The Model of Spatiotemporal Dynamics of Alzheimer’s Disease
•
1 like•391 views
Speaker: Jimmy Lu Topics: The Model of Spatiotemporal Dynamics of Alzheimer's Disease Date: 2011.04.23 WECO Lab, CSIE, FJU
Report
Share
Report
Share
Recommended
RNN basics in deep learning
Recurrent neural networks (RNNs) are a type of neural network that can handle sequential data by saving the output of each layer and feeding it back as input. RNNs were created to address issues with feed-forward neural networks, which cannot handle sequential data, only consider the current input, and cannot memorize previous inputs. RNNs have applications in areas like image captioning, time series prediction, natural language processing, and machine translation.
Ivy Zhu, Research Scientist, Intel at MLconf SEA - 5/01/15
Model-Based Machine Learning for Real-Time Brain Decoding: Neurofeedback derived from real-time functional magnetic resonance imaging (rtfMRI) is promising for both scientific applications, such as uncovering hidden brain networks that respond to stimulus, and clinical applications, such as helping people cope with brain disorders ranging from addiction to autism. One of the greatest challenges in applying machine learning to real time brain “decoding” is that traditional methods fit per-voxel parameters, leading to large computational problems on relatively small datasets. As such, it is easy to over-fit parameters to noise rather than the desired signals. Bayesian model-based hierarchical topographical factor analysis (HTFA) solves this problem by uncovering low-dimensional representations (latent factors) of brain images, fitting parameters for latent factors (rather than voxels) while removing the false assumption that all voxels are independent. In this talk, we’ll discuss the promise of using this and other model-based machine learning to better understand full-brain activity and functional connectivity. And we’ll show how Intel Labs and its partners are combining neuroscience and computer science expertise to further extend such algorithms for real-time brain decoding.
Brain connectivity analysis
Graph analysis of brain networks has become essential for quantifying brain dysfunctions, but requires expertise in properly applying the methodological pipeline from raw brain signals to network analysis and interpretation based on neural phenomena. The pipeline includes defining brain nodes from voxels or sensors, calculating connectivity links functionally or effectively, filtering graphs using thresholds, extracting metrics, and classifying networks using models or machine learning while addressing challenges such as statistical variability.
CVPR 2020 Workshop: Sparsity in the neocortex, and its implications for conti...
Numenta VP Research Subutai Ahmad presents a talk on "Sparsity in the Neocortex and its Implications for Continual Learning" at the virtual CVPR 2020 workshop. In this talk, he discusses how continuous learning systems can benefit from sparsity, active dendrites and other neocortical mechanisms.
Final cnn shruthi gali
This document discusses biological neurons, artificial neurons, and cellular neural networks (CNNs). It provides an overview of CNNs, including their history, architecture, applications, advantages, and future scope. CNNs were proposed to reduce the number of interconnections between neurons in neural networks by only connecting neurons within a local neighborhood. A CNN is an array of dynamical systems with local connections only. Each cell in the CNN interacts with neighboring cells.
A framework for approaches to transfer of mind substrate
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.
Have We Missed Half of What the Neocortex Does? by Jeff Hawkins (12/15/2017)
This was a presentation given on December 15, 2017 at the MIT Center for Brains, Minds + Machines as part of their Brains, Minds and Machines Seminar Series.
You can watch the recording of the presentation after Slide 1.
In this talk, Jeff describes a theory that sensory regions of the neocortex process two inputs. One input is the well-known sensory data arriving via thalamic relay cells. We propose the second input is a representation of allocentric location. The allocentric location represents where the sensed feature is relative to the object being sensed, in an object-centric reference frame. As the sensors move, cortical columns learn complete models of objects by integrating sensory features and location representations over time. Lateral projections allow columns to rapidly reach a consensus of what object is being sensed. We propose that the representation of allocentric location is derived locally, in layer 6 of each column, using the same tiling principles as grid cells in the entorhinal cortex. Because individual cortical columns are able to model complete complex objects, cortical regions are far more powerful than currently believed. The inclusion of allocentric location offers the possibility of rapid progress in understanding the function of numerous aspects of cortical anatomy.
Jeff discusses material from these two papers. Others can be found at https://numenta.com/papers
A Theory of How Columns in the Neocortex Enable Learning the Structure of the World
URL: https://doi.org/10.3389/fncir.2017.00081
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in the Neocortex
URL: https://doi.org/10.3389/fncir.2016.00023
Sparsity In The Neocortex, And Its Implications For Machine Learning
These slides were presented by Numenta VP Research Subutai Ahmad on November 18, 2019 at Yale University.
Recommended
RNN basics in deep learning
Recurrent neural networks (RNNs) are a type of neural network that can handle sequential data by saving the output of each layer and feeding it back as input. RNNs were created to address issues with feed-forward neural networks, which cannot handle sequential data, only consider the current input, and cannot memorize previous inputs. RNNs have applications in areas like image captioning, time series prediction, natural language processing, and machine translation.
