The document discusses the structure and function of the brain as a complex network. It notes that the brain exhibits both segregation and integration at multiple scales from neurons to regions. The structural connectivity of the brain forms a small-world network that allows for both specialized processing within clusters and integrated processing between regions via short path lengths. Computational models can capture large-scale brain activity and dynamics based on the underlying structural connectivity.
1. The document discusses several key aspects of artificial neural networks including their architecture, learning algorithms, and applications.
2. ANNs are modeled after biological neural networks and utilize features such as parallel distributed processing, learning from examples, and the ability to generalize.
3. The document covers various ANN architectures including feedforward networks, recurrent networks, and different learning methods like supervised and unsupervised learning.
(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.
This document discusses using network and semantic analysis to map disciplinary structures in cognitive neuroscience. It provides examples of contemporary meta-analyses tools like Neurosynth and the Cognitive Atlas that synthesize knowledge in the field using semantic terminology and brain locations. The document outlines applying network analysis techniques like text network analysis to represent relations between anatomy and concept terms found in cognitive neuroscience literature. It describes generating networks from a corpus of cognitive neuroscience articles and analyzing the conceptual, anatomical, and functional network structures that emerge. Limitations and future directions are also discussed.
Consciousness, Graph theory and brain network tsc 2017Nir Lahav
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.
Toward Tractable AGI: Challenges for System Identification in Neural CircuitryRandal Koene
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon.
Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
Neural networks are inspired by biological neural networks and are composed of interconnected processing elements called neurons. Neural networks can learn complex patterns and relationships through a learning process without being explicitly programmed. They are widely used for applications like pattern recognition, classification, forecasting and more. The document discusses neural network concepts like architecture, learning methods, activation functions and applications. It provides examples of biological and artificial neurons and compares their characteristics.
A framework for approaches to transfer of mind substrateKarlos Svoboda
This document outlines a framework for discussing approaches to transferring a mind's substrate. It summarizes recent developments in neural prosthesis that could allow functional replacement of brain parts, potentially leading to a form of "mind-substrate transfer." It reviews two main proposed approaches to mind-substrate transfer: 1) Reconstruction from a brain scan, which would involve scanning the brain at high resolution and simulating its functioning. 2) Reconstruction from behavior, which would involve collecting behavioral information about an individual to parametrize a generic substrate. It argues that an underlying question is what constitutes a person's identity and whether identity could be transferred between original and synthetic substrates.
1. The document discusses several key aspects of artificial neural networks including their architecture, learning algorithms, and applications.
2. ANNs are modeled after biological neural networks and utilize features such as parallel distributed processing, learning from examples, and the ability to generalize.
3. The document covers various ANN architectures including feedforward networks, recurrent networks, and different learning methods like supervised and unsupervised learning.
(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.
This document discusses using network and semantic analysis to map disciplinary structures in cognitive neuroscience. It provides examples of contemporary meta-analyses tools like Neurosynth and the Cognitive Atlas that synthesize knowledge in the field using semantic terminology and brain locations. The document outlines applying network analysis techniques like text network analysis to represent relations between anatomy and concept terms found in cognitive neuroscience literature. It describes generating networks from a corpus of cognitive neuroscience articles and analyzing the conceptual, anatomical, and functional network structures that emerge. Limitations and future directions are also discussed.
Consciousness, Graph theory and brain network tsc 2017Nir Lahav
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.
Toward Tractable AGI: Challenges for System Identification in Neural CircuitryRandal Koene
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon.
Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
Neural networks are inspired by biological neural networks and are composed of interconnected processing elements called neurons. Neural networks can learn complex patterns and relationships through a learning process without being explicitly programmed. They are widely used for applications like pattern recognition, classification, forecasting and more. The document discusses neural network concepts like architecture, learning methods, activation functions and applications. It provides examples of biological and artificial neurons and compares their characteristics.
A framework for approaches to transfer of mind substrateKarlos Svoboda
This document outlines a framework for discussing approaches to transferring a mind's substrate. It summarizes recent developments in neural prosthesis that could allow functional replacement of brain parts, potentially leading to a form of "mind-substrate transfer." It reviews two main proposed approaches to mind-substrate transfer: 1) Reconstruction from a brain scan, which would involve scanning the brain at high resolution and simulating its functioning. 2) Reconstruction from behavior, which would involve collecting behavioral information about an individual to parametrize a generic substrate. It argues that an underlying question is what constitutes a person's identity and whether identity could be transferred between original and synthetic substrates.
