Brain Networks
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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.
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The document discusses the use of perfusion MRI and MR spectroscopy in evaluating brain tumors. It states that perfusion MRI can help estimate tumor grade, guide biopsy to aggressive portions, and better delineate tumor margins compared to conventional MRI. MR spectroscopy can detect metabolic alterations to help characterize tumors and differentiate them from other conditions like abscesses. Together, perfusion MRI and MR spectroscopy provide physiological and metabolic information complementary to anatomical MRI for diagnosing and monitoring brain tumors.
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Analytical Review on the Correlation between Ai and Neuroscience
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.
Network Science: Theory, Modeling and Applications
This document summarizes a lecture on network science given by Madhav Marathe at Lawrence Livermore National Laboratory in December 2010. It provides an overview of network science, including definitions of networks and their unique properties. It also discusses mathematical and computational approaches to modeling complex networks and applications to infrastructure planning, energy systems, and national security. The lecture acknowledges prior work that contributed to its material from various researchers and textbooks.
Pattern Formation Drosophila
The document discusses two approaches to modeling pattern formation during embryonic development in fruit flies (Drosophila): nonlinear dynamics and Boolean logic. It presents a dynamical model of the segment polarity gene network in Drosophila that is modeled using kinetic equations with 50 free parameters. It questions how the network can be built more systematically and models shared more efficiently. It proposes extracting subnetworks from online pathway databases to build a gene network model for early wing development in Drosophila and simulate mutations by fixing node values to verify the model against experimental phenotypes.
A tutorial in Connectome Analysis (3) - Marcus Kaiser
This document discusses various topological and spatial features of brain networks, including small-world properties, motifs, clusters, degree distributions, and robustness. It provides examples of analyses conducted on structural and functional brain networks, such as detecting clusters in the cat cortex and examining the effects of simulated brain lesions. Modular organization is highlighted as important for local integration and global separation of processing. Developing brain networks are found to require less information to encode connectivity patterns compared to random networks.
Convolutional Networks
The verbal fluency test involves two groups of students, one group of art students and one group of non-art students. Brain scans using voxel-based morphometry found that art students had more gray matter in the parietal lobe, a region linked to visual imagery and object manipulation. This suggests art training is associated with differences in brain structure related to visual-spatial skills.
An Overview On Neural Network And Its Application
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.
Maps of sparse memory networks reveal overlapping communities in network flows
1. The document discusses using sparse memory networks and higher-order flow modeling to map flows through complex systems and reveal overlapping communities.
2. It addresses three challenges: the detectability limit of conventional approaches, the problem of many different network representations, and selecting an appropriate scale and model.
3. The solutions proposed are using higher-order network representations like memory and multilayer networks, representing these with a single sparse memory network framework, and choosing an appropriate sparse network using state lumping and cross-validation.
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.
Artificial Neural Networks
Artificial Neural Networks is a calculation method that builds several processing units based on
interconnected connections. The network consists of an arbitrary number of cells or nodes or units
or neurons that connect the input set to the output. It is a part of a computer system that mimics how
the human brain analyzes and processes data. Self-driving vehicles, character recognition, image
compression, stock market prediction, risk analysis systems, drone control, welding quality analysis,
computer quality analysis, emergency room testing, oil and gas exploration and a variety of other
applications all use artificial neural networks. Predicting consumer behavior, creating and
understanding more sophisticated buyer segments, marketing automation, content creation and
sales forecasting are some applications of the ANN systems in the marketing. In this paper, a review
in recent development and applications of the Artificial Neural Networks is presented in order to move
forward the research filed by reviewing and analyzing recent achievements in the published papers.
