The document describes the virtual retina software and the biological retina it aims to model. It discusses the structure and function of the vertebrate retina, including its layers and cell types. It then explains the underlying computational model of the retina implemented in the virtual retina software. The goal is to simulate light transduction in the retina and output spike trains from ganglion cells in a biologically plausible manner while allowing for large-scale simulations. Statistics will later be performed on output from the virtual retina software and real retinal data to analyze correlations between ganglion cells.
This document describes a semester project involving rigid body sound synthesis. The project uses modal synthesis in the frequency domain to generate contact sounds based on forces from a rigid body simulation. It utilizes various math libraries like Armadillo for linear algebra and SVDLIBC for sparse matrix decompositions. The simulation models rigid bodies using finite elements, computes their vibration modes, and plays back the resulting sounds using SDL audio. Key aspects covered include matrix formation, material parameters, modal analysis calculations, and audio playback implementation.
Information extraction systems aspects and characteristicsGeorge Ang
This document provides a survey of information extraction systems and techniques. It discusses the main components and design approaches of information extraction, including manual and automatic pattern discovery. It also reviews several important prior information extraction systems and approaches to wrapper generation, including both supervised and unsupervised methods. The document serves to describe the state of the art in information extraction and provide an overview of the field.
Fuzzy and Neural Approaches in Engineering MATLABESCOM
This document provides an introduction to a MATLAB supplement for the book "Fuzzy and Neural Approaches in Engineering". It describes MATLAB as an educational software package for technical computing. The supplement contains MATLAB code examples that demonstrate concepts from the book, such as neural networks, fuzzy logic, and hybrid systems. It is intended to help readers gain a practical understanding of implementing soft computing techniques in MATLAB.
VSAN is a new storage solution from VMware that is fully integrated with vSphere. It automatically aggregates server disks in a cluster to create shared storage that can be rapidly provisioned from VMware vCenter during VM creation.
This document contains course notes for E&CE 327: Digital Systems Engineering. It provides an introduction to VHDL, including levels of abstraction, origins and history, syntax overview, processes, and simulation techniques like register-transfer level simulation. It also discusses hardware building blocks that can be modeled in VHDL and differences between synthesizable and non-synthesizable code. The document aims to explain key concepts for designing and simulating digital circuits using the VHDL hardware description language.
This document is an outline for an online book about computer, network, technical, physical, information and cryptographic security. It covers a wide range of security topics across 15 chapters, including security concepts, physical security, hardware security, distributed systems, identification and authentication, authorization and access control, secure system administration, logging, and abuse detection. The author intends it to be a comprehensive but incomplete reference work on security.
This document provides course notes on information visualization. It covers topics such as the history of information visualization, techniques for visualizing different data types like hierarchies, networks, and multidimensional data. It also discusses concepts in visual perception and lists many examples of visualization systems developed over the years for different data types. The document is intended as a reference for students taking a course on information visualization.
This document describes a semester project involving rigid body sound synthesis. The project uses modal synthesis in the frequency domain to generate contact sounds based on forces from a rigid body simulation. It utilizes various math libraries like Armadillo for linear algebra and SVDLIBC for sparse matrix decompositions. The simulation models rigid bodies using finite elements, computes their vibration modes, and plays back the resulting sounds using SDL audio. Key aspects covered include matrix formation, material parameters, modal analysis calculations, and audio playback implementation.
Information extraction systems aspects and characteristicsGeorge Ang
This document provides a survey of information extraction systems and techniques. It discusses the main components and design approaches of information extraction, including manual and automatic pattern discovery. It also reviews several important prior information extraction systems and approaches to wrapper generation, including both supervised and unsupervised methods. The document serves to describe the state of the art in information extraction and provide an overview of the field.
Fuzzy and Neural Approaches in Engineering MATLABESCOM
This document provides an introduction to a MATLAB supplement for the book "Fuzzy and Neural Approaches in Engineering". It describes MATLAB as an educational software package for technical computing. The supplement contains MATLAB code examples that demonstrate concepts from the book, such as neural networks, fuzzy logic, and hybrid systems. It is intended to help readers gain a practical understanding of implementing soft computing techniques in MATLAB.
VSAN is a new storage solution from VMware that is fully integrated with vSphere. It automatically aggregates server disks in a cluster to create shared storage that can be rapidly provisioned from VMware vCenter during VM creation.
This document contains course notes for E&CE 327: Digital Systems Engineering. It provides an introduction to VHDL, including levels of abstraction, origins and history, syntax overview, processes, and simulation techniques like register-transfer level simulation. It also discusses hardware building blocks that can be modeled in VHDL and differences between synthesizable and non-synthesizable code. The document aims to explain key concepts for designing and simulating digital circuits using the VHDL hardware description language.
This document is an outline for an online book about computer, network, technical, physical, information and cryptographic security. It covers a wide range of security topics across 15 chapters, including security concepts, physical security, hardware security, distributed systems, identification and authentication, authorization and access control, secure system administration, logging, and abuse detection. The author intends it to be a comprehensive but incomplete reference work on security.
This document provides course notes on information visualization. It covers topics such as the history of information visualization, techniques for visualizing different data types like hierarchies, networks, and multidimensional data. It also discusses concepts in visual perception and lists many examples of visualization systems developed over the years for different data types. The document is intended as a reference for students taking a course on information visualization.
A buffer overflow study attacks and defenses (2002)Aiim Charinthip
This document provides an overview of buffer overflow attacks and defenses. It discusses stack and heap overflows, and how programs can be exploited by overwriting memory buffers. It then summarizes various protection solutions, including Libsafe and the Grsecurity kernel patch, which make the stack and heap non-executable to prevent execution of injected code. The document serves as an introduction to buffer overflows and techniques for mitigating these vulnerabilities.
This document is the master's thesis of Tamás Martinec titled "Real-Time Non-Photorealistic Shadow Rendering". The thesis discusses non-photorealistic rendering (NPR) techniques, real-time shadow rendering algorithms, and presents an example of combining hatching-based NPR with shadow mapping to generate stylized shadows in real-time. The thesis is divided into chapters covering NPR techniques and styles, real-time shadow rendering methods, graphics hardware and shaders, and a demonstration implementing hatching and shadowed hatching shaders.
This document provides a project report for the MicazXpl project. It describes the hardware and software used, including MICAz sensor motes, TinyOS operating system, and Linux. It outlines the individual roles of group members and their plan to create versions 0.1 and 0.2 of the MicazXpl software. It also includes tutorials on installing TinyOS and compiling applications for the sensor motes. Later sections summarize technical papers on wireless sensor network routing protocols, topologies and the Collection Tree Protocol.
This document is a manual for JIU (Java Imaging Utilities), an open source Java library for image processing. It introduces JIU, describes its image data types and classes for loading, saving and manipulating images. It provides overviews of operations, codecs, color processing and the GUI capabilities of JIU. It also gives guidance for developers on writing custom image operations and codecs to extend JIU's functionality.
This document provides a detailed overview of the internals of the GNU debugger (GDB), including its overall structure, key algorithms, user interface, symbol handling, and other components. It describes GDB's symbol side for accessing program symbols and debugging information, target side for communicating with the debugged process, and configurations for different operating systems and architectures. The document also examines GDB's algorithms for tasks like breakpoint handling, single stepping, watchpoints, and unwinds stack frames to locate values. It provides details on GDB's UI, use of libgdb for CLI support, handling of values and types, and support for various object file and debugging formats.
This document is a dissertation submitted for the degree of Master of Technology. It describes work done to implement real-time video and image processing algorithms for object tracking on the Texas Instruments TMS320DM6437 DaVinci digital media processor. Specifically, algorithms for single object tracking and multiple object tracking were developed and tested on the DaVinci processor. The performance of the algorithms was faster and more accurate compared to implementing the same algorithms on a PC using Matlab. Debugging and profiling results showed that the DaVinci processor provided at least a ten-times speedup for real-time object tracking compared to a PC implementation.