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.
This document is a preface to a book about neural networks. It provides an overview of the book's contents and objectives. The book aims to present a variety of standard neural network architectures along with their training algorithms and examples of applications. It is intended as both a textbook and reference for students and researchers interested in using neural networks. The preface outlines the scope and organization of the material covered in the book.
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.
This study used signal detection theory to examine how neuroscientists identify the default mode network compared to other prominent resting-state networks. Twenty participants were asked to distinguish the default mode network from three other networks in a rapid forced-choice task, where the networks were presented at different signal thresholds. Results showed that participants more accurately identified the default mode network when it was presented at the most stringent threshold, and made the most conservative decisions when networks were not thresholded. These findings suggest that thresholding fMRI data improves accuracy in identifying brain networks.
The document discusses neural correlates of higher level brain functions. It covers several topics:
1) Experience arises at the quantum level in ion channel proteins, with quantum properties like coherence and entanglement playing a role.
2) Construction of perception involves transitions from quantum to classical domains in the brain, mediated by ion channel proteins. Top-down processes and long-range connections in large brains are important for conscious perception.
3) Perception emerges from complex interactions between ascending and recurrent signaling in the brain, with feedback thought to be crucial for awareness. Receptive field properties evolve along synaptic distances in hierarchical cortical networks.
The sophisticated signal processing techniques developed during last years for structural and functional imaging methods allow us to detect abnormalities of brain connectivity in brain disorders with unprecedented detail. Interestingly, recent works shed light on both functional and structural underpinnings of musical anhedonia (i.e., the individual's incapacity to enjoy listening to music). On the other hand, computational models based on brain simulation tools are being used more and more for mapping the functional consequences of structural abnormalities. The latter could help to better understand the mechanism that is impaired in people unable to derive pleasure from music, and formulate hypotheses on how music acquired reward value. The presentation gives an overview of today's studies and proposes a possible simulation pipeline to reproduce such scenario.
DiscoversNet is an adaptive simulation-based learning environment for designing neural networks. It contains a knowledge-based neural network consultant module to provide educational guidance during the neural network design process. The consultant module represents domain knowledge using a knowledge-based neural network and provides advice to users based on their current understanding level. The system allows users to build neural network simulators through interactive manipulation of neural network components and receives feedback to correct misconceptions.
Introduction to Spiking Neural Networks: From a Computational Neuroscience pe...Jason Tsai
The document provides an introduction to spiking neural networks (SNNs) and neuromorphic computing. It discusses the characteristics and advantages of SNNs, including their spatio-temporal nature, asynchronous processing, sparsity, and energy efficiency. It also covers basic neuroscience concepts like neurons, action potentials, synaptic plasticity, and learning rules like STDP. Common SNN models and neural encoding schemes are described. Examples of SNN applications in visual processing and pattern generation are presented. Finally, neuromorphic hardware platforms like Intel's Loihi chip are introduced.
This document provides an overview of computational neuroscience from modeling single neurons to neural circuits and behavior. It discusses:
- Models of single neurons from the Hodgkin-Huxley model to reduced models like FitzHugh-Nagumo and Izhikevich neurons.
- How neurons are organized into neural circuits using different connection types and how properties like synchronization emerge from circuit properties.
- Approaches to modeling larger brain areas as neural populations using techniques like neural fields to model mean firing rates over continuous space.
- Phenomena like neural coding, plasticity, learning and their role in computational models of behaviors and cognition. It provides examples of modeling visual attention, decision making and more
This document outlines the course details for an "Intelligent Systems" course including 16 lectures and 8 practical works covering topics such as knowledge representation methods, expert systems, machine learning, natural language processing, intelligent robots, and the future of artificial intelligence. The course is taught by Professor Dr. Andrey V. Gavrilov and will provide students with basic concepts of different intelligent systems development methods and tools. Grades will be based on a midterm exam worth 50% and a final exam worth 50% of the total grade.
Functional brain parcellations break the brain into modules of regions with similar connectivity profiles. Parcellations exist over a range of scales from large scale networks to smaller specialized cortical areas. Larger parcels have higher homogeneity while stability is highest at the group level and more variable at the individual level. A number of metrics are used to evaluate parcellations including homogeneity, stability, and agreement with functional tasks. Individual parcellations provide more detail than group parcellations but estimating them jointly improves accuracy. Gradients provide an alternative to parcels by describing continuous connectivity patterns in the brain.
