feed forward artificial neural network in C++. The report includes an abstract, table of contents, list of figures, and 5 chapters that discuss the objectives of the project, provide background on artificial neural networks, describe the design and implementation of a 3-layer feed forward neural network using backpropagation, present the results, and provide references. The design section explains the backpropagation algorithm and provides pseudocode for calculating outputs at each layer. The implementation section provides pseudocode for training patterns and minimizing error.
This document is a technical seminar report submitted by N. Shyam Kumar to the Department of Electronics and Communication Engineering at SVS Institute of Technology. The report discusses brain-computer interfaces, including their working architecture and types. It covers invasive BCIs implanted in the brain, partially invasive BCIs implanted in the skull, and non-invasive BCIs using EEG. It also discusses early animal research with BCIs implanted in monkeys and rats.
This document is a preface to a book about neural networks. It provides an overview of the book's contents and objectives. The book aims to present a variety of standard neural network architectures along with their training algorithms and examples of applications. It is intended as both a textbook and reference for students and researchers interested in using neural networks. The preface outlines the scope and organization of the material covered in the book.
Neural networks are inspired by biological neural networks and are composed of interconnected processing elements called neurons. Neural networks can learn complex patterns and relationships through a learning process without being explicitly programmed. They are widely used for applications like pattern recognition, classification, forecasting and more. The document discusses neural network concepts like architecture, learning methods, activation functions and applications. It provides examples of biological and artificial neurons and compares their characteristics.
Similar to On the Development of a Brain Simulator (20)
All the troubles you get into when setting up a production ready Kubernetes c...Jimmy Lu
Have you ever try to set up a Kubernetes cluster manually by your own? It may be a small dish to you to set one up on your laptop. However, things are getting harder and harder once you have more nodes to handle, not to mention you also want security, monitoring, auto-scaling, and federated cluster enabled in the production environments. With more features added, the situation gets even worse and more complicated. We developers in Linker Networks had put in a tremendous amount of time in investigating on how to set up Kubernetes clusters efficiently. We designed and built our own tools to automate and facilitate such the painful processes. In this talk, I'll go through all the details and pitfalls in setting up a production ready cluster. Hopefully, the experience I shared could keep you out of these troubles, saving your precious time.
A Million ways of Deploying a Kubernetes ClusterJimmy Lu
Developers and operators tend to build and develop different ways to set up a Kubernetes cluster due to its complexity and openness. Most of the time, it's quite confusing for the newcomers to get started with the Kubernetes. In this short talk, I'll introduce you some popular ways of Kubernetes deployment and briefly talk about pros and cons of each solution.
Renaissance of JUnit - Introduction to JUnit 5Jimmy Lu
The document introduces JUnit 5, which was rewritten to address limitations in JUnit 4. JUnit 5 includes JUnit Jupiter for writing tests, JUnit Vintage for running JUnit 3/4 tests, and a unified platform. It provides key features like lambda syntax for assertions, dependency injection, dynamic and nested tests, and an extension model. The platform defines APIs for test discovery, execution and reporting that are used by IDEs and build tools to launch testing frameworks in a modular way.
This document provides an overview of Spring Boot. It begins with a brief introduction to Spring Boot, including that it takes an opinionated approach to building production-ready Spring applications quickly. It then discusses features of Spring Boot like providing starter POMs, auto-configuration, and production-ready features out of the box. The document also covers getting started, including a simple example application, and how to customize and extend Spring Boot for microservices development.
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...Jimmy Lu
Speaker: Jimmy Lu
Topics: A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheimer’s Disease
Date: 2011.05.31
Defense of WECO Lab at CSIE, FJU
This document discusses network models of Alzheimer's disease and the time course of lesions in the neocortex related to aging and Alzheimer's. It outlines small-world networks and functional connectivity research on Alzheimer's patients. It also summarizes a study on the progression of lesions in the neocortex over time for aging individuals and those with Alzheimer's based on autopsy findings. References for the small-world networks research and neocortex lesions time course study are provided.