This document provides a technical overview of the Symbol blockchain protocol. It describes the key components of the Symbol system including transactions, blocks, accounts, addresses, cryptography, trees, networking, consensus and more. The goal in developing Symbol was to create a trustless, high-performance, layered blockchain architecture that improves upon the original NEM protocol.
This document is the CUDA API Reference Manual for version 5.0. It provides documentation on the CUDA API, including sections that describe the synchronization behavior of functions like memcpy and kernel launches. It also includes indexes of deprecated functions, modules, and data structures. The majority of the document consists of in-depth documentation of the functions within each CUDA API module, including parameters, return values, and usage examples.
This document provides lecture notes on hybrid systems. It begins with an overview of dynamical systems and examples of continuous, discrete, and hybrid systems. It then discusses modeling hybrid systems as hybrid automata and the concept of executions. The notes cover topics such as the existence and properties of solutions to hybrid systems, modeling and analysis techniques including deductive methods, model checking and timed automata, and reachability analysis using viability theory. The goal is to introduce fundamental concepts for investigating properties of hybrid systems such as existence of solutions, reachability, and decidability.
This document describes the implementation of an Android kernel rootkit for unrooted stock Android images. It discusses the structure of the Linux kernel, existing kernel rootkits and related work. The thesis then presents a concept for an Android kernel rootkit and infection method. It details the implementation process, including building a customized kernel, developing the rootkit module and creating an exploit tool and infected app. Problems encountered during implementation are also discussed. The work evaluates the practical usage of the rootkit, potential defenses and areas for future improvement.
This document summarizes a master's thesis that implements a reliable overlay multicast protocol on wireless sensor nodes. The thesis first discusses related work on wireless sensor networks, communication schemes, hardware, and the Contiki operating system. It then presents the design of the Sensor Nodes Overlay Multicast Communication (SNOMC) protocol, including node roles, message types, design models, data structures, and the SNOMC algorithm. The implementation of SNOMC in Contiki is described, along with implementations of UDP and TCP for comparison. An evaluation analyzes the performance of transmitting small and large messages using SNOMC.
This document is a draft of a textbook titled "Applied Calculus" written by Karl Heinz Dovermann, a professor of mathematics at the University of Hawaii. It is dedicated to his wife and sons. The textbook covers topics in calculus including definitions of derivatives, integrals, and applications of calculus through 12 chapters with sections on background concepts, derivatives, applications of derivatives, integration, and prerequisites from precalculus.
The document contains a list of figures related to computer hardware components and their usage. There are figures showing different types of resistors, transistors, meters, motherboards, hard disks, disk drives, and other computer parts. Steps for partitioning hard disks using different Windows operating systems and maintaining disk drives are also illustrated.
Metatron Technology Consulting 's MySQL to PostgreSQL ...webhostingguy
This document provides a guide for migrating a database from MySQL to PostgreSQL. It discusses key differences between the two databases, including features available in one but not the other. It also provides references for porting SQL functions and tools to help with the migration process. Common problems that may occur during migration like error messages are also addressed.
This document provides reference documentation for Pylons version 0.9.7. It covers getting started topics like requirements, installation and creating a Pylons project. It also covers key Pylons concepts like WSGI applications and middleware. The document is divided into several sections that cover controllers, views, models, configuration, logging, helpers, forms and internationalization.
This document is the thesis of Arnaud Jean-Baptiste presented at the Universite des Sciences et Technologies de Lille for the degree of Doctor of Philosophy in computer science. The thesis proposes a model of handles to control references in dynamically typed languages by enforcing behavioral properties like read-only at the reference level. It presents three experiments with handles - enforcing read-only, supporting various behavioral properties, and adding state to handles. The thesis also discusses implementation details and evaluates the performance overhead of the handle approach.
This document provides an introduction and overview of a dissertation project that aims to investigate how data encryption affects data recovery. Specifically, the project will conduct an experiment to recover identical files from both an encrypted and unencrypted hard drive. This will determine if encryption makes some file types unrecoverable. The objectives are to evaluate recovery and encryption software, test different types of data loss, perform the recovery experiment, document results, and determine the effect of encryption on recoverability.
Six Myths and Paradoxes of Garbage Collection Holly Cummins
MSc dissertation.
Many myths and paradoxes surround garbage collection. The first myth is that garbage collection is only suitable for the incompetent, unskilled, or lazy. In fact garbage collection offers many architec- tural and software engineering advantages, even to the skilled developer. The second myth is that garbage collection is all about about collecting garbage. Garbage collectors also include an allocation component, which, along with their powers of object rearrangement, can make a significant difference to application performance. Thirdly, criticisms of garbage collection often focus on the pause times, and responses to these criticisms often focus exclusively on reducing pause times, in the mistaken belief that small pause times guarantee good application response times. Pause times are also often used as a metric of general application performance, and an increase in pause times is taken as an indicator of worsened performance, when in fact the opposite the opposite is often true. Paradoxically, even the total amount of time spent paused for garbage collection is not a good predictor of the impact of garbage collection on application performance. Finally, the sixth myth is that garbage collection has a disastrous performance impact. While garbage collection can hurt application performance, it can also help application performance to the point where it exceeds the performance with manual memory management.
This document provides an overview and printing history of the book "Lessons In Electric Circuits, Volume III – Semiconductors" by Tony R. Kuphaldt. It discusses the topics that will be covered in the book, including solid-state device theory, diodes and rectifiers, bipolar junction transistors, and more. The printing history section notes that the book was originally published in 2000 and has since had four subsequent editions, with new sections and corrections added over time. The latest edition discussed is the fifth edition from July 2007.
A buffer overflow study attacks and defenses (2002)Aiim Charinthip
This document provides an overview of buffer overflow attacks and defenses. It discusses stack and heap overflows, and how programs can be exploited by overwriting memory buffers. It then summarizes various protection solutions, including Libsafe and the Grsecurity kernel patch, which make the stack and heap non-executable to prevent execution of injected code. The document serves as an introduction to buffer overflows and techniques for mitigating these vulnerabilities.
This document is the master's thesis of Tamás Martinec titled "Real-Time Non-Photorealistic Shadow Rendering". The thesis discusses non-photorealistic rendering (NPR) techniques, real-time shadow rendering algorithms, and presents an example of combining hatching-based NPR with shadow mapping to generate stylized shadows in real-time. The thesis is divided into chapters covering NPR techniques and styles, real-time shadow rendering methods, graphics hardware and shaders, and a demonstration implementing hatching and shadowed hatching shaders.
This document provides a project report for the MicazXpl project. It describes the hardware and software used, including MICAz sensor motes, TinyOS operating system, and Linux. It outlines the individual roles of group members and their plan to create versions 0.1 and 0.2 of the MicazXpl software. It also includes tutorials on installing TinyOS and compiling applications for the sensor motes. Later sections summarize technical papers on wireless sensor network routing protocols, topologies and the Collection Tree Protocol.
This document is a manual for JIU (Java Imaging Utilities), an open source Java library for image processing. It introduces JIU, describes its image data types and classes for loading, saving and manipulating images. It provides overviews of operations, codecs, color processing and the GUI capabilities of JIU. It also gives guidance for developers on writing custom image operations and codecs to extend JIU's functionality.
This document provides a detailed overview of the internals of the GNU debugger (GDB), including its overall structure, key algorithms, user interface, symbol handling, and other components. It describes GDB's symbol side for accessing program symbols and debugging information, target side for communicating with the debugged process, and configurations for different operating systems and architectures. The document also examines GDB's algorithms for tasks like breakpoint handling, single stepping, watchpoints, and unwinds stack frames to locate values. It provides details on GDB's UI, use of libgdb for CLI support, handling of values and types, and support for various object file and debugging formats.