Cassandra audio-video sensor fusion for aggression detectionJoão Gabriel Lima
The document presents CASSANDRA, a system for detecting aggressive human behavior using audio-video sensor fusion. At the low level, audio and video streams are independently analyzed to extract intermediate descriptors like "scream" from audio and "articulation energy" from video. At the higher level, a Dynamic Bayesian Network fuses these descriptors and contextual knowledge to produce an aggregate aggression indication. The system was validated on scenarios performed by actors at a train station to ensure realistic noise conditions.
Analytical Review on the Correlation between Ai and NeuroscienceIOSR Journals
This document discusses the relationship between artificial intelligence and neuroscience. It describes how AI has benefited from studying neuroscience to better understand natural intelligence. Specifically, AI has used insights from neuroscience related to learning, perception, and reasoning by modeling neural mechanisms. The document also provides several examples of how AI and robotics have been influenced by neuroscience, including early robots designed to mimic animal behavior and more recent projects that apply insights about the brain to develop artificial neural networks or brain-inspired devices.
The document describes Knowledge Engineering from Experimental Design (KEfED), a semantic framework for representing biomedical experimental data and knowledge. KEfED models experiments using logical elements like activities, experimental objects, parameters, measurements, and branches to represent experimental designs, observations, and interpretations. It aims to introduce formalism to heterogeneous biomedical statements in a way that is intuitive for scientists. KEfED can be used as the basis for data repositories and integrated with tools like the Open Biomedical Ontology. Future work includes developing domain-specific reasoning models and semantic links to other frameworks.
The document discusses cognitive architectures, which are engineering approaches for modeling cognitive systems like humans. It notes that cognitive architectures aim to provide a unified set of mechanisms to explain various cognitive functions like language, problem solving, dreaming, goal-directed behavior, symbol usage, and learning. The document then reviews several specific cognitive architectures, including Soar, ACT-R, LIDA, and 4CAPS. It also discusses challenges in creating cognitive architectures that integrate symbolic and sub-symbolic approaches and can be implemented on neural hardware at large scales.
The document discusses how data mining techniques can be applied to analyze multichannel encephalographic recordings from brain activity by extracting patterns and relationships from large amounts of single-trial data in an unorganized format. It provides examples of how clustering and other algorithms have been used to summarize variability in regional brain responses and reveal couplings between ongoing activity and stimulus-evoked responses. The analysis of event-related dynamics using these methods has provided lessons about the dynamic processing performed by the brain.
Aspect oriented a candidate for neural networks and evolvable softwareLinchuan Wang
This is a paper written in 2004 and has been accepted by WASOD workshop 2004
https://people.cs.kuleuven.be/~dirk.craeynest/ada-belgium/events/04/040927-sefm-waosd.html
the meta data can be found in A Bibliography of Aspect-Oriented Software Development, Version 2.0
The document discusses neural computing and artificial neural networks. It provides an overview of several key topics, including the aims of investigating biological nervous systems and designing artificial systems that emulate biological principles. The document outlines several planned lectures on topics like the differences between natural and artificial intelligence, neurobiology, neural processing and signaling, stochasticity in neural codes, neural operators for vision, cognition and evolution, and artificial neural networks. It also lists some reference books and provides examples for exercises on neural computing concepts.
The document discusses fundamentals of neural networks and artificial intelligence. It provides an overview of topics covered in lectures 37 and 38, including the biological neuron model, artificial neuron model, neural network architectures, learning methods in neural networks, single-layer neural network systems, and applications of neural networks. It also includes details on the McCulloch-Pitts neuron model and the basic elements of an artificial neuron, such as weights, thresholds, and activation functions.
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.
This document is a preface to a book about neural networks. It provides an overview of the book's contents and objectives. The book aims to present a variety of standard neural network architectures along with their training algorithms and examples of applications. It is intended as both a textbook and reference for students and researchers interested in using neural networks. The preface outlines the scope and organization of the material covered in the book.
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.
This study used signal detection theory to examine how neuroscientists identify the default mode network compared to other prominent resting-state networks. Twenty participants were asked to distinguish the default mode network from three other networks in a rapid forced-choice task, where the networks were presented at different signal thresholds. Results showed that participants more accurately identified the default mode network when it was presented at the most stringent threshold, and made the most conservative decisions when networks were not thresholded. These findings suggest that thresholding fMRI data improves accuracy in identifying brain networks.
The document discusses neural correlates of higher level brain functions. It covers several topics:
1) Experience arises at the quantum level in ion channel proteins, with quantum properties like coherence and entanglement playing a role.