Ivy Zhu, Research Scientist, Intel at MLconf SEA - 5/01/15
Model-Based Machine Learning for Real-Time Brain Decoding: Neurofeedback derived from real-time functional magnetic resonance imaging (rtfMRI) is promising for both scientific applications, such as uncovering hidden brain networks that respond to stimulus, and clinical applications, such as helping people cope with brain disorders ranging from addiction to autism. One of the greatest challenges in applying machine learning to real time brain “decoding” is that traditional methods fit per-voxel parameters, leading to large computational problems on relatively small datasets. As such, it is easy to over-fit parameters to noise rather than the desired signals. Bayesian model-based hierarchical topographical factor analysis (HTFA) solves this problem by uncovering low-dimensional representations (latent factors) of brain images, fitting parameters for latent factors (rather than voxels) while removing the false assumption that all voxels are independent. In this talk, we’ll discuss the promise of using this and other model-based machine learning to better understand full-brain activity and functional connectivity. And we’ll show how Intel Labs and its partners are combining neuroscience and computer science expertise to further extend such algorithms for real-time brain decoding.
Brain connectivity analysis
Graph analysis of brain networks has become essential for quantifying brain dysfunctions, but requires expertise in properly applying the methodological pipeline from raw brain signals to network analysis and interpretation based on neural phenomena. The pipeline includes defining brain nodes from voxels or sensors, calculating connectivity links functionally or effectively, filtering graphs using thresholds, extracting metrics, and classifying networks using models or machine learning while addressing challenges such as statistical variability.
CVPR 2020 Workshop: Sparsity in the neocortex, and its implications for conti...
Numenta VP Research Subutai Ahmad presents a talk on "Sparsity in the Neocortex and its Implications for Continual Learning" at the virtual CVPR 2020 workshop. In this talk, he discusses how continuous learning systems can benefit from sparsity, active dendrites and other neocortical mechanisms.
Final cnn shruthi gali
This document discusses biological neurons, artificial neurons, and cellular neural networks (CNNs). It provides an overview of CNNs, including their history, architecture, applications, advantages, and future scope. CNNs were proposed to reduce the number of interconnections between neurons in neural networks by only connecting neurons within a local neighborhood. A CNN is an array of dynamical systems with local connections only. Each cell in the CNN interacts with neighboring cells.
A framework for approaches to transfer of mind substrate
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.
Have We Missed Half of What the Neocortex Does? by Jeff Hawkins (12/15/2017)
This was a presentation given on December 15, 2017 at the MIT Center for Brains, Minds + Machines as part of their Brains, Minds and Machines Seminar Series.
You can watch the recording of the presentation after Slide 1.
In this talk, Jeff describes a theory that sensory regions of the neocortex process two inputs. One input is the well-known sensory data arriving via thalamic relay cells. We propose the second input is a representation of allocentric location. The allocentric location represents where the sensed feature is relative to the object being sensed, in an object-centric reference frame. As the sensors move, cortical columns learn complete models of objects by integrating sensory features and location representations over time. Lateral projections allow columns to rapidly reach a consensus of what object is being sensed. We propose that the representation of allocentric location is derived locally, in layer 6 of each column, using the same tiling principles as grid cells in the entorhinal cortex. Because individual cortical columns are able to model complete complex objects, cortical regions are far more powerful than currently believed. The inclusion of allocentric location offers the possibility of rapid progress in understanding the function of numerous aspects of cortical anatomy.
Jeff discusses material from these two papers. Others can be found at https://numenta.com/papers
A Theory of How Columns in the Neocortex Enable Learning the Structure of the World
URL: https://doi.org/10.3389/fncir.2017.00081
Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in the Neocortex
URL: https://doi.org/10.3389/fncir.2016.00023
Sparsity In The Neocortex, And Its Implications For Machine Learning
These slides were presented by Numenta VP Research Subutai Ahmad on November 18, 2019 at Yale University.
ITAB2010-Thresholding Correlation Matrices
The presentation that accompanied the paper submission to ITAB2010. The paper will become available from IEEE Xplore (http://ieeexplore.ieee.org/Xplore/dynhome.jsp)
Neuroscience Information Framework Ontologies: Nerve cells in Neurolex and NI...
Neuroscience Information Framework Ontologies: Nerve cells in Neurolex and NI...Neuroscience Information Framework
The document discusses neuroscience ontologies created by the Neuroscience Information Framework (NIF). It describes how NIF incorporates existing ontologies and extends them for neuroscience as needed. NIF includes modular ontologies covering multiple scales including molecules, cells, anatomy, and functions. Key ontologies discussed include NIFSTD, Neurolex, and bridging files that link related concepts across ontologies. Examples are provided of how neuron classes are defined based on attributes such as brain region, molecular constituents, and roles.Neural Network Applications In Machining: A Review
This document was accepted for presentation in the proceedings of "International Conference on Advances in Manufacturing Technology '08".
Deep learning and feature extraction for time series forecasting
This document outlines the use of deep learning and feature extraction techniques for time series forecasting. It discusses using artificial neural networks like RNNs on raw time series data and on extracted features. RNNs can be used for anomaly detection and forecasting. The document also discusses modeling quasi-periodic time series using RNNs with LSTM units, extracting features through clustering, and evaluating models on forecast horizons of minutes to segments.
Осадчий А.Е. Анализ многомерных магнито- и электроэнцефалографических данных ...
This document summarizes research on analyzing EEG and MEG data to build dynamic functional brain networks. It discusses Broadmann brain areas and their relation to function, different brain imaging techniques like fMRI and their pros and cons, methods for measuring neuronal synchrony and connectivity, experimental paradigms used with MEG to study transient networks involving word processing, and challenges in fully characterizing complex brain network dynamics from neuroimaging data.