The document outlines a research proposal to develop a brain simulator to model Alzheimer's disease. It will define brain components using object-oriented concepts and connections between components based on neuroanatomy references. A network dynamics approach will be used to model how acetylcholine affects brain structure in Alzheimer's patients. The milestones include defining components and connections by February, then modifying and verifying the model until April when thesis writing begins. The expected results are a simple brain structural simulator and a model describing how acetylcholine affects Alzheimer's patients.
1. The document discusses recent neurophysiological studies in primates that have revealed neurons in certain brain structures carry signals related to past and future rewards.
2. These signals include reward prediction errors - when actual rewards differ from expected rewards - which may serve as a teaching signal for learning.
3. Other neurons detect and discriminate between different rewards, which could underlie the perception and assessment of individual rewards.
4. Neurons also respond to cues that predict future rewards and adapt their activity based on ongoing experience to estimate future rewards.
1. The document examines the relationship between brain anatomical networks and intelligence by analyzing structural, functional, and effective connectivity patterns.
2. It reviews concepts from graph theory and complex networks that are relevant for studying brain networks, including small-world networks and scale-free networks.
3. An experiment analyzed diffusion tensor images and other data from 79 subjects to construct and analyze anatomical brain networks and investigate their relationships with general and high intelligence.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Climate Impact of Software Testing at Nordic Testing Days
On the Development of a Brain Simulator
1. On the Development of
a Brain Simulator
Speaker : Jimmy Lu
Advisor : Hsing Mei
Web Computing Laboratory(WECO Lab)
Computer Science and Information Engineering Department
Fu Jen Catholic University
2. Outline
Reviews of the braininformatics
Current status of brain simulators
The approaches and architecture of the
proposed brain simulator
Modular models of brain function
Next steps and the focus
WECO Lab, CSIE dept., FJU
2010/8/25 2
http://www.weco.net
3. Reviews of The Brain Informatics
WECO Lab, CSIE dept., FJU
2010/8/25 3
http://www.weco.net
4. Web Infrastructure
Overlay Model
Layered Protocol
Deep Web Intelligence
Deep Web
Intelligence
Behavioral : Learning, Education
Cognitive : Psychology
Cognitive Neuroscience
Science (Brain)
Macro : Brain
Micro : Neuron
BrainInformatics
The relationship between the braininformatics and other research fields
WECO Lab, CSIE dept., FJU
2010/8/25 4
http://www.weco.net
7. Internet Human Brain
Scale Billions of unit elements 1011 unit elements
Anatomical structure,
Layered structure OSI model network overlay, and
functional outputs
Error correction, Degeneracy
Mechanisms of fault
recomputation of routing mechanism, replaceable
tolerance
pathway functional area
Motif, communities,
Properties of Motif, communities, hubs,
hubs, shortest path way,
complex networks shortest path way, etc.
etc.
Capability of an unit
Versatile Specific
element
Global functions Not shown diverse
Physical structure Stable Dynamic
Internet vs. the human brain
WECO Lab, CSIE dept., FJU
2010/8/25 7
http://www.weco.net
8. Current Status of the Brain Simulator
WECO Lab, CSIE dept., FJU
2010/8/25 8
http://www.weco.net
9. Proposed Brain
IBM’s C2 Blue Brain
Simulator
Neuron-Level Neuron-Level Brain-Level
Perspective
Microscopic Microscopic Macroscopic
Nuclei, Region,
Basic Component Neuron Neuron
Tracts
Communication
Connection Synapse Synapse
Pathway
Protocol Data
Communication Electrical Signal Electrical Signal
Unit
layered layered
Architecture P2P Network
Architecture Architecture
Neocortical
Focus Area Cortex Whole Brain
column
Supercomputer Supercomputer Cloud Computing
Computation
Blue Gene Blue Gene Environment
The comparison among C2, Blue Brain and proposed brain simulator
WECO Lab, CSIE dept., FJU
2010/8/25 9
http://www.weco.net
10. The Approach and Architecture of
Proposed Brain Simulator
WECO Lab, CSIE dept., FJU
2010/8/25 10
http://www.weco.net
12. Short Long
Term Term
Time Scale
Sleep Learning Brain Disease Aging
Resting State Decision Brain Disease Neural Darwin
Making Model Model Selection Application Layer
……
REM
Stage N1
Stage N2 Network Network (Behavior/Disease
Damage Model Development
Stage N3
Model
Layer)
Causal Layer
…… (Overlay Layer)
By case
……… Processing Layer
Thalamocortical Motif Polysynaptic Loops Diffuse Ascending Projection
Brain Connectivity
Layer
13. Modular Models of Brain Function
WECO Lab, CSIE dept., FJU
2010/8/25 13
http://www.weco.net
14. Modular approach
Much of computational neuroscience focuses on
properties of single neurons and small circuits
However, modular approach to modeling is needed
A complex system may be analyzed by being
decomposed into a set of interacting subsystems
because the complexity has been reduced.