This document is a dissertation submitted for the degree of Master of Technology. It describes work done to implement real-time video and image processing algorithms for object tracking on the Texas Instruments TMS320DM6437 DaVinci digital media processor. Specifically, algorithms for single object tracking and multiple object tracking were developed and tested on the DaVinci processor. The performance of the algorithms was faster and more accurate compared to implementing the same algorithms on a PC using Matlab. Debugging and profiling results showed that the DaVinci processor provided at least a ten-times speedup for real-time object tracking compared to a PC implementation.
This document provides a technical overview of the Symbol blockchain protocol. It describes the key components of the Symbol system including transactions, blocks, accounts, addresses, cryptography, trees, networking, consensus and more. The goal in developing Symbol was to create a trustless, high-performance, layered blockchain architecture that improves upon the original NEM protocol.
This document is the CUDA API Reference Manual for version 5.0. It provides documentation on the CUDA API, including sections that describe the synchronization behavior of functions like memcpy and kernel launches. It also includes indexes of deprecated functions, modules, and data structures. The majority of the document consists of in-depth documentation of the functions within each CUDA API module, including parameters, return values, and usage examples.
This document provides lecture notes on hybrid systems. It begins with an overview of dynamical systems and examples of continuous, discrete, and hybrid systems. It then discusses modeling hybrid systems as hybrid automata and the concept of executions. The notes cover topics such as the existence and properties of solutions to hybrid systems, modeling and analysis techniques including deductive methods, model checking and timed automata, and reachability analysis using viability theory. The goal is to introduce fundamental concepts for investigating properties of hybrid systems such as existence of solutions, reachability, and decidability.
This document describes the implementation of an Android kernel rootkit for unrooted stock Android images. It discusses the structure of the Linux kernel, existing kernel rootkits and related work. The thesis then presents a concept for an Android kernel rootkit and infection method. It details the implementation process, including building a customized kernel, developing the rootkit module and creating an exploit tool and infected app. Problems encountered during implementation are also discussed. The work evaluates the practical usage of the rootkit, potential defenses and areas for future improvement.
This document summarizes a master's thesis that implements a reliable overlay multicast protocol on wireless sensor nodes. The thesis first discusses related work on wireless sensor networks, communication schemes, hardware, and the Contiki operating system. It then presents the design of the Sensor Nodes Overlay Multicast Communication (SNOMC) protocol, including node roles, message types, design models, data structures, and the SNOMC algorithm. The implementation of SNOMC in Contiki is described, along with implementations of UDP and TCP for comparison. An evaluation analyzes the performance of transmitting small and large messages using SNOMC.
This document is a draft of a textbook titled "Applied Calculus" written by Karl Heinz Dovermann, a professor of mathematics at the University of Hawaii. It is dedicated to his wife and sons. The textbook covers topics in calculus including definitions of derivatives, integrals, and applications of calculus through 12 chapters with sections on background concepts, derivatives, applications of derivatives, integration, and prerequisites from precalculus.
The document contains a list of figures related to computer hardware components and their usage. There are figures showing different types of resistors, transistors, meters, motherboards, hard disks, disk drives, and other computer parts. Steps for partitioning hard disks using different Windows operating systems and maintaining disk drives are also illustrated.
Metatron Technology Consulting 's MySQL to PostgreSQL ...webhostingguy
This document provides a guide for migrating a database from MySQL to PostgreSQL. It discusses key differences between the two databases, including features available in one but not the other. It also provides references for porting SQL functions and tools to help with the migration process. Common problems that may occur during migration like error messages are also addressed.
This document provides reference documentation for Pylons version 0.9.7. It covers getting started topics like requirements, installation and creating a Pylons project. It also covers key Pylons concepts like WSGI applications and middleware. The document is divided into several sections that cover controllers, views, models, configuration, logging, helpers, forms and internationalization.
This document is the thesis of Arnaud Jean-Baptiste presented at the Universite des Sciences et Technologies de Lille for the degree of Doctor of Philosophy in computer science. The thesis proposes a model of handles to control references in dynamically typed languages by enforcing behavioral properties like read-only at the reference level. It presents three experiments with handles - enforcing read-only, supporting various behavioral properties, and adding state to handles. The thesis also discusses implementation details and evaluates the performance overhead of the handle approach.
This document provides an introduction and overview of a dissertation project that aims to investigate how data encryption affects data recovery. Specifically, the project will conduct an experiment to recover identical files from both an encrypted and unencrypted hard drive. This will determine if encryption makes some file types unrecoverable. The objectives are to evaluate recovery and encryption software, test different types of data loss, perform the recovery experiment, document results, and determine the effect of encryption on recoverability.
Six Myths and Paradoxes of Garbage Collection Holly Cummins
MSc dissertation.
Many myths and paradoxes surround garbage collection. The first myth is that garbage collection is only suitable for the incompetent, unskilled, or lazy. In fact garbage collection offers many architec- tural and software engineering advantages, even to the skilled developer. The second myth is that garbage collection is all about about collecting garbage. Garbage collectors also include an allocation component, which, along with their powers of object rearrangement, can make a significant difference to application performance. Thirdly, criticisms of garbage collection often focus on the pause times, and responses to these criticisms often focus exclusively on reducing pause times, in the mistaken belief that small pause times guarantee good application response times. Pause times are also often used as a metric of general application performance, and an increase in pause times is taken as an indicator of worsened performance, when in fact the opposite the opposite is often true. Paradoxically, even the total amount of time spent paused for garbage collection is not a good predictor of the impact of garbage collection on application performance. Finally, the sixth myth is that garbage collection has a disastrous performance impact. While garbage collection can hurt application performance, it can also help application performance to the point where it exceeds the performance with manual memory management.
This document provides an overview and printing history of the book "Lessons In Electric Circuits, Volume III – Semiconductors" by Tony R. Kuphaldt. It discusses the topics that will be covered in the book, including solid-state device theory, diodes and rectifiers, bipolar junction transistors, and more. The printing history section notes that the book was originally published in 2000 and has since had four subsequent editions, with new sections and corrections added over time. The latest edition discussed is the fifth edition from July 2007.
A Florida couple was arrested for faking documents to claim ownership of a home worth over $1 million that was in foreclosure. A lawyer notified authorities after discovering forged documents transferring ownership of the home to Justin Dean. An investigation revealed that Justin Dean and his wife Jenna Dean had used fake documents in an attempt to take the home, which was already owned by another couple according to the bank. The couple was charged with grand burglary, forgery, and other offenses.
This document provides guidelines for writing a research paper, including:
- The aims of a research paper are to demonstrate independent and critical analysis of sources and apply knowledge to specific cases and questions.
- Research papers will be assessed based on logical reasoning, structure, use of sources, language, and meeting deadlines. Plagiarism is forbidden.
- The paper should have an introduction, body, and conclusion. The body addresses the research question in chapters with sections.
- Students must find their own sources, get a supervisor's approval, and present their research in a 15 minute PowerPoint presentation with time for questions.
- Technical specifications cover paper, printing, formatting, citations, and required parts like a
Advanced Medical Imaging requires all employees to complete training on HIPAA privacy and security policies and procedures to protect patients' protected health information. The training covers HIPAA requirements to protect privacy, limit access to and use of PHI, and extend patient rights over their information. It also addresses identifying PHI, handling paper records, securing electronic devices and data, safe email practices, and reporting any potential privacy breaches or security incidents. Completing the training helps ensure everyone understands their role in maintaining privacy and security according to HIPAA and the organization's policies.