Jeff Hawkins Human Brain Project Summit Keynote: "Location, Location, Locatio...
The document summarizes Jeff Hawkins' presentation on a proposed framework for understanding intelligence and cortical computation. Some key points:
- Hawkins proposes that grid cells exist in the neocortex and represent the location of sensory input relative to objects. Each cortical column learns a complete model of objects, including their location spaces.
- Objects can be composed of other objects via displacement cells, allowing efficient learning of new combinations without relearning parts. Behaviors can also be represented as sequences of displacements.
- This framework provides insights into neuroscience, concepts, limits of intelligence, and has implications for building true artificial intelligence based on distributed object-centric representations.
Location, Location, Location - A Framework for Intelligence and Cortical Comp...
Jeff Hawkins gave this presentation as part of the Johns Hopkins APL Colloquium Series on Septemer 21, 2018.
View the video of the talk here: https://numenta.com/resources/videos/jeff-hawkins-johns-hopkins-apl-talk/
Ferree Resume
Thomas Charles Ferree has over 25 years of experience in signal processing, algorithm development, and neuroscience research. He has a PhD in Physics from the University of Colorado and has held positions at several universities and research institutions. His research has focused on developing algorithms and models for analyzing EEG, EIT, and other biological signal data to study visual attention, stroke detection, and the neurological effects of various stimuli. He has extensive experience developing software and analyzing data across various computing platforms.
Have We Missed Half of What the Neocortex Does? A New Predictive Framework ...
Numenta VP of Research Subutai Ahmad delivered this presentation at the Centre for Theoretical Neuroscience, University of Waterloo on October 2, 2018.
Soft computing
This document discusses neural network training, including supervised and unsupervised training. Supervised training involves providing the network with inputs and desired outputs to compare results and refine weights. Unsupervised training only provides inputs, requiring the network to group data on its own. Backpropagation is a common supervised training technique that adjusts weights to minimize error. Both sufficient data and understanding of the problem are needed to properly train a network.
Could A Model Of Predictive Voting Explain Many Long-Range Connections? by Su...
These are slides on a workshop Subutai Ahmad hosted on March 5, 2018 at the Computational and Systems Neuroscience Meeting (Cosyne) 2018.
About:
This workshop on long-range cortical circuits is focused on our peer-reviewed paper, “A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.” Subutai discussed the inference mechanism introduced in the paper, our theory of location information, and how long-range connections allow columns to integrate inputs over space to perform object recognition.
Locations in the Neocortex: A Theory of Sensorimotor Prediction Using Cortica...
This document summarizes a talk on how cortical grid cells may represent the structure of objects.
[1] Cortical grid cells in the entorhinal cortex and within cortical columns can represent locations within environments and on objects. As an animal or sensor moves, grid cell modules update locations using path integration.
[2] A network model shows how grid cell representations of location can be used to build predictive models of objects by associating locations with sensory features. Simulations demonstrate the network can recognize objects through sequences of movements and sensations.
[3] There is suggestive anatomical and physiological evidence mapping the proposed model to biology, including grid cell signatures in human cortex and sensorimotor prediction in primary sensory areas.
Automated Analysis of Microscopy Images using Deep Convolutional Neural Network
This document summarizes research on using deep convolutional neural networks to automatically analyze microscopy images. The goals are to expedite the analysis of high-content microscopy data and automate tasks like cell counting and classification. The researchers trained and tested models using TensorFlow on microscopy images to classify cells, achieving over 75% accuracy. This level of automation could benefit biological research by reducing human errors and speeding up analysis of large image datasets.
Operating system
This document discusses supervised and unsupervised training of artificial neural networks. In supervised training, both the inputs and desired outputs are provided to the network during training. The network adjusts its weights to match the outputs to the desired outputs. Unsupervised training only provides inputs to the network, and it must group the input data without guidance on desired outputs. Both approaches require sufficient data for training and testing the network. Supervised learning is more common and achieves better results currently.
Reward
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.
The Models of Alzheimer's Disease Part II
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.
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
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
The Models of Alzheimer's Disease Part I
Speaker: Jimmy Lu
Topics: The Models of Alzheimer's Disease Part I
Date: 2011.02.19
WECO Lab, CSIE, FJU
Renaissance of JUnit - Introduction to JUnit 5
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.
Event sourcing with reactor and spring statemachine
An idea to apply reactor, spring statemachine to event sourcing pattern to build event driven applications.
Bootify your spring application
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.
Adolescent Brain Development And Its Effects
The document discusses adolescent brain development and its implications. It notes that the prefrontal cortex, responsible for reasoning and problem solving, develops last. During adolescence, the brain undergoes synaptic pruning and myelination in the frontal lobes. This results in improved abstract thinking abilities but also impaired emotional control and judgment. Teens may engage in risky behavior due to a less developed prefrontal cortex. The document emphasizes the importance of supporting adolescent well-being, competence, confidence, connections, character and sleep for healthy development.
More Related Content
What's hot
ITAB2010-Thresholding Correlation Matrices
The presentation that accompanied the paper submission to ITAB2010. The paper will become available from IEEE Xplore (http://ieeexplore.ieee.org/Xplore/dynhome.jsp)
Neuroscience Information Framework Ontologies: Nerve cells in Neurolex and NI...