2) Construction of perception involves transitions from quantum to classical domains in the brain, mediated by ion channel proteins. Top-down processes and long-range connections in large brains are important for conscious perception.
3) Perception emerges from complex interactions between ascending and recurrent signaling in the brain, with feedback thought to be crucial for awareness. Receptive field properties evolve along synaptic distances in hierarchical cortical networks.
The sophisticated signal processing techniques developed during last years for structural and functional imaging methods allow us to detect abnormalities of brain connectivity in brain disorders with unprecedented detail. Interestingly, recent works shed light on both functional and structural underpinnings of musical anhedonia (i.e., the individual's incapacity to enjoy listening to music). On the other hand, computational models based on brain simulation tools are being used more and more for mapping the functional consequences of structural abnormalities. The latter could help to better understand the mechanism that is impaired in people unable to derive pleasure from music, and formulate hypotheses on how music acquired reward value. The presentation gives an overview of today's studies and proposes a possible simulation pipeline to reproduce such scenario.
DiscoversNet is an adaptive simulation-based learning environment for designing neural networks. It contains a knowledge-based neural network consultant module to provide educational guidance during the neural network design process. The consultant module represents domain knowledge using a knowledge-based neural network and provides advice to users based on their current understanding level. The system allows users to build neural network simulators through interactive manipulation of neural network components and receives feedback to correct misconceptions.
Introduction to Spiking Neural Networks: From a Computational Neuroscience pe...Jason Tsai
The document provides an introduction to spiking neural networks (SNNs) and neuromorphic computing. It discusses the characteristics and advantages of SNNs, including their spatio-temporal nature, asynchronous processing, sparsity, and energy efficiency. It also covers basic neuroscience concepts like neurons, action potentials, synaptic plasticity, and learning rules like STDP. Common SNN models and neural encoding schemes are described. Examples of SNN applications in visual processing and pattern generation are presented. Finally, neuromorphic hardware platforms like Intel's Loihi chip are introduced.
This document provides an overview of computational neuroscience from modeling single neurons to neural circuits and behavior. It discusses:
- Models of single neurons from the Hodgkin-Huxley model to reduced models like FitzHugh-Nagumo and Izhikevich neurons.
- How neurons are organized into neural circuits using different connection types and how properties like synchronization emerge from circuit properties.
- Approaches to modeling larger brain areas as neural populations using techniques like neural fields to model mean firing rates over continuous space.
- Phenomena like neural coding, plasticity, learning and their role in computational models of behaviors and cognition. It provides examples of modeling visual attention, decision making and more
This document outlines the course details for an "Intelligent Systems" course including 16 lectures and 8 practical works covering topics such as knowledge representation methods, expert systems, machine learning, natural language processing, intelligent robots, and the future of artificial intelligence. The course is taught by Professor Dr. Andrey V. Gavrilov and will provide students with basic concepts of different intelligent systems development methods and tools. Grades will be based on a midterm exam worth 50% and a final exam worth 50% of the total grade.
Functional brain parcellations break the brain into modules of regions with similar connectivity profiles. Parcellations exist over a range of scales from large scale networks to smaller specialized cortical areas. Larger parcels have higher homogeneity while stability is highest at the group level and more variable at the individual level. A number of metrics are used to evaluate parcellations including homogeneity, stability, and agreement with functional tasks. Individual parcellations provide more detail than group parcellations but estimating them jointly improves accuracy. Gradients provide an alternative to parcels by describing continuous connectivity patterns in the brain.
Cassandra audio-video sensor fusion for aggression detectionJoão Gabriel Lima
The document presents CASSANDRA, a system for detecting aggressive human behavior using audio-video sensor fusion. At the low level, audio and video streams are independently analyzed to extract intermediate descriptors like "scream" from audio and "articulation energy" from video. At the higher level, a Dynamic Bayesian Network fuses these descriptors and contextual knowledge to produce an aggregate aggression indication. The system was validated on scenarios performed by actors at a train station to ensure realistic noise conditions.
Analytical Review on the Correlation between Ai and NeuroscienceIOSR Journals
This document discusses the relationship between artificial intelligence and neuroscience. It describes how AI has benefited from studying neuroscience to better understand natural intelligence. Specifically, AI has used insights from neuroscience related to learning, perception, and reasoning by modeling neural mechanisms. The document also provides several examples of how AI and robotics have been influenced by neuroscience, including early robots designed to mimic animal behavior and more recent projects that apply insights about the brain to develop artificial neural networks or brain-inspired devices.