Blue Cross Blue Shield of Delaware (BCBSD) offers 10 reasons why their health insurance plan is a good choice: 1) They are the largest and most trusted provider in Delaware with 75 years of experience. 2) They have the largest provider network in the state. 3) Their BlueCard network provides coverage across the U.S. 4) They offer innovative wellness programs and care management for conditions like asthma and diabetes. 5) They receive high ratings and accreditation for quality healthcare.
The document discusses differences between poor and rich nations. It states that a nation's wealth is not determined by its age, natural resources, or intellectual abilities, but rather by the attitudes and principles embraced by its people. These include ethics, integrity, responsibility, respect for laws, work ethic, savings, productivity, and punctuality. While a minority follow these in poor countries, they are widely adopted in rich countries. The document argues that nations are poor due to a lack of these attitudes rather than any deficiencies.
El documento lista cinco matadores - Arturo Macias, Fernando Roca Rey, Cristobal Pardo, Eduardo Gallo y Sanchez Vara - que participarán en una corrida de toros en la Plaza de Toros El "Vizcaíno".
This document describes the creation of 3D objects and environments for use in a virtual reality study investigating alterations in the experience of reality. 3D models of common everyday objects like books, phones, and jackets were created in both normal and "bizarre" variants to elicit typical or altered experiences. The objects were modelled, textured, and animated as needed in Blender then imported and rendered in Unreal Engine. A data tracking system was also developed to collect information on participants' interactions with the objects during experiments. The goal is to use the virtual environment to study how reality is experienced by participants under different conditions.
This document describes Kerry Steven Hall's dissertation research on using air-coupled ultrasonic tomography to image concrete elements. The research aims to integrate recent developments in air-coupled ultrasonic measurements with advanced tomography technology to apply them to concrete structures. Finite element models are developed and used to simulate measurement configurations and optimize data collection procedures. Non-contact and semi-contact ultrasonic sensors are developed and tested on concrete cylinder and block specimens. Tomographic reconstructions with error calculations are performed to image inclusions and defects within the concrete. Issues related to applying the techniques to full-scale concrete structures are also discussed.
This document is a doctoral thesis submitted by Manuela P. Feilner to the Department of Microtechnology at EPFL in 2002. The thesis proposes using statistical wavelet analysis methods for functional magnetic resonance imaging (fMRI) of the brain. Chapter 1 introduces the motivation and contributions of the thesis. Chapter 2 provides background on fMRI and image acquisition techniques. Subsequent chapters develop statistical analysis methods using wavelet transforms and apply them to analyze real fMRI data to identify brain activation patterns. The goal is to improve detection of activated regions compared to existing real-space methods.
There will be more change in the next 10 years than there has been in the previous 100. This paper describes these expected foundational shifts and explains how we can manage them to our advantage.
In their latest discussion presentation "Winning the Game", Geoff Hollingworth, Ericsson North America Evangelist, in collaboration with Jason Hoffman, founder and CTO of Joyent, discuss what these changes will mean for devices, the cloud and the network.
This interactive presentation is supported by 8 videos. It describes the foundational changes that will occur across industries and networks, and attempts to explain how we can manage them to our advantage. The target audience of this paper is those who are involved in planning, building and profitably operating digital networks.
This document describes a Matlab implementation of neural networks. It begins with an introduction to neural networks and associative memory, explaining how neural networks can be used to create associative memories that recall stored information based on partial cues. It then discusses implementing associative memory using neural networks and provides Matlab functions for storing and recalling information. The document goes on to describe perceptrons, multi-layer networks, and backpropagation networks. It concludes by presenting three applications of backpropagation networks: solving the XOR problem, curve fitting, and time series forecasting.
This document describes the Embedded Filesystem Library (EFSL), which provides filesystem functionality for embedded systems. It allows accessing files stored on various storage devices from different microcontroller families. The document explains how to set up and compile EFSL for Linux, AVR, DSP and ARM microcontrollers. It provides examples and describes the EFSL API functions for opening, reading, writing and managing files and directories. It also gives notes for EFSL configuration and developing for new hardware targets.
Trade-off between recognition an reconstruction: Application of Robotics Visi...stainvai
Autonomous and ecient action of robots requires a robust robot vision system that can
cope with variable light and view conditions. These include partial occlusion, blur, and
mainly a large scale dierence of object size due to variable distance to the objects. This
change in scale leads to reduced resolution for objects seen from a distance. One of the
most important tasks for the robot's visual system is object recognition. This task is also
aected by orientation and background changes. These real-world conditions require a
development of specic object recognition methods.
This work is devoted to robotic object recognition. We develop recognition methods
based on training that includes incorporation of prior knowledge about the problem.
The prior knowledge is incorporated via learning constraints during training (parameter
estimation). A signicant part of the work is devoted to the study of reconstruction
constraints. In general, there is a tradeo between the prior-knowledge constraints and
the constraints emerging from the classication or regression task at hand. In order to
avoid the additional estimation of the optimal tradeo between these two constraints, we
consider this tradeo as a hyper parameter (under Bayesian framework) and integrate
over a certain (discrete) distribution. We also study various constraints resulting from
information theory considerations.
Experimental results on two face data-sets are presented. Signicant improvement in
face recognition is achieved for various image degradations such as, various forms of image
blur, partial occlusion, and noise. Additional improvement in recognition performance is
achieved when preprocessing the degraded images via state of the art image restoration
techniques.
Stochastic Processes and Simulations – A Machine Learning Perspectivee2wi67sy4816pahn
Written for machine learning practitioners, software engineers and other analytic professionals interested in expanding their toolset and mastering the art. Discover state-of-the-art techniques explained in simple English, applicable to many modern problems, especially related to spatial processes and pattern recognition. This textbook includes numerous visualization techniques (for instance, data animations using video libraries in R), a true test of independence, simple illustration of dual confidence regions (more intuitive than the classic version), minimum contrast estimation (a simple generic estimation technique encompassing maximum likelihood), model fitting techniques, and much more. The scope of the material extends far beyond stochastic processes.
This document provides an overview and tutorial on the Python programming language. It introduces Python's main features like lists, dictionaries, functions, object-oriented programming and modules. The tutorial includes example code and explanations of Python concepts like data types, scope, exceptions, classes and inheritance. It also covers debugging tools and accessing Python's online documentation. The goal is to quickly teach the essentials of Python in a painless manner.
Im-ception - An exploration into facial PAD through the use of fine tuning de...Cooper Wakefield
This document is a thesis submitted by Cooper Wakefield to the University of Queensland for the degree of Bachelor of Engineering. The thesis proposes developing a presentation attack detection (PAD) system through fine tuning a deep convolutional neural network. It aims to leverage pre-trained networks and fine tune the upper layers to differentiate between real and fake facial images with a high degree of accuracy. The thesis outlines the problem of presentation attacks on facial recognition systems, reviews prior approaches to PAD, and describes the proposed solution of using transfer learning on a CNN to classify images as real or fake.
The document is the Flask documentation, which provides information on using the Flask web framework in Python. It covers topics like installation, basic usage, routing, templates, testing, configuration, debugging errors, and signals. The documentation contains tutorials, guides, and reference material to help developers build web applications with Flask.
This document is a user's guide for WIEN2k, an augmented plane wave plus local orbitals program for calculating crystal properties. It describes WIEN2k, a computational software package that uses density functional theory to calculate the electronic structure of crystals and molecules. The guide provides an overview of the basic concepts behind the program, including the augmented plane wave method and density functional theory. It also gives instructions for getting started with the program, running calculations, and calculating various material properties.
This document is a doctrine manual that provides instructions on how to get started with doctrine, including requirements, installation methods, starting new projects, creating tables, generating models, auto loading models, using the command line interface, and a tutorial on creating a basic project with users. It also covers connecting to databases, managing connections, basic schema mapping including table and class naming, table and column options, and data types.