Neuroscience Information Framework Ontologies: Nerve cells in Neurolex and NI...Neuroscience Information Framework
The document discusses neuroscience ontologies created by the Neuroscience Information Framework (NIF). It describes how NIF incorporates existing ontologies and extends them for neuroscience as needed. NIF includes modular ontologies covering multiple scales including molecules, cells, anatomy, and functions. Key ontologies discussed include NIFSTD, Neurolex, and bridging files that link related concepts across ontologies. Examples are provided of how neuron classes are defined based on attributes such as brain region, molecular constituents, and roles.Neural Network Applications In Machining: A Review
This document was accepted for presentation in the proceedings of "International Conference on Advances in Manufacturing Technology '08".
Deep learning and feature extraction for time series forecasting
This document outlines the use of deep learning and feature extraction techniques for time series forecasting. It discusses using artificial neural networks like RNNs on raw time series data and on extracted features. RNNs can be used for anomaly detection and forecasting. The document also discusses modeling quasi-periodic time series using RNNs with LSTM units, extracting features through clustering, and evaluating models on forecast horizons of minutes to segments.
Осадчий А.Е. Анализ многомерных магнито- и электроэнцефалографических данных ...
This document summarizes research on analyzing EEG and MEG data to build dynamic functional brain networks. It discusses Broadmann brain areas and their relation to function, different brain imaging techniques like fMRI and their pros and cons, methods for measuring neuronal synchrony and connectivity, experimental paradigms used with MEG to study transient networks involving word processing, and challenges in fully characterizing complex brain network dynamics from neuroimaging data.
Jeff Hawkins Human Brain Project Summit Keynote: "Location, Location, Locatio...
The document summarizes Jeff Hawkins' presentation on a proposed framework for understanding intelligence and cortical computation. Some key points:
- Hawkins proposes that grid cells exist in the neocortex and represent the location of sensory input relative to objects. Each cortical column learns a complete model of objects, including their location spaces.
- Objects can be composed of other objects via displacement cells, allowing efficient learning of new combinations without relearning parts. Behaviors can also be represented as sequences of displacements.
- This framework provides insights into neuroscience, concepts, limits of intelligence, and has implications for building true artificial intelligence based on distributed object-centric representations.
Location, Location, Location - A Framework for Intelligence and Cortical Comp...
Jeff Hawkins gave this presentation as part of the Johns Hopkins APL Colloquium Series on Septemer 21, 2018.
View the video of the talk here: https://numenta.com/resources/videos/jeff-hawkins-johns-hopkins-apl-talk/
Ferree Resume
Thomas Charles Ferree has over 25 years of experience in signal processing, algorithm development, and neuroscience research. He has a PhD in Physics from the University of Colorado and has held positions at several universities and research institutions. His research has focused on developing algorithms and models for analyzing EEG, EIT, and other biological signal data to study visual attention, stroke detection, and the neurological effects of various stimuli. He has extensive experience developing software and analyzing data across various computing platforms.
Have We Missed Half of What the Neocortex Does? A New Predictive Framework ...
Numenta VP of Research Subutai Ahmad delivered this presentation at the Centre for Theoretical Neuroscience, University of Waterloo on October 2, 2018.
Soft computing
This document discusses neural network training, including supervised and unsupervised training. Supervised training involves providing the network with inputs and desired outputs to compare results and refine weights. Unsupervised training only provides inputs, requiring the network to group data on its own. Backpropagation is a common supervised training technique that adjusts weights to minimize error. Both sufficient data and understanding of the problem are needed to properly train a network.
Could A Model Of Predictive Voting Explain Many Long-Range Connections? by Su...
These are slides on a workshop Subutai Ahmad hosted on March 5, 2018 at the Computational and Systems Neuroscience Meeting (Cosyne) 2018.
About:
This workshop on long-range cortical circuits is focused on our peer-reviewed paper, “A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.” Subutai discussed the inference mechanism introduced in the paper, our theory of location information, and how long-range connections allow columns to integrate inputs over space to perform object recognition.
Locations in the Neocortex: A Theory of Sensorimotor Prediction Using Cortica...
This document summarizes a talk on how cortical grid cells may represent the structure of objects.
[1] Cortical grid cells in the entorhinal cortex and within cortical columns can represent locations within environments and on objects. As an animal or sensor moves, grid cell modules update locations using path integration.
[2] A network model shows how grid cell representations of location can be used to build predictive models of objects by associating locations with sensory features. Simulations demonstrate the network can recognize objects through sequences of movements and sensations.
[3] There is suggestive anatomical and physiological evidence mapping the proposed model to biology, including grid cell signatures in human cortex and sensorimotor prediction in primary sensory areas.
Automated Analysis of Microscopy Images using Deep Convolutional Neural Network
This document summarizes research on using deep convolutional neural networks to automatically analyze microscopy images. The goals are to expedite the analysis of high-content microscopy data and automate tasks like cell counting and classification. The researchers trained and tested models using TensorFlow on microscopy images to classify cells, achieving over 75% accuracy. This level of automation could benefit biological research by reducing human errors and speeding up analysis of large image datasets.