Thus, the developed ANN systems can be presented and new methodologies and applications of the
ANN systems can be introducedArtificial Neural Networks (ANNs), or more simply neural networks, are new systems and computational
methods for machine learning, knowledge demonstration, and finally the application of knowledge
gained to maximize the output responses of complex systems (Chen et al. 2019). An Artificial Neural
Network (ANN) is a data processing model based on the way biological nervous systems, such as the
brain, process data. They're focused on the neuronal structure of the mamalian cerebral cortex, but at
a much smaller scale. Many artificial intelligence experts believe that artificial neural networks are the Artificial neural networks are designed in the same way as the human brain, with neuron nodes
interconnected in a web-like fashion. Neurons are billions of cells that make up the human brain. Each
neuron is made up of a cell body that processes information by bringing it to and from the brain (inputs
and outputs) (Van Gerven and Bohte 2017). The main idea of such networks is (to some extent) inspired
by the way the biological neural system works, to process data, and information in order to learn and
create knowledge. The key element of this idea is to create new structures for the information
processing system. The Artificial neural network architecture is shown in the figure 2 (Bre, Gimenez,
and Fachinotti 2018).The system is made up of a large number of highly interconnected processing elements called neurons
that work together to solve a problem and transmit information through synapses (electromagnetic
connections). The neurons are interconnected closely and organized into layer. The input layer receives the data, while the output layer generates the final result. Between the two, one or more secret layers are typically sandwiched. This arrangement makes predicting
P2-Artificial.pdf
Artificial Neural Networks is a calculation method that builds several processing units based on
interconnected connections. The network consists of an arbitrary number of cells or nodes or units
or neurons that connect the input set to the output. It is a part of a computer system that mimics how
the human brain analyzes and processes data. Self-driving vehicles, character recognition, image
compression, stock market prediction, risk analysis systems, drone control, welding quality analysis,
computer quality analysis, emergency room testing, oil and gas exploration and a variety of other
applications all use artificial neural networks.
An information-theoretic, all-scales approach to comparing networks
My presentation at NetSci 2018 on Portrait Divergence, a new approach to comparing networks that is simple, general-purpose, and easy to interpret.
The preprint: https://arxiv.org/abs/1804.03665
The code: https://github.com/bagrow/portrait-divergence
Network Biology: A paradigm for modeling biological complex systems
These slides are part of the two lectures delivered at the as part of the 'National Workshop on Network Modelling and Graph Theory' (Dec 14-16, 2017) at Department of Mathematics, Dibrugarh University, Assam, India.
(1) Network Biology: A paradigm for integrative modeling of biological complex systems -- 14 Dec 2017, 3:30pm
(2) Applications of network modeling in biomedicine -- 15 Dec 2017, 9:00pm
Sponsored by UGC under SAP DRS (II)
(1) Workshop link: https://www.dibru.ac.in/upcoming-events/2981-national-workshop-on-network-modelling-and-graph-theory
(2) The Workshop Flyer: https://www.dibru.ac.in/images/uploaded_files/2017/Nov/National_Workshop_on_Network_Modelling_and_Graph_Theory.pdf
Albus
1) Intelligence is defined as the ability to act appropriately in uncertain environments in order to achieve goals and succeed.
2) Natural intelligence evolved through natural selection to produce behaviors that increase survival and reproduction.
3) More intelligent individuals and groups are better able to sense their environment, make decisions, and take actions that provide biological advantages over less intelligent competitors.
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
Technology R&D Theme 3: Multi-scale Network Representations
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
A Prototype of Brain Network Simulator for Spatiotemporal Dynamics of Alzheim...
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Date: 2011.05.31
Defense of WECO Lab at CSIE, FJU
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.
Community Analysis of Deep Networks (poster)
This document proposes research to analyze hidden unit collaboration and community formation in deep networks. It defines collaborating units as having high mutual information and communities as sets with greater within- than between-set collaboration. It poses leading questions on community existence, necessity, specialization, and abstraction. Preliminary experiments on MNIST and toy data trained stacked denoising autoencoders and measured community modularity and size over epochs, finding fluctuation that stabilizes with training. Future work is outlined on overlapping communities, evolution relations, layer/unit effects, overfitting, and learning algorithms.
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A Kulkarni
[IJET V2I2P20] Authors: Dr. Sanjeev S Sannakki, Ms.Anjanabhargavi A KulkarniIJET - International Journal of Engineering and Techniques
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
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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.