This dissertation examines methods for measuring the spatial arrangement of neurons and glial cells in the mammalian cortex. The document begins with an introduction discussing the importance of studying brain cell arrangement and the need for quantitative tools. It then provides a literature review on brain anatomy, spatial arrangement mechanisms, and existing measurement theories. The experimental method section describes a three-part process: 1) digitizing tissue samples at high resolution, 2) developing algorithms to recognize cells in the digitized images, and 3) analyzing the data using metrics like cell counts, density maps, and cross-correlations. Results are presented on tissue samples from the macaque monkey and rat brain, focusing on specific cortical areas. Future studies are proposed to integrate the data, analyze
This document provides an overview and summary of a thesis on visualizing uncertainty in fiber tracking based on diffusion tensor imaging (DTI). The thesis addresses challenges with visualizing uncertainty throughout the DTI and fiber tracking pipeline, including image acquisition, diffusion modeling, fiber tracking, and visualization. It proposes and evaluates various techniques for visualizing different types of uncertainty, such as value uncertainty, location uncertainty, and parameter uncertainty. The visualization techniques are applied to fiber tracking results to aid in neurosurgical planning and other medical applications.
This document specifies the Linked Media Layer architecture and describes its key components. The architecture includes a repository layer for media storage and metadata, an integration layer, and a service layer. It also describes modules for unstructured search using Apache Nutch/Solr, media collection from social networks, searching media resources with latent semantic indexing, and participation in the MediaEval 2013 benchmarking initiative for video search and hyperlinking tasks.
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Similar to Toward a realistic retina simulator (20)
Analysis of large scale spiking networks dynamics with spatio-temporal constr...Hassan Nasser
Recent experimental advances have made it possible to record up to several hundreds of neurons simultaneously in the cortex or in the retina. Analysing such data requires mathematical and numerical methods to describe the spatio-temporal correlations in population activity. This can be done thanks to Maximum Entropy method. Here, a crucial parameter is the product NxR where N is the number of neurons and R the memory depth of correlations (how far in the past does the spike activity affects the current state). Standard statistical mechanics methods are limited to spatial correlation structure with
R = 1 (e.g. Ising model) whereas methods based on transfer matrices, allowing the analysis of spatio-temporal correlations, are limited to NR = 20.
In the first part of the thesis we propose a modified version of the transfer matrix method, based on the parallel version of the Montecarlo algorithm, allowing us to go to NR = 100.
In the second part we present EnaS, a C++ library with a Graphical User Interface developed for neuroscientists. EnaS offers highly interactive tools that allow users to manage data, perform empirical statistics, modeling and visualizing results.
Finally, in a third part, we test our method on synthetic and real data sets. Real data set correspond to retina data provided by neuroscientists partners. Our non extensive analysis shows the advantages of considering spatio-temporal correlations for the analysis of retina spike trains, but it also outlines the limits of Maximum Entropy methods.
For more information about the software that I co-developed with my colleagues, please visit this page:
https://enas.inria.fr/
For more information about the publications, please visit this page:
https://scholar.google.fr/citations?user=L97ZODwAAAAJ
For the thesis, please visit this link:
https://www.theses.fr/178166669
Poster Toward a realistic retinal simulatorHassan Nasser
The document discusses improving the statistical realism of a retina simulator called VirtualRetina by implementing additional retinal circuitry features. While VirtualRetina can accurately model individual retinal ganglion cell responses, it does not capture the synchronization and correlations seen in real retinal data. Implementing gap junction connections between retinal ganglion cells and modeling feedback from amacrine cells could help VirtualRetina better match real data statistics. The goal is to produce a statistically plausible retinal output that can serve as realistic input for models of the visual cortex.
This document summarizes a Monte Carlo-based method for analyzing large neural networks using Gibbs distributions. The method allows reproducing the statistics of spike train data by finding the parameters of a Gibbs potential that minimizes the KL divergence between the empirical and theoretical distributions. For large networks, a classical Monte Carlo approach does not converge, so the authors developed a new approach using a "particular potential" that allows analytical computation of observable averages. The method was tested on small networks to compute errors, but the goal is to apply it to real neural data to estimate statistics and compute optimal parameters for large networks with memory.
The document summarizes retinal anatomy and modeling. It discusses the layers of the retina including photoreceptors, bipolar cells, horizontal cells, amacrine cells, and ganglion cells. It describes the connections between these cells, including chemical synapses and electrical gap junctions. The goal of the author's PhD is to improve an existing retinal model to produce synchronized spike outputs from ganglion cells by adding connections between ganglion cells and between ganglion and amacrine cells.
Mesure locale de la vitesse de l’onde de pression par l’IRM dynamique.
Toward a realistic retina simulator
1. University of Nice Sophia-antipolis
INRIA - I3S
Midterm Report
Parameter Tuning in virtual retina
using synaptic plasticity
Author:
Hassan Nasser
Supervisor:
Dr. Bruno Cessac tutor:
Dr. Thierry Vieville Dr. Marc Antonini
Dr. Pierre Kornprobst
August 13, 2010
3. List of Figures
1.1 The vertebrate Eye . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 The visual pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Up: the action potential. Down: The corresponding spike train . . . . . 6
1.4 The different cell types in the vertebrate retina . . . . . . . . . . . . . 7
1.5 The different layers in the retina . . . . . . . . . . . . . . . . . . . . . . 8
1.6 The different layers in the retina . . . . . . . . . . . . . . . . . . . . . . 8
1.7 The different stages of the retina model . . . . . . . . . . . . . . . . . . 10
1.8 The VirtualRetina software logo . . . . . . . . . . . . . . . . . . . . . . 11
1.9 An example about a gray scale input image . . . . . . . . . . . . . . . 11
1.10 The structure of the .spk file . . . . . . . . . . . . . . . . . . . . . . . . 12
1.11 the log scheme distribution of the ganglion cells . . . . . . . . . . . . . 12
1.12 The beginning of the retina.xml file . . . . . . . . . . . . . . . . . . . . 13
1.13 The Signal at each layer of the retina model . . . . . . . . . . . . . . . 14
1.14 Spike train for different cell types . . . . . . . . . . . . . . . . . . . . . 15
1.15 The spike train in a large scale simulation . . . . . . . . . . . . . . . . 16
2.1 A typical spike train (produced with VirtualRetina) . . . . . . . . . . . 18
2.2 The technical details of a raster plot . . . . . . . . . . . . . . . . . . . 18
2.3 The Enas Logo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4 The correlation between non-correlated data . . . . . . . . . . . . . . . 23
2.5 Evolving of the correlation for different probability distributions (Bernouilli
distribution) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.6 Evolving of the correlation for different time coincidence. The delay is
the term we use to express that the two neurons fire between a τ time
interval. We studied the correlation for different daly values, going from
1 to 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.7 The MEA chip for ganglion recordings . . . . . . . . . . . . . . . . . . 25
2.8 The evoked potential recording . . . . . . . . . . . . . . . . . . . . . . 25
2.9 Correlation between ganglion cells (at rest) in real acquisition . . . . . 26
2.10 Correlation between ganglion cells (evoked potential) in real acquisition 27
2.11 Correlation between X-ON ganglion cells in VirtualRetina . . . . . . . 28
2.12 Correlation between Y-ON ganglion cells in VirtualRetina . . . . . . . 28
2.13 Correlation between X-OFF ganglion cells in VirtualRetina . . . . . . . 29
2.14 Correlation between Y-OFF ganglion cells in VirtualRetina . . . . . . . 29
2
4. 3.1 The MEA chip for ganglion recordings . . . . . . . . . . . . . . . . . . 32
3.2 The λij in term of distance between the retinal ganglion cells . . . . . . 33
3
5. Motivation
The virtual Retina, recently developed in the Odyss´e Team at INRIA Sophia-Antipolis
e
allows large scale simulation of the retina with a reasonable computational cost. This
software simulates the Vertebrate Retina by implementing the steps of light-to-spikes
transformations. The light is the input, a spike train is the output. The output depends
on the retina parameters. The actual implementation doesn’t take into account the
correlation between the ganglion cells at the ouput level.