Operating system
This document discusses supervised and unsupervised training of artificial neural networks. In supervised training, both the inputs and desired outputs are provided to the network during training. The network adjusts its weights to match the outputs to the desired outputs. Unsupervised training only provides inputs to the network, and it must group the input data without guidance on desired outputs. Both approaches require sufficient data for training and testing the network. Supervised learning is more common and achieves better results currently.
What's hot (14)
Neuroscience Information Framework Ontologies: Nerve cells in Neurolex and NI...
Neuroscience Information Framework Ontologies: Nerve cells in Neurolex and NI...
Neural Network Applications In Machining: A Review
Neural Network Applications In Machining: A Review
Deep learning and feature extraction for time series forecasting
Deep learning and feature extraction for time series forecasting
Осадчий А.Е. Анализ многомерных магнито- и электроэнцефалографических данных ...
Осадчий А.Е. Анализ многомерных магнито- и электроэнцефалографических данных ...
Jeff Hawkins Human Brain Project Summit Keynote: "Location, Location, Locatio...
Jeff Hawkins Human Brain Project Summit Keynote: "Location, Location, Locatio...
Location, Location, Location - A Framework for Intelligence and Cortical Comp...
Location, Location, Location - A Framework for Intelligence and Cortical Comp...
Have We Missed Half of What the Neocortex Does? A New Predictive Framework ...
Have We Missed Half of What the Neocortex Does? A New Predictive Framework ...
Could A Model Of Predictive Voting Explain Many Long-Range Connections? by Su...
Could A Model Of Predictive Voting Explain Many Long-Range Connections? by Su...
Locations in the Neocortex: A Theory of Sensorimotor Prediction Using Cortica...
Locations in the Neocortex: A Theory of Sensorimotor Prediction Using Cortica...
Automated Analysis of Microscopy Images using Deep Convolutional Neural Network
Automated Analysis of Microscopy Images using Deep Convolutional Neural Network
Viewers also liked
Reward
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.
The Models of Alzheimer's Disease Part II
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.
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
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
The Models of Alzheimer's Disease Part I
Speaker: Jimmy Lu
Topics: The Models of Alzheimer's Disease Part I
Date: 2011.02.19
WECO Lab, CSIE, FJU
Renaissance of JUnit - Introduction to JUnit 5
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.
Event sourcing with reactor and spring statemachine
An idea to apply reactor, spring statemachine to event sourcing pattern to build event driven applications.
Bootify your spring application
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.
Adolescent Brain Development And Its Effects
The document discusses adolescent brain development and its implications. It notes that the prefrontal cortex, responsible for reasoning and problem solving, develops last. During adolescence, the brain undergoes synaptic pruning and myelination in the frontal lobes. This results in improved abstract thinking abilities but also impaired emotional control and judgment. Teens may engage in risky behavior due to a less developed prefrontal cortex. The document emphasizes the importance of supporting adolescent well-being, competence, confidence, connections, character and sleep for healthy development.
Viewers also liked (8)
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
Event sourcing with reactor and spring statemachine
Event sourcing with reactor and spring statemachine
Similar to The Model of Spatiotemporal Dynamics of Alzheimer’s Disease
A tutorial in Connectome Analysis (0) - Marcus Kaiser
This document provides an overview of a tutorial on connectome analysis given by Dr. Marcus Kaiser. The tutorial covers topics such as graph theory, spatial and topological properties of neural networks. It also discusses how brain structure is influenced by function and evolution. Computer simulations are presented that model brain dynamics and can predict the location of epileptic tissue or the effects of optogenetic stimulation. Dr. Kaiser's research group at Newcastle University studies brain connectivity across species using neuroimaging and modeling approaches.
Consciousness, Graph theory and brain network tsc 2017
How does our brain create consciousness?
It's a great mystery!
New research published in New Journal of Physics tries to find the "conscious network" in our cortex.
They decomposed the structural layers of our cortical network to different hierarchies enabling to identify hierarchy of data integration in the cortex and the network’s nucleus. This nucleus is the most connected area in the network, from which our consciousness could emerge.
the original article in New Journal of Physics:
"K-shell decomposition reveals hierarchical cortical organization of the human brain"
by: Nir Lahav, Baruch Ksherim, Eti Ben-Simon, Adi Maron-Katz, Reuven Cohen and Shlomo Havlin (from Bar Ilan university and Tel Aviv university, Israel):
http://iopscience.iop.org/article/10.1088/1367-2630/18/8/083013/meta;jsessionid=BF44F1E6AEA7A74EAA4C0414FD01D617.c4.iopscience.cld.iop.org?platform=hootsuite
short video:
Where is my mind? physicists look for consciousness in the brain -
https://www.youtube.com/watch?v=k2qVFjzyyxI
Copyrights of the presentation "Consciousness, Graph theory and brain network tsc 2017" by "Nir Lahav":
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Please give credit for this presentattion and for Nir Lahav.
Nencki321 day2
(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.
Lecture at the C3BI 2018
This document discusses two decades of model sharing in systems biology. Over the past 20 years, there has been significant progress in developing standards and software for sharing mathematical models. This includes standards for describing models (SBML), simulations (SED-ML), and annotations (MIRIAM). Major model repositories now host thousands of shared models across various domains. Standardization has enabled large-scale model reconstruction, validation of existing models, and discovery of new models from data.
Design the Brain Simulator
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.