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.
The Model of Spatiotemporal Dynamics of Alzheimer’s Disease
Speaker: Jimmy Lu
Topics: The Model of Spatiotemporal Dynamics of Alzheimer's Disease
Date: 2011.04.23
WECO Lab, CSIE, FJU
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.
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
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
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.
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.
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.
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
How To Build A Personal Portal On Google App Engine With Django
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Topics: How To Build A Personal Portal On Google App Engine With Django
Date: 2010.02.19
WECO Lab, CSIE, FJU
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“How Axelera AI Uses Digital Compute-in-memory to Deliver Fast and Energy-eff...
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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.
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Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
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A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Dandelion Hashtable: beyond billion requests per second on a commodity server
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GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Columbus Data & Analytics Wednesdays - June 2024
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Artificial Intelligence and Electronic Warfare
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Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Driving Business Innovation: Latest Generative AI Advancements & Success Story
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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!
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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.
Leveraging the Graph for Clinical Trials and Standards
Katja Glaß
OpenStudyBuilder Community Manager - Katja Glaß Consulting
Marius Conjeaud
Principal Consultant - Neo4j
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.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?
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Brain Networks
- 1. Brian networks2010/02/06Brain Anatomical Network and Intelligence&Organization, development and function of complex brain networks Speaker : Jimmy Lu Advisor : Hsing Mei Web Computing Laboratory Computer Science and Information Engineering Department Fu Jen Catholic University
- 2. Outline Introduction Background Graph theory Complex networks Examine principles (three aspects) Structural, functional, effective connectivity Clinical terms Experiment Future work 2
- 3. Introduction Examines principles Structural properties Relationship between the structural substrate and dynamic functional and effective connectivity patterns The efficiency of brain structural organization may be an important biological basis for intelligence 3
- 4. Background – graph theory Adjacency (connection) matrix Characteristic path length Clustering coefficient Connectedness Cycle Degree Distance Distance matrix Graph Path Regular graph Random graph Scale-free graph Small-world networks
- 5. Background – complex networks Random networks Small-world networks Scale-free networks
- 6. Background – three aspects Neuroanatomical substrate (structural connectivity) Dynamics of neurons (functional connectivity) Functional segregation Functional integration Causal interaction (effective connectivity)
- 7. Background - structural Network participation indices In-degree, out-degree transmission coefficient Broadcasters, integrators cooperative Matching index Connectional fingerprint Motif analysis
- 8. Background - structural Graph theoretical analysis High clustering, short path length Functional proximity Multivariate analysis Multidimensional scaling Hierarchical cluster Evolutionary optimization Inter-cluster connection Connectedness
- 13. Background – development Local spatial growth rules Preferential attachment Global network design
- 14. Background – clinical terms Anatomical Automatic Labeling (AAL) Magnetic resonance imaging (MRI) Diffusion MRI Functional MRI (fMRI) In vivo Cerebral cortex Tractography
- 15. Background - functional There are some problems in the previous work Only binary networks Nonzero connection probability value to brain region pairs which are unlikely to be connected Etc... But they all have evidence that brain network is close to small-world
- 16. Background - experiment 79 subjects (44 males and 35 females, mean age = 23.8 years, range = 17-33 years) intelligence quotient (IQ) test (FSIQ, PIQ, VIQ) GI (general intelligence)/HI (high intelligence) group Diffusion tensor image (DTI)/T1 image/AAL co-register Normalize, inverse Construct binary and weighted network of an individual brain Anatomical network analyses Statistical analysis
- 25. Future work
- 26. Future work From the computer science perspective How about the social network? Other internet overlaid networks? A brain simulator? Etc…?
- 27. Reference Li Y, Liu Y, Li J, Qin W, Li K, et al. (2009) Brain Anatomical Network and Intelligence. PLoSComput Olaf Sporns, Dante R. Chialvo, Marcus Kaiser and Claus C. Hilgetag (2004) Organization, development and function of complex brain networks. TRENDS in Cognitive Sciences Vol.8 No.9