Beginning with the understanding of the VirtualRetina software and the statistical
framework that helps to study the ganglion cell, the aim of this work is to introduce an
important retina characteristic in the simulator to improve the biological plausibility.
The motivation behind is twofold:
1. To introduce a tool for visual neuroscientists that allows large scale input for
cortical simulation.
2. The bio inspired image compression. Due to our collaboration with the I3S who
are working on image compression and reconstruction from spiking data, the
parallel work aims to see what is the effect of introducing connections between
ganglion cells on the image compression efficiency.
Previous works have shown that there are connections between the retina cells at dif-
ferent levels. At different levels in the VirtualRetina, the connections between cells are
taken into account, but not at the level of the ganglion cells. Theoretically, this relation
between cells (at previous levels) doesn’t propagate within the different layers of Virtu-
alRetina there is no correlation between the ganglion cells. This hypothesis is verified
by statistics that are presented in the second chapter. This work demands multidisci-
plinary knowledge. Thanks to the team and the collaboration and people from different
domain are working to accomplish the task. The work is divided into two part. The
first part, which is described in this preliminary report, is to study of previous work
about VirtualRetina and then performing statistics from the VirtualRetina output to
verify if that is no correlation between ganglion cells in Virtual Retina.
4
6. Chapter 1
The Virtual Retina
1.1 Introduction
The goal of this chapter is to introduce the VirtualRetina software. This chapter is
divided it into three parts:
1. In the first part we briefly present vertebrate retina.
2. The second part explains the model which is behind the VirtualRetina Software.
3. The third part explains the software itself.
The main reference of this chapter is the thesis of Adrien Wohrer [1].
1.2 The Vertebrate Retina
The vertebrate retina (situated at the back of the eye, Figure 1.1 ) is the interface
between the Incoming light and the neural pathways. Its main role is the transduction
of light rays into electrical currents. These electrical currents are the action potentials
that go to the cortex. The right eye current go to the left cortex and vice versa (Figure
1.2). The light transduction is processed by consecutive complex phenomena that
happen at different layer in the retina.
The complexity of the retinal structure makes it a wide world to be studied by
scientists. To traduce light into action potentials, several procedure take place through
different stages, going from the photo transducers to the ganglion cells. Note that we
use the term spike instead of action potential. In general, a spike denotes that the
neuron fires, which is equivalent to the fact that this neuron achieves the threshold of
the action potential and fires a potential at its output (Figure 1.3).
5
7. Figure 1.1: At left, the eye from the cornea to the optic nerve. At the right, a magni-
fication shows the retina with some details
Figure 1.2: The visual pathways: From the optics nerves to cortex
6
8. Figure 1.3: Up: the action potential. Down: The corresponding spike train
Back to the retina structure. The following layers define the retina:
• Light receptors.
• Horizontal cells.
• Bipolar cells.
• Amacrine Cells.
• Ganglion cells
These different types of cells are represented in the Figure 1.4. Each of these cell
types has a role in the ’external work image processing’. At last count, the retinal
network is composed of at least 50 clearly distinct cell types.
7
9. Figure 1.4: The different cell types in the vertebrate retina
The Figure 1.5 shows the trajectory of the light-then-spikes process through the
different layers. The light comes from outside, being optically processed by the eye
compartments (Pupil, lens, ...), it then goes through the neural layers and then hits the
extreme back of the retina where the photo receptors are. A chemical process happens
in the photo receptors to transform light into electrical current. There are two types
of receptors: the cones and rods. The rods are very sensitive to light and motion. The
cones get activated at high illumination level and they are sensitive to the colors and
shapes.
8
10. Figure 1.5: The different layers in the retina
Figure 1.6: The different layers in the retina
The electrical current produced by these receptors goes then to the OPL (Outer
Plexiform Layer) where there are two kinds of cells: the horizontal and bipolar cells. As
the Figure 1.6 shows, there are direct connections between the consecutive components
9
11. as well as between the non consecutive components. The light receptors excite the
horizontal and bipolar cells. The bipolar and ganglion cells excite the amacrine cells.
There are also feedback actions: the horizontal cells give inhibitory feedback to the
bipolar cells. The amacrine cells give also inhibitory feedback to the ganglion and
bipolar cells.
1.3 The underlying retina model
This part explains the virtual retina model. The creator of the VirtualRetina software
has chosen to use the contrast gain control model to implement the retinal function-
ality because he wanted to allow large scale simulation keeping in mind the biological
plausibility of the underlying model. The term ’contrast gain control’ refers to the
ability of the retinal system to control the transfer function of the contrast information.
The Figure 1.7 represents a retina model. From incoming light to spike generation,
the process is represented by a processed image and the corresponding mathematical
equation.
The incoming light is represented by L(x, y, t), the illumination quantity at each
pixel of the image, at the time t. This light, being convoluted in time and space
at the receptor layer, gives a positive action for the bipolar cells. On the opposite,
the horizontal cells do another time-space convolution for this signal and acts with
a negative feedback. From both actions, comes the notation difference of Gaussian
(DOG); the output of the OPL for a Light input. The DOF is hence the input of the
IPL; the loge of the contrast gain control process. Our main interest is the ganglion
layer which receive an input current Igang (x,y,t). This current goes to the ganglion cells
which fire spikes when the threshold is reached. The ganglion cells axons, altogether,
are the optic nerve that goes to the cortex. It’s the output from the ganglion cells what
neuroscientists need to do simulation and research in the cortex.
10
13. 1.4 The VirtualRetina software
This software (Figure 1.8 represents its logo) has been developed by Adrien Wohrer, a
former PhD student at INRIA and it exists in two releases:
• February 2009 - May 2010
Figure 1.8: The VirtualRetina software logo
The main aim of this software is to produce spike trains knowing an image sequence
and a retina configuration. An image sequence is a set of successive images appearing
with a fixed speed (24 frame/sec.). The Retina configuration is the set of model pa-
rameters. This configuration could be done thanks to an xml file. To summarize, the
input files are:
• An xml file (the retina configuration).
• An image sequence (The Figure 1.9 is one of the images in the input sequence).
Figure 1.9: An example about a gray scale input image
The software shows how the image appears at the different layers and, at the end, it
gives 4 output files summarizing the simulation, the spike train, the neuron positions.
The spike train file, with a .spk extension, contains two columns. The first column
contains the unit of the ganglion cell and the second contains the corresponding time
of firing.
As the distribution of the ganglion cells is already configured in the input retina.xml
file, the output retina.xml file contains the data of the input.xml file and the metadata
12
14. Figure 1.10: The structure of the .spk file
Figure 1.11: The log polar scheme distribution of the ganglion cells in VirtualRetina.
The distribution is mainly concentrated at the center of the retina (fovea loge). The
concentration of this distribution follows an exponential decay along the distance from
the fovea. The background color shows the pixels of the image and the cell(s) for each
pixel. The VirtualRetina allows the simulation through another scheme, the rectangular
one, where the distribution of the ganglion cells in uniform overall the retina. We
can herein simulate a rectangular scheme with 1 cell/pixel density where each pixel
corresponds to one ganglion cell.
13
15. concerning the units configuration; the identifier (unit) of each cell and it’s spatial
position in degree (Figure 1.11). The degree is a space unit measure in VirtualRetina
and it refers to how much the cell is far from the center, in degree.