Mauricio carrillo tripp
This document discusses computational biophysics, including current trends, needs, and challenges regarding high-performance computing (HPC). Computational biophysics uses physics-based modeling and simulation to study biological processes at the molecular level. Simulation sizes have increased from 20,000 atoms in 2000 to over 3 million atoms currently. Emerging needs include faster CPUs, solid state storage, and GPUs to analyze large simulation data in real time. Connectivity and infrastructure also need improvements to enable larger multicenter simulations. Overall, computational biophysics provides insights into cellular functions and mechanisms of life through increasingly detailed molecular simulations.
Computational neuropharmacology drug designing
This document discusses computational neuropharmacology, which uses computational modeling approaches from neuroscience and dynamical systems theory integrated with traditional neuropharmacological methods to study drug effects on the brain and behavior. It describes how computational models are used in neuroscience to simulate neurons, neural circuits, and brain regions. It suggests computational neuropharmacology could help integrate molecular and systems-level descriptions of the nervous system to analyze drug effects on neural activity patterns and behavioral states. This may provide strategies for molecular screening of drugs and searching for target-specific drugs to shift pathological brain dynamics to normal patterns.
Reinterpreting the Cortical Circuit
Presentation at the First International Workshop on Architectures for Generality & Autonomy (AGA 2017), part of IJCAI 2017 in Melbourne.
20141003.journal club
- Researchers used a hierarchical convolutional neural network (CNN) optimized for object categorization performance to predict neural responses in higher visual cortex.
- The top layer of the CNN accurately predicted responses in inferior temporal (IT) cortex, and intermediate layers predicted responses in V4 cortex.
- This suggests that biological performance optimization directly shaped neural mechanisms in visual processing areas, as the CNN was not explicitly trained on neural data but emerged as predictive of responses in IT and V4.
INVITEDP A P E RSilicon-Integrated High-DensityElectro.docx
INVITED
P A P E R
Silicon-Integrated High-Density
Electrocortical Interfaces
This paper examines the state of the art of chronically implantable
electrocorticography (ECoG) interface systems and introduces a novel modular
ECoG system using an encapsulated neural interfacing acquisition chip (ENIAC)
that allows for improved, broad coverage in an area of high spatiotemporal
resolution.
By Sohmyung Ha, Member IEEE, Abraham Akinin, Student Member IEEE,
Jiwoong Park, Student Member IEEE, Chul Kim, Student Member IEEE,
Hui Wang, Student Member IEEE, Christoph Maier, Member IEEE,
Patrick P. Mercier, Member IEEE, and Gert Cauwenberghs, Fellow IEEE
ABSTRACT | Recent demand and initiatives in brain research
have driven significant interest toward developing chronically
implantable neural interface systems with high spatiotempo-
ral resolution and spatial coverage extending to the whole
brain. Electroencephalography-based systems are noninva-
sive and cost efficient in monitoring neural activity across the
brain, but suffer from fundamental limitations in spatiotem-
poral resolution. On the other hand, neural spike and local
field potential (LFP) monitoring with penetrating electrodes
offer higher resolution, but are highly invasive and inade-
quate for long-term use in humans due to unreliability in
long-term data recording and risk for infection and inflamma-
tion. Alternatively, electrocorticography (ECoG) promises a
minimally invasive, chronically implantable neural interface
with resolution and spatial coverage capabilities that, with
future technology scaling, may meet the needs of recently
proposed brain initiatives. In this paper, we discuss the chal-
lenges and state-of-the-art technologies that are enabling
next-generation fully implantable high-density ECoG inter-
faces, including details on electrodes, data acquisition front-
ends, stimulation drivers, and circuits and antennas for
wireless communications and power delivery. Along with
state-of-the-art implantable ECoG interface systems, we
introduce a modular ECoG system concept based on a fully
encapsulated neural interfacing acquisition chip (ENIAC).
Multiple ENIACs can be placed across the cortical surface,
enabling dense coverage over wide area with high spatio-
temporal resolution. The circuit and system level details of
ENIAC are presented, along with measurement results.
KEYWORDS | BRAIN Initiative; electrocorticography; neural
recording; neural stimulation; neural technology
I. INTRODUCTION
The Brain Research through Advancing Innovative Neuro-
technologies (BRAIN) Initiative envisions expanding our
understanding of the human brain. It targets development
and application of innovative neural technologies to ad-
vance the resolution of neural recording, and stimulation
toward dynamic mapping of the brain circuits and process-
ing [1], [2]. These advanced neurotechnologies will enable
new studies and experiments to augment our current unde ...
Electrophysiological imaging for advanced pharmacological screening
We at 3Brain are committed to advancing scientific research and boosting drug discovery. Like our technology, our product lines are always evolving to accommodate high-resolution recording of in vitro cultures. Discover our HD-MEA technology and soon-to-be-released devices and see how they are furthering research in brain diseases, drug discovery, retinal organoids, etc..
For more information, visit our website at https://www.3brain.com
Why Neurons have thousands of synapses? A model of sequence memory in the brain
Presentation given by Yuwei Cui, Numenta Research Engineer at Beijing Normal University. December 2015.
Collaborators: Jeff Hawkins, Subutai Ahmad, Chetan Surpur
Neural Engineering Research Overview
The document summarizes research on neural engineering related to cochlear implants and intracortical microelectrodes. It discusses:
1) Cochlear implant research involving developing a method to fit implants using stapedius electromyography recordings in rats.