If we try to model “everything all at once” we will
understand nothing
WECO Lab, CSIE dept., FJU
2010/8/25 14
http://www.weco.net
15. 3 views of modules for modeling
Modules as brain structures
Corresponds to physical structure of the human brain
Modules as schemas
Combine functionality of actual brain regions in a single
abstract schema
Decompose a schema into finer schema and simulate their
interaction, and some but not all of them will be mapped
onto detailed neural structures
Modules as interface
Designed to help the user interact with the model
WECO Lab, CSIE dept., FJU
2010/8/25 15
http://www.weco.net
16. A basic model of reflex control of saccades
WECO Lab, CSIE dept., FJU
2010/8/25 16
http://www.weco.net
17. Neural simulation language
Neural Simulation Language (NSL) was developed
to support such modular modeling
Object-oriented approach
Run atop C++, Java, or Matlab
http://www.neuralsimulationlanguage.org/
http://nsl.usc.edu/nsl/Homepage.php
WECO Lab, CSIE dept., FJU
2010/8/25 17
http://www.weco.net
18. Interoperable simulator (1/2)
The further issue is "simulator independent"
descriptions of models
The objective is to separate the model from a
particular software implementation which then allows
modelers to use models from other simulators within
their preferred simulator
This work is based in XML and can be further
examined at the following:
Systems Biology Markup Language
(SBML) http://sbml.org/index.psp
WECO Lab, CSIE dept., FJU
2010/8/25 18
http://www.weco.net
19. Interoperable simulator (2/2)
CellML http://cellml.org/
XML for computational neuroscience
(NeuroML) http://www.neuroml.org/
XML for Neuronal Morphology
Data: http://www.morphml.org/ and http://www.morphml.o
rg:8080/NeuroMLValidator/
NeuroML Interfaces for GENESIS 3: http://www.genesis-
sim.org/GENESIS/G3
ChannelDB: http://www.genesis-
sim.org/hbp/channeldb/ChannelDB.html
WECO Lab, CSIE dept., FJU
2010/8/25 19
http://www.weco.net
20. Next Steps and the Focus
WECO Lab, CSIE dept., FJU
2010/8/25 20
http://www.weco.net
21. Next Steps and the Focus
A comprehensive study of the neuroanatomy,
Neurophysiology, and cognitive psychology in order
to design and construct the components and
mechanisms of the brain simulator
Try to provide a extendable brain simulator
framework, not just a workable application (needs to
be well-architected and -documented)
Focusing on AD, trying to simulate the dynamicity of
the brain connectivity and the matches between
structural patterns and explicit behavior
WECO Lab, CSIE dept., FJU
2010/8/25 21
http://www.weco.net
22. Reference
[1] Wen-Hsien Tseng, Song-Yun Lu, Hsing Mei, “On the development of a
brain simulator”, 2nd ICCCI (2010).
[2] Michael A Arbib (2007), Scholarpedia,
2(3):1869.doi:10.4249/scholarpedia.1869revision #59679
[3] Thomas M. Morse (2007), Scholarpedia,
2(4):3036.doi:10.4249/scholarpedia.3036revision #39060
WECO Lab, CSIE dept., FJU
2010/8/25 22
http://www.weco.net