The document describes Knowledge Engineering from Experimental Design (KEfED), a semantic framework for representing biomedical experimental data and knowledge. KEfED models experiments using logical elements like activities, experimental objects, parameters, measurements, and branches to represent experimental designs, observations, and interpretations. It aims to introduce formalism to heterogeneous biomedical statements in a way that is intuitive for scientists. KEfED can be used as the basis for data repositories and integrated with tools like the Open Biomedical Ontology. Future work includes developing domain-specific reasoning models and semantic links to other frameworks.
The document discusses cognitive architectures, which are engineering approaches for modeling cognitive systems like humans. It notes that cognitive architectures aim to provide a unified set of mechanisms to explain various cognitive functions like language, problem solving, dreaming, goal-directed behavior, symbol usage, and learning. The document then reviews several specific cognitive architectures, including Soar, ACT-R, LIDA, and 4CAPS. It also discusses challenges in creating cognitive architectures that integrate symbolic and sub-symbolic approaches and can be implemented on neural hardware at large scales.
The document discusses how data mining techniques can be applied to analyze multichannel encephalographic recordings from brain activity by extracting patterns and relationships from large amounts of single-trial data in an unorganized format. It provides examples of how clustering and other algorithms have been used to summarize variability in regional brain responses and reveal couplings between ongoing activity and stimulus-evoked responses. The analysis of event-related dynamics using these methods has provided lessons about the dynamic processing performed by the brain.
Aspect oriented a candidate for neural networks and evolvable softwareLinchuan Wang
This is a paper written in 2004 and has been accepted by WASOD workshop 2004
https://people.cs.kuleuven.be/~dirk.craeynest/ada-belgium/events/04/040927-sefm-waosd.html
the meta data can be found in A Bibliography of Aspect-Oriented Software Development, Version 2.0
The document discusses neural computing and artificial neural networks. It provides an overview of several key topics, including the aims of investigating biological nervous systems and designing artificial systems that emulate biological principles. The document outlines several planned lectures on topics like the differences between natural and artificial intelligence, neurobiology, neural processing and signaling, stochasticity in neural codes, neural operators for vision, cognition and evolution, and artificial neural networks. It also lists some reference books and provides examples for exercises on neural computing concepts.
The document discusses fundamentals of neural networks and artificial intelligence. It provides an overview of topics covered in lectures 37 and 38, including the biological neuron model, artificial neuron model, neural network architectures, learning methods in neural networks, single-layer neural network systems, and applications of neural networks. It also includes details on the McCulloch-Pitts neuron model and the basic elements of an artificial neuron, such as weights, thresholds, and activation functions.
1) Artificial neural networks (ANNs) are processing systems inspired by biological neural networks, consisting of interconnected nodes that process information via algorithms or hardware components. ANNs can accurately model functions like visual processing in the retina.
2) ANNs are useful for problems like facial recognition that are difficult to solve with algorithms due to their ability to learn from examples in a way similar to the human brain.
3) ANNs have many applications, including pattern recognition, modeling complex relationships in large datasets, and real-time systems due to their parallel architecture.
Artificial neural networks are fundamental means for providing an attempt at modelling the information
processing capabilities of artificial nervous system which plays an important role in the field of cognitive
science. This paper focuses the features of artificial neural networks studied by reviewing the existing research
works, these features were then assessed and evaluated and comparative analysis. The study and literature
survey metrics such as functional capabilities of neurons, learning capabilities, style of computation, processing
elements, processing speed, connections, strength, information storage, information transmission,
communication media selection, signal transduction and fault tolerance were used as basis for comparison. A
major finding in this paper showed that artificial neural networks served as the platform for neuron computing
technology in the field of cognitive science.
Zhou Changsong presents a document discussing the brain as a complex dynamical network system subject to constraints of cost and function. It aims to reconcile irregular neuronal spiking with neural avalanches through a biologically plausible neuronal network model and statistical physics analysis. The key findings are that the model shows coexistence of irregular spiking, oscillations, and critical avalanches through a dynamical mechanism of Hopf bifurcation in the mean field model that explains critical neural avalanches corresponding to irregular spiking in the microscopic neuronal network model. This multiscale variability in brain activity reflects principles of cost-efficient neural representation and dynamics.