2) Chronic neural interfacing research using intracortical microelectrodes to record brain activity, the challenges of long-term recordings due to tissue encapsulation, and methods explored to address this like enzyme-aided electrode insertion.
3) The quantification of recording performance over time and correlations with electrode impedance.
Reservoir Computing Overview (with emphasis on Liquid State Machines)
A brief presentation about concepts and research in reservoir computing, presented in COSC594 Fall 2016 at the University of Tennessee
Principles of Hierarchical Temporal Memory - Foundations of Machine Intelligence
This document provides an overview of a workshop on hierarchical temporal memory (HTM) held by Numenta on October 17, 2014. The key points discussed include:
1) Numenta's mission is to discover the operating principles of the neocortex and create machine intelligence technology based on these principles.
2) HTM is based on theories about how the neocortex works, and models the neocortex as a hierarchical system that learns sequences and makes predictions.
3) Numenta's research roadmap focuses on developing HTM applications for tasks like prediction, anomaly detection, and goal-oriented behavior.
Una estrategia para la integración de ontologías, servicios web y PLN en el a...
Conferencia presentada por el Ing. José López, como parte del X Coloquio Internacional de Tecnologías aplicadas a los Serivicios de Información
736 icat a
This study aimed to classify the epileptic state of patients as pre-ictal, ictal, or inter-ictal using EEG data and machine learning techniques. EEG data was obtained from the Children's Hospital Boston database. Features were extracted using a second order difference plot technique. An artificial neural network with three hidden layers was used to classify the epileptic states with an overall accuracy of 98.7%. The study demonstrated that epileptic states can be classified using machine learning algorithms applied to EEG data.
What is (computational) neuroscience?
Computational neuroscience is the scientific study of the nervous system using computational approaches. It is an interdisciplinary field that uses techniques from biology, chemistry, computer science, engineering, linguistics, mathematics, medicine, physics, psychology and philosophy to study the molecular, cellular, developmental, structural, functional, evolutionary and medical aspects of the nervous system. Some examples of current areas of study include Parkinson's disease, epilepsy, hearing loss, and brain-machine interfaces. Computational neuroscience aims to understand what computations are performed in neural systems and how they are implemented at molecular, cellular and system levels.
Lecture artificial neural networks and pattern recognition
This document provides an overview of artificial neural networks and pattern recognition. It discusses key topics such as:
- The basic anatomy and function of artificial neurons and how they are modeled after biological neurons.
- Different types of neural networks including feedforward networks, recurrent networks, self-organizing maps, and Hopfield networks.
- Popular supervised and unsupervised learning algorithms like backpropagation and self-organizing feature maps.
- Examples of applications like handwritten character recognition, stock price prediction, and memory recall in Hopfield networks.
The document serves as an introduction for students to understand the basic concepts and applications of artificial neural networks.
Similar to The Model of Spatiotemporal Dynamics of Alzheimer’s Disease (20)
A tutorial in Connectome Analysis (0) - Marcus Kaiser
A tutorial in Connectome Analysis (0) - Marcus Kaiser
Consciousness, Graph theory and brain network tsc 2017
Consciousness, Graph theory and brain network tsc 2017
INVITEDP A P E RSilicon-Integrated High-DensityElectro.docx
INVITEDP A P E RSilicon-Integrated High-DensityElectro.docx
Electrophysiological imaging for advanced pharmacological screening
Electrophysiological imaging for advanced pharmacological screening
Why Neurons have thousands of synapses? A model of sequence memory in the brain
Why Neurons have thousands of synapses? A model of sequence memory in the brain
Reservoir Computing Overview (with emphasis on Liquid State Machines)
Reservoir Computing Overview (with emphasis on Liquid State Machines)
Principles of Hierarchical Temporal Memory - Foundations of Machine Intelligence
Principles of Hierarchical Temporal Memory - Foundations of Machine Intelligence
Una estrategia para la integración de ontologías, servicios web y PLN en el a...
Una estrategia para la integración de ontologías, servicios web y PLN en el a...
Lecture artificial neural networks and pattern recognition
Lecture artificial neural networks and pattern recognition
More from Jimmy Lu
All the troubles you get into when setting up a production ready Kubernetes c...
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 Cluster
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.
The Models of Alzheimer's Disease Part III
Speaker: Jimmy Lu
Topics: The Models of Alzheimer's Disease Part III
Date: 2011.03.26
WECO Lab, CSIE, FJU
On the Development of a Brain Simulator
Speaker: Jimmy Lu
Topics: On the Development of a Brain Simulator
Date: 2010.11.06
WECO Lab, CSIE, FJU
Research Proposal and Milestone
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.
On the Development of a Brain Simulator
Speaker: Jimmy Lu
Topics: On the Development of a Brain Simulator
Date: 2010.08.26
WECO Lab, CSIE, FJU
Mining the Parkinson's Telemonitoring Data Set
Speaker: 周思婷、林芳竹、趙健復、鄭筱頻、盧松筠
Topics: Exploring Complex Networks
Date: 2010.05.29
WECO Lab, CSIE, FJU
Brian Simulator (a draft)
Speaker: Tony Tseng, Jimmy Lu
Topics: Exploring Complex Networks
Date: 2010.05.29
WECO Lab, CSIE, FJU
Exploring Complex Networks
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.