The input retina.xml file looks like the Figure 1.12
Figure 1.12: The xml file is the ’written virtual retina’. It carries all the properties of
the different layers in the model.
1.5 Simulation via VirtualRetina
In this section we show some of the results that VirtualRetina produces. The Figure
1.13 shows the signal at the different layers of the retina model. This is the first output
of the software, showing in real time the variation of the signal following the variation
of the input images overall the sequence.
Mainly, we used retina configurations for X, Y, ON and OFF cells 1 for the ganglion
cells layer. The input sequence is the default one (Figure 1.9). To change the configu-
ration of the ganglion layer it’s sufficient to change 4 parameters in the retina.xml file.
1
The X-cells are A specific retinal ganglion cells (neurons) involved in visual information processing
(Troy and Shou, 2002; Hughes, 1979). These cells differ from related Y cells (called also alpha-cells)
and W cells (called also gamma-cells) by their morphology, response properties, and their projection
into cell layers of the lateral geniculate nucleus of the thalamus that transmits information to the visual
cortex (Lennie, 1980; Bowling and Michael, 1984; Sur et al, 1987; Tamamaki et al, 1995; Stanford et
al, 1983; Boykott and Wassle, 1991; Wassle and Boycott, 1991) and represent a class of horizontal cells.
The ON cells have the property to get activated in response to a positive stimilus. In the opposite,
the OFF cells get activated in response to and inhibition.
14
16. (a) Receptor Layer (b) Horizontal Layer (c) OPL Current
(d) Bipolar Current (e) Fast adaptation layer (f) Ganglion inputs cur-
rent
Figure 1.13: The Signal at each layer of the retina model
We used 24 Frame/sec to simulate natural retina ability in caption images. Figure 1.14
shows several spike trains for different cells. The Difference between ON and OFF cells
is that the ON are sensitive to illumination while, on the opposite, the OFF cells are
sensitive to obscurity).
15
17. (a) X-ON cells (b) Y-ON cells
(c) X-OFF cells (d) Y-OFF cells
Figure 1.14: Spike train for different cell types. The retina was configured with 16 cells
in the fovea and with a log polar distribution around. The input image is the default
sequence (Figure 1.9).
The Figure 1.15 shows the spike trains for different colonies of cells but there are
more larger simulation, thousands of cells.
1.6 Conclusion
We saw in this chapter a fast view about the retina from biological and simulation point
of view. As it is supposed to offer, we can use it to produce spike trains for more than
thousands of ganglion cells. It also offers access to other layer of the simulator such
as the input current to the ganglion cells. The time that the simulator take with an
ordinary personal machine (Ubunto 9.10, 2 GB RAM, 1.66 GHZ Core2Duo Processor)
is somehow reasonable: seconds to simulate a retina with hundred of fovea cells and and
a 50 images as an input sequences. The simulation time arises to minutes for simulating
long time sequences,
16
18. (a) XY-cat cells
(b) Magno cells
Figure 1.15: The spike train for a large scale simulation; thousands of cells. The two
figures show the response for ganglion cells of two types (Up: XY cat cells; two layer
of cells. Down: Magno ON and OFF cells). We see the time that the cells take at the
beginning to achieve the asymptotic behavior. This response corresponds to a moving
bar stimulus.
17
19. Chapter 2
Performing statistics with real and
VirtualRetina data
2.1 Introduction
In this chapter we explain the basics and tools with which we are performing statistics
of spike trains. We begin with an explanation about the theoretical basis. We then
introduce the EnaS library; the main tool we use to perform our statistics. Finally,
results from synthetic, real and VirtualRetina data are presented.
2.2 What is a spike train?
As previously presented, the spike train is a structure that contains boolean data (0 and
1) about a neuron activity. To create a spike train we need to know at which time the
neuron fires. A raster is the spike train of several neurons or a neural network (Figure
2.1). We tag the neuron as a ’unit’.
By definition, a spike train can be written as:
N
S(t) = δ(t − ti ) (2.1)
i=1
Where ti is the list of times where the neuron i fires, N is the total number of units or
neurons and δ is the Dirac function.
The Figure 2.1 shows a typical spike train for a network of 600 ganglion cells during
3 secondes (From VirtualRetina simulation).
The length of the raster is the number of the time sample for this raster, N is the
number of neurons in the network.
18
20. Figure 2.1: A typical spike train (produced with VirtualRetina)
Figure 2.2: The raster of 5 neurons is presented here with an explanation about its
length, size and a spike block of Range R
19
21. 2.3 Performing statistics
The data of a spike train are boolean, so that, if we want to perform statistics we will be
dealing with ’ones’ and ’zeros’ that represent the activity and non-activity of neurons.
Denote that i = 0, 1...N − 1; the neuron index. We consider that the activity of the
neuron i is represented by wi (t) where:
1 if the neuron i fires
ωi (t) = (2.2)
0 elsewhere
We define here also another three terms:
1. A spike pattern: represents the activity of the N neurons at a specific time t.
ω(t) = [ωi (t)]N −1
i=0 (2.3)
A spike pattern is -for numerical purpose- encoded as follow:
N −1
ω(t) = 2i ωi (t) (2.4)
i=0
Where i is an integer.
2. A spike block: represents the activity of N neurons between the time t1 and t2 ,
in another way, it’s several consecutive spiking patterns.
t1
ωt2 = ω(t)t1 ≤t≤t2 (2.5)
3. A raster plot: the activity of the N neurons overall the time.
ω(t) = [ωi (t)]0
i=−∞ (2.6)
We consider that the neuron fires in a time interval with a precision δ, i.e., between
t and t + δ. Hence, we consider that, typically, δ = 1ms, the smallest time unit in the
raster time scale. A simple characteristics about a spike train is the firing rate:
ni (t)
ri (t) = f ire/sec. (2.7)
t
Where n the number of times the neuron i fired.
Observables
We call observable, the function that associate a real number to a raster plot. For
example, we can say that observing if the neuron i fires at the time t = 0 is an observable.
We can also observe if the neurons i and j fired at time t = 0 and we attribute the
following function to this event:
φ(ω) = ωi (0)ωj (0) (2.8)
Idem, if the neuron j fired a τ time interval after the neuron i:
φ(ω) = ωi (0)ωj (τ ) (2.9)
20
22. 2.3.1 The statistical models
We want to characterize spike train statistics. Our main assumptions in this scope are
that the spike train is stationary and non deterministic. We also assume to study the
asymptotic behavior of this spike train.
The simplest statistical model is the Bernoulli distribution.
The probability that a block ωs corresponds to a known word 1 at is:
t
s
µ(ωs = at ) = µ(ωi (k) = ai (k), i = 1...N, k = s...t)
t
s
= ΠN Πt µ(ωi (k) = ai (k))
i=1 k=s (2.10)
n (t,s)
= ΠN ri i (1
i=1 − ri )(t−s−ni (t−s))
µ refers to the probability that the events (between parenthesis) happen, ni and ri are
respectively the number of spike and firing rate of the neuron i in the time interval
[s, t] This probability (In the Bernoulli Distribution) corresponds to the firing rate of
the neuron i. The correlation between two neuron is then:
Ci,j = |µ(ωi (t)ωj (t)) − µ(ωi (t))µ(ωj (t))| (2.11)
Thus, Ci,j is equal to how many time the two neurons i and j fired together minus the
product of their firing rate.
More generally, given a spike train, we are looking to find the statistical model of the
data representing this spike train. For example, in a Bernoulli model, we search the
probability µ that corresponds to the firing rate ri (µ(ωi ) = ri ).