This document outlines the agenda and content for a seminar on cognitive neuroscience. It introduces cognitive neuroscience as the study of biological substrates underlying cognition, focusing on the neural substrate of mental processes. It discusses the basic unit of the brain (the neuron), cognition, neurocognition, areas of the brain like the hippocampus and prefrontal cortex. It also outlines methods used to study cognition like psychophysics, EEG, fMRI, and transcranial magnetic stimulation. The seminar aims to provide an understanding of how psychological/cognitive functions are produced by neural circuits in the brain.
Analyzing Complex Problem Solving by Dynamic Brain Networks.pdfNancy Ideker
This study analyzed complex problem solving using dynamic brain networks estimated from fMRI data collected while subjects played the Tower of London (TOL) game. A novel computational model was proposed that represented the brain network as an artificial neural network, with edge weights corresponding to relationships between anatomical regions. Dynamic brain networks were estimated from preprocessed fMRI signals using this neural network model. Network properties were analyzed to identify regions of interest and subgroups during planning and execution phases of TOL. Results found more hubs during planning and more strongly connected clusters, providing insights into the cognitive processes underlying complex problem solving.
The document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are modeled after biological neural networks and neurons. The key concepts covered include the basic structure and functioning of artificial neurons, different types of learning in ANNs, commonly used network architectures, and applications of ANNs. Examples of applications discussed are classification, recognition, assessment, forecasting and prediction. The document also notes how ANNs are used across various fields including computer science, statistics, engineering, cognitive science, neurophysiology, physics and biology.
Jeff Hawkins NAISys 2020: How the Brain Uses Reference Frames, Why AI Needs t...Numenta
Jeff Hawkins presents a talk on "How the Brain Uses Reference Frames to Model the World, Why AI Needs to do the Same." In this talk, he gives an overview of The Thousand Brains Theory and discusses how machine intelligence can benefit from working on the same principles as the neocortex.
This talk was first presented at the NAISys conference on November 10, 2020. You can find a re-recording of the talk here: https://youtu.be/mGSG7I9VKDU
Have We Missed Half of What the Neocortex Does? by Jeff Hawkins (12/15/2017)Numenta
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
Thoughtworks Ignite On Brains And ComputingAnthony Hsiao
1) The brain is a complex network of neurons and synapses that functions in a massively parallel and distributed manner.
2) Individual neurons perform simple computations by transmitting electrical signals via ion concentration gradients, while synapses vary in efficacy to perform "weighting" between neurons.
3) Neuromorphic engineering aims to understand and emulate the brain's principles in hardware to further research and potentially develop useful applications.
Complexity and Quantum Information ScienceMelanie Swan
This document discusses using quantum information science and quantum computing to model complex systems like the human brain. It proposes the "AdS/Brain Theory of Neural Signaling" which uses wavefunctions, tensor networks, and neural field theories at different scales from brain networks to molecules. Quantum computing could provide a new platform to model the brain across its nine orders of magnitude of complexity and help complete the human connectome by handling the large data and processing requirements. The AdS/Brain theory represents the first application of the AdS/CFT correspondence across multiple scales of the brain.
The Blue Brain Project aims to recreate the human brain at the cellular level through detailed computer simulation. It involves scanning actual brain tissue to collect data on neurons and synapses, which is used to build biologically realistic models. These models are then simulated on supercomputers. The goal is to better understand the brain and enable faster treatment development for brain diseases. Key aspects include using nanobots to non-invasively map entire brains, and eventually creating a simulated rat brain with over 20 million neurons by 2014 and a simulated human brain with over 80 billion neurons by 2023.
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs can learn complex patterns and make predictions based on large amounts of data. The document discusses the basic structure and functioning of ANNs, including their ability to learn through adjustment of synaptic weights between neurons. It also describes several common types of ANNs, focusing on perceptrons and multi-layer perceptrons.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
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!
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Sporns kavli2008
1. Kavli Institute 2008
Brain Networks for Efficient Computation
Olaf Sporns
Department of Psychological and Brain Sciences
Indiana University, Bloomington, IN 47405
http://www.indiana.edu/~cortex , osporns@indiana.edu
Outline
Brain Connectivity
Network Science Approaches
Brain Dynamics
Structure, Function, Information, Complexity
The Human Brain
Building a Map of the Human Brain
3. Brain Connectivity
The Brain is a Complex Network Organized on Multiple Scales
Microscopic: Single neurons and their synaptic connections.
Mesoscopic: Connections within and between microcolumns (minicolumns) or other
types of local cell assemblies
Macroscopic: Anatomically segregated brain regions and inter-regional pathways.