Brain Network - Thalamocortical Motif
Speaker: Jimmy Lu
Topics: Brain Network - Thalamocortical Motif
Date: 2010.03.20
WECO Lab, CSIE, FJU
How To Build A Personal Portal On Google App Engine With Django
Speaker: Jimmy Lu
Topics: How To Build A Personal Portal On Google App Engine With Django
Date: 2010.02.19
WECO Lab, CSIE, FJU
Brain Networks
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.
More from Jimmy Lu (13)
All the troubles you get into when setting up a production ready Kubernetes c...
All the troubles you get into when setting up a production ready Kubernetes c...
How To Build A Personal Portal On Google App Engine With Django
How To Build A Personal Portal On Google App Engine With Django
Recently uploaded
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Essentials of Automations: Exploring Attributes & Automation Parameters
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...Pitangent Analytics & Technology Solutions Pvt. Ltd
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
Leveraging the Graph for Clinical Trials and Standards
Katja Glaß
OpenStudyBuilder Community Manager - Katja Glaß Consulting
Marius Conjeaud
Principal Consultant - Neo4j
Apps Break Data
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Artificial Intelligence and Electronic Warfare
Artificial Intelligence and Electronic WarfarePapadakis K.-Cyber-Information Warfare Analyst & Cyber Defense/Security Consultant-Hellenic MoD
AI & Electronic WarfareGraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Tomaz Bratanic
Graph ML and GenAI Expert - Neo4j
Generating privacy-protected synthetic data using Secludy and Milvus
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/how-axelera-ai-uses-digital-compute-in-memory-to-deliver-fast-and-energy-efficient-computer-vision-a-presentation-from-axelera-ai/
Bram Verhoef, Head of Machine Learning at Axelera AI, presents the “How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-efficient Computer Vision” tutorial at the May 2024 Embedded Vision Summit.
As artificial intelligence inference transitions from cloud environments to edge locations, computer vision applications achieve heightened responsiveness, reliability and privacy. This migration, however, introduces the challenge of operating within the stringent confines of resource constraints typical at the edge, including small form factors, low energy budgets and diminished memory and computational capacities. Axelera AI addresses these challenges through an innovative approach of performing digital computations within memory itself. This technique facilitates the realization of high-performance, energy-efficient and cost-effective computer vision capabilities at the thin and thick edge, extending the frontier of what is achievable with current technologies.
In this presentation, Verhoef unveils his company’s pioneering chip technology and demonstrates its capacity to deliver exceptional frames-per-second performance across a range of standard computer vision networks typical of applications in security, surveillance and the industrial sector. This shows that advanced computer vision can be accessible and efficient, even at the very edge of our technological ecosystem.Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Principle of conventional tomography-Bibash Shahi ppt..pptx
before the computed tomography, it had been widely used.
Mutation Testing for Task-Oriented Chatbots
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Recently uploaded (20)
Nordic Marketo Engage User Group_June 13_ 2024.pptx
Nordic Marketo Engage User Group_June 13_ 2024.pptx
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Essentials of Automations: Exploring Attributes & Automation Parameters
Essentials of Automations: Exploring Attributes & Automation Parameters
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...
Leveraging the Graph for Clinical Trials and Standards
Leveraging the Graph for Clinical Trials and Standards
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
GraphRAG for LifeSciences Hands-On with the Clinical Knowledge Graph
Generating privacy-protected synthetic data using Secludy and Milvus
Generating privacy-protected synthetic data using Secludy and Milvus
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
The Microsoft 365 Migration Tutorial For Beginner.pptx
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Principle of conventional tomography-Bibash Shahi ppt..pptx
Principle of conventional tomography-Bibash Shahi ppt..pptx
The Model of Spatiotemporal Dynamics of Alzheimer’s Disease
- 1. The Model of Spatiotemporal Dynamics of Alzheimer’s Disease Speaker : Jimmy Lu Advisor : Hsing Mei Web Computing Laboratory (WECO Lab) Computer Science and Information Engineering Department Fu Jen Catholic University
- 2. New Cases Case Study and Analysis Layered Architecture Extending and Refactoring Existing Cases Cases Integration Brain Components Extending and Refactoring Feedback Build Theoretical models Evaluate Theoretical Models Evolved Brain Simulator
- 3. Isocortical Areas (including the belt fields and primary areas) Isocortex Association Area Basal Portion of Occipital Lobe Basal Portion of Frontal Lobe Basal Portion of Limbic Lobe
- 4. Isorcortex Limbic Area (involve the entorhinal and transentorhinal layer Pre-α) Transentorhinal Region
- 5. 5K 3.6 4K 2.0 1.2 1K
- 6. 𝜃𝑘𝑚𝑎𝑥𝑡−𝑘=1 𝑖𝑓 𝑘≤𝑘𝑚𝑎𝑥0 𝑖𝑓 𝑘≥𝑘𝑚𝑎𝑥 𝑘𝑚𝑎𝑥𝑡=𝑘𝑚𝑎𝑥−𝑡𝑝 Where 𝒌𝒎𝒂𝒙represents a certain degree of nodes, 𝒑is a period of time that controls the duration of an attack
- 7. 0K 5K 3.6K 0 3.6 2.8 0 2.0 1.2