Sporns (2007) Brain Connectivity. www.scholarpedia.org
4. Brain Connectivity
Structure and Function of the Brain are Intricately Linked
Anatomical (Structural) Connectivity: Pattern of structural connections
between neurons, neuronal populations, or brain regions.
Functional Connectivity: Pattern of statistical dependencies (e.g. temporal
correlations) between distinct (often remote) neuronal elements.
Effective Connectivity: Network of causal effects, combination of functional
connectivity and structural model.
5. Brain Connectivity
Brain Networks Form a Small World
In highly evolved brains, structural brain
connectivity forms a small-world (high clustering,
short path length, low wiring cost, modules,
hubs) Sporns and Zwi (2004)
Highly clustered connection patterns at the large-
scale reflect functional relations between sets of
brain regions. These functional relations may be
a result of clustered connectivity.
Hilgetag et al., 2000
Kaiser and Hilgetag, 2006
Short path lengths indicate that all cortical areas
can be linked in very few processing steps.
7. Brain Dynamics
The Brain is Organized to Efficiently Extract and Coordinate Information
Two major organizational principles of cortex:
Segregation (anatomical/functional) clustering
Integration (anatomical/functional) path length
These principles are complementary and interdependent.
Two major challenges for information processing in the brain:
Rapid extraction of information (elimination of redundant dimensions,
efficient coding, maximum information transfer)
Coordination of distributed resources to create coherent states
Both challenges must be solved simultaneously, within a common neural
architecture.
8. Brain Dynamics
Segregation + Integration = Complexity
complexity:
coexistence of
complexity
segregation and
integration (local and
global structure)
C ( X ) = H ( X ) − ∑i H ( xi X − xi ).
Movie courtesy of Vincent, Raichle,
Snyder et al (Washington University)
small-world structural network spontaneous activity in a neural model spontaneous activity in a human brain
10. The Human Brain
The Brain is Always Active – Even “at Rest”
Slow fluctuations in fMRI signal at rest may reflect neuronal baseline activity.
Patterns of resting state BOLD signal change are consistent across subjects.
Spontaneous fluctuations reveal the existence of two distributed and anti-
correlated resting state networks.
Damoiseaux et al., PNAS (2006)
Fox et al.,
PNAS (2005)
fMRI resting state functional networks of wavelet coefficients show small-world
attributes. Small-world networks (in wavelet space) may be fractal across
multiple frequency ranges.
Achard et al., J Neurosci. (2006), Bassett et al., PNAS (2006)
11. The Human Brain
Connectivity + Dynamics = Endogenous Brain Activity
Connection matrix of macaque cortex
+
Dynamic equations describing the physiology of a
neural population
=
Spontaneous (endogenous)
neural dynamics
(chaoticity, metastability)
Honey, Breakspear, Kötter,
Sporns (2007) PNAS
12. The Human Brain
Neural Dynamics Unfold on Multiple Time Scales
Fast fluctuations in neural synchrony drive slower fluctuations in neural
population activity.
Functional brain networks reflect the small-world architecture of their
underlying structural substrate (structural/functional modularity).
simulated fMRI cross-correlations
13. The Human Brain
Functional Brain Networks form a Variable Repertoire
static pattern (anatomy)
variable pattern (functional
relations)
14. The Human Brain
The Connectome is Necessary for Understanding Brain Function
The human connectome represents a comprehensive structural description of the
network of elements and connections forming the human brain.
Proposed initial focus: thalamocortical system
Possible scales of the human connectome:
Microscale (neurons, synapses)
Macroscale (parcellated brain regions, voxels)
Mesoscale (columns, minicolumns)
Most feasible approach: macroscale (first draft), followed by “filling-in” at the
mesoscale.
Sporns, O., Tononi, G., and Kötter, R. (2005) The human connectome: A structural description of the
human brain. PLoS Comp. Biol.
15. The Human Brain
Fiber Pathways of the Cerebral Cortex can be Mapped with MRI
Diffusion Spectrum Imaging (DSI) and Computational Tractography
Hagmann, Cammoun, Gigandet, Meuli, Honey, Wedeen, Sporns (2008) PLoS Biology
18. The Human Brain
Human Brain Networks have a Structural Core
We analyzed weighted human brain connection matrices from 5 individual
subjects for a broad range of measures, including degrees/strength, small-
world attributes, assortativity, motifs, centrality, efficiency.
Network modularity was assessed with k-core decomposition, spectral
community detection and nodal participation indices.
All network analyses point to the existence of a structural core in human
cortex, centered on posterior medial cortex, and comprised of
cuneus/precuneus, superior parietal cortex and portions of cingulate cortex.
Brain regions within the structural core share high degree, strength and
betweenness centrality, and they constitute connector hubs that link all major
structural modules. The structural core contains brain regions that form the
posterior components of the human default network.
19. The Human Brain
A
scan 1 scan 2
subject A subject B subject C subject D subject E
B
subject A-E
C
20. The Human Brain
Human Brain Networks Have Numerous Hubs
connector hub distribution centrality distribution
22. The Human Brain
Structural and Functional Connections are Highly Correlated
A
B C
all subjects, PCUN + PC all subjects, all areas
r2 = 0.53 r2 = 0.62
C
25. The Human Brain
Computational Models Capture Large-Scale Human Brain Activity
Structural connections of the human brain shape
functional activations and dynamic states.
r = 0.85
rPC r = 0.76 r = 0.87
Honey et al. (PNAS, in revision)
rsFC rsFC
SC (empirical) (nonlinear model)
empirical nonlinear model
SC
rsFC
26. Summary
The Brain is a Complex Network Organized on Multiple Scales
Structure-function relationship, plasticity, turnover, redundancy
Brain Networks Form a Small World
Allows the brain to efficiently process information, promotes complexity
The Brain is Always Active – Even “at Rest”
Endogenous processes vs. exogenous perturbations, multiple time scales
Human Brain Networks have a Structural Core and Hubs
Core located in medial parietal cortex – a region central to self and consciousness
Hubs may serve as integrators of cortico-cortical signal traffic
Individual variations – clinical disturbances
Computational Models Capture Large-Scale Human Brain Activity
Possibility of a global brain simulator
Models as tools for exploring mechanistic substrates of human cognition
Funded by the JS McDonnell Foundation
27. Summary
The Brain is a Complex Network Organized on Multiple Scales
Cells to systems
Scalable architecture – common principles?
Structure and Function of the Brain are Intricately Linked
Structure shapes function shapes structure …
Reorganization and plasticity
Brain Networks Form a Small World
High clustering, short path length
Reflects volume and processing constraints
The Brain is Organized to Efficiently Extract and Coordinate Information
A dual challenge addressed in a common architecture
Small-world attributes map onto information processing requirements
Segregation + Integration = Complexity
Complexity is a mixture of randomness and regularity
Complexity emerges from structural small-world networks
28. Summary
The Brain is Always Active – Even “at Rest”
Endogenous processes vs. exogenous perturbations
Connectivity + Dynamics = Endogenous Brain Activity
Coupled dynamic models
Metastability, itinerancy
Neural Dynamics Unfold on Multiple Time Scales
Milliseconds to seconds
Fractal (self-similar) functional connectivity
Long-term averages more stable than short-term averages
Functional Brain Networks form a Variable Repertoire
Cognitive microstates?
Robustness versus flexibility
29. Summary
Fiber Pathways of the Cerebral Cortex can be Mapped with MRI
Noninvasive methodology
Rapid technological development
Increasingly refined maps
Human Brain Networks have a Structural Core and Hubs
Core located in medial parietal cortex – a region central to self and consciousness
Hubs may serve as integrators of cortico-cortical signal traffic
Human Brain Networks Show Individual Variations
Relation to cognitive/behavioral variation unknown
Network disturbances can help to diagnose brain disease
Structural and Functional Connections are Highly Correlated
Topological principles shared between anatomical and functional
networks
Endogenous brain activity – an expression of structural linkages
Computational Models Capture Large-Scale Human Brain Activity
Possibility of a global brain simulator
Models as tools for exploring mechanistic substrates of human cognition
Funded by the JS McDonnell Foundation
30. The Human Brain
1) High consistency of DSI tractography between hemispheres.
2) High consistency of DSI tractography in repeat scans.
r2 = 0.78
scan 1 scan 2
RH
r2 = 0.94
LH
3) Connection patterns are robust to degradation (simulation scanning and
tractography noise).
4) Comparison between macaque DSI tractography and connection patterns
derived by anatomical tract tracing shows significant overlap.
5) Comparison between structural and functional connections in human
brain shows significant correlation.
32. Macaque Brain Imaging
A Comparison of DSI
tractography data with
classical tract tracing
neuroanatomical data
B B
DSI Cocomac
fiber data
density (symmetrized)
known present
unknown
known absent