The Entropy measures how disorganized the system is. For a spike block w having a
probability µ we define the entropy as:
h(µ) = − µ( )logµ( ) (2.12)
Where (t) = N −1 2i ωi (t). (t) is the sum overall possible spike blocks. We take
i=0
into account some constraint when maximizing the entropy:
1. The first constraint means to approach the experimental and measured probabil-
ity: µ(ϕl ) = ϕexp , the experimental average of a funtion ϕl .
l
2. The second constraint is a classic assumption in probability: w µ(w) = 1.
Gibbs Potential
A Gibbs distribution is a probability that maximizes the statistical entropy under the
constraint that the experimental average ϕl the same prescribed functions ϕl is equal
¯
to the average with respect to µ.
1
We note by the term word, the structure that represents the neuron activity through a spike block
21
23. This distribution is such that the probability of a block , µ( ), behaves like eψ( ) .
ψ( ) is the Gibbs Potential, defined by:
L
ψ= λl φl (2.13)
l=1
Where λl are the set of Lagrange multipliers. The equation 3.1 represent the statistical
model. Additionally, the entropy os such a potential obeys:
P [ψ] = h[µ] + µ[ψ] (2.14)
Where µ is the probability distribution we are locking for (µ(φl ) = Cl ), and P is the
topological pressure.
The process of Entropy Maximization is equivalent then to find the parameters λl .
For example, to see the correlation between two neurons, we suppose that the statistical
model is given by the following Gibbs Potential:
ψ(ω) = λ1 ω0 (0) + λ2 ω1 (0) + λ3 ω0 (0)ω1 (0) (2.15)
In this equation, we have three monomials, each of then is multiplied by a coefficient
λi . The developed EnaS library allow to find these parameters given a spike train and
by the mean of the Entropy Maximization. To find out the correlation, we read and
interpret the coefficient λ3 which means that if this coefficient is important in the model
then, the number of time the neurons 0 and 1 fired together is high, which means that
they are strongly correlated.
2.4 The EnaS Library
EnaS (Figure 2.3), the Event neural assembly Simulation is a dedicated C++ library.
It has been developed in collaboration between the NeuroMathComp and Cortex team
within the INRIA Sophia-Antipolis. This library is dedicated for neural simulation and
doing statistics for real or synthetic data.
Figure 2.3: The Enas Logo
22
24. The code source is available on the EnaS website (http://enas.gforge.inria.
fr/) with some tutorial documentations and examples about the uses if its classes.
The source doesn’t need to be installed or configured. It’s sufficient to put it in the
same work director and include ‘‘EnaS.h’’; and include namespace enas;.
The compilation of the program that holds ‘‘EnaS.h’’; is a bit different because the
library uses another external libraries such that gsl, gpl and glpk which have to be
installed and configured before using this library, and hence, the compilation command
has to be the following:
g++ -Wall -lgsl -lgslcblas -lglpk -lm MyFile.cpp -o MyOutputFile.o
The main classes we used from this library are the following:
• FileTimeSequence: to load a ’time-unit’or a .spk file containing the boolean data
of a spike train.
• PrefixTree: That defines a tree from a Gibbs potential which allows to estimate
some statistical parameters.
• GibbsPotential: the class that allows the modeling of a set of data and the ex-
traction of the model parameters such as the λ coefficients.
This library is helpul to our project because it contains classes that can help us
to perform some statistics. For example, EnaS can estimate the parameters of the
statistical model of a spike train (The λs in the Eq. 2.15).
2.5 Performing statistics with the EnaS library and
results
The main idea behind using EnaS with spike trains data is to estimate the parameters
λl between neurons. As a special case, we are interested in correlation. This correlation
depends on the statistical model that the spike train follows. In an Bernoulli model,
the correlation tends to zero along a raster.
Data from Bernoulli distribution could be generated at different probability value.
We note that, in Bernoulli distribution, the probability of the two independent possible
events is complementary, i.e., if p is the probability that the event happens, so, the
probability that the event doesn’t happen is equal to 1 − p.
The purpose to measure the correlation between two neurons doesn’t mean that we
estimate this correlation and we see if its value is very low or not, but, the matter is
to see how does this correlation evolve in term of the Raster length. Actually, we can
show theoretically and experimentally that this correlation decreases with the Raster
length. The evolving function in a log scale is:
k
Ci,j (t) = (2.16)
(T )
23
25. where k is a real number and T is the raster length.
2.5.1 Synthetic data
Figure 2.4 shows the property of the exponential decay of correlation in term of the
raster length. The data were generated randomly with a Bernoulli distribution and
p = 0.5. We can also show the same idea for different Bernoulli probability distribution
(From p = 0.1 to p = 0.9), Figure 2.5.
A fitting technique (Lavenberg-Marquadt) allowed us to fit the results with the Tka . If
we saw the results for the whole time scale, we can observe that they really follow the
0.6
line of equation √T (Where k = 0.6 and a = 0.5).
Figure 2.4: The correlation between two neurons whose raster plots follow a Bernouilli
distribution. The data are synthetic with a probability distribution p = 0.4, and the
plot is in the logarithmic scale. The evolving of the calculated correlation with empirical
algorithms decreases in a closed manner to the √k line.
(T )
24
26. Figure 2.5: Evolving of the correlation for different probability distributions (Bernouilli
distribution)
Figure 2.6: Evolving of the correlation for different time coincidence. The delay is the
term we use to express that the two neurons fire between a τ time interval. We studied
the correlation for different daly values, going from 1 to 6.
25
27. 2.5.2 Real data
One of our collaborator (Kolo Bodgan, INSERM)supplied us with some real data ac-
quisition for ganglion cells activity. The signals were acquired with an “MEA Chip”
that contains 58 electrodes (Figure 3.1).
Figure 2.7: The MEA chip for ganglion recordings
Data characteristics:
• The total duration of the acquisition is 93 s.
• 30 sec. of spontaneous activity followed by 63 sec. of evoked potential activity
(Figure 2.8). The 63 sec. of evoked potential were done as 1 sec. of light stimulus
followed by 5 sec. inter-interval whit non stimulus.
Figure 2.8: The evoked potential recording. A light stimulus is given in face to the
eye and, back, at the ganglion cells layer, the recording of electric potential takes place
with the MEA acquisition grid.
Statistics about the correlation between two random neuron within the real acquired
data have shown that the correlation doesn’t tend to 0 when the raster length tend to
26
28. ∞. This fact emphasizes the hypothesis that the neurons in the vertebrate retina are
correlated, from where our motivation to add the correlation property to the ganglion
cells in VirtualRetina.
We applied the Gibbs potential model on this data (Equation 2.15) in order to
estimate the correlation between several couples of neurons, in term of the raster length.
Figure 2.9: Correlation between ganglion cells in real acquisition. The figure shows
the correlation for three couples of neurons, at severals distances. The time scale
corresponds to the first 30 sec. of the acquisition 2.5.2; neurons at rest.
27
29. Figure 2.10: Correlation between ganglion cells in real acquisition. The figure shows
the correlation for three couples of neurons, at severals distances. The time scale
corresponds to the last 60 sec. of the acquisition 2.5.2; in evoked potential
2.5.3 VirtualRetina data
Data with virtual retina could be generated through the installed software or the web-
service implementation. It’s hence prefered to use the software after installation because
it’s more controllable and all the parameter you put in the model are also accessible.
The below figures (2.11, 2.12, 2.13, 2.14) show that the correlation between different
couple of neurons at at several distances. For the 4 cell types, the correlation tends to
zero for the highe raster length.
These figures show the increasing of the correlation value in term of the raster length.
There are some perturbation at the extermities of the curves. The perturbation at the
beginning comes from the non asymptotic behavior of the spike train.
28
30. Figure 2.11: Correlation between X-ON ganglion cells in VirtualRetina
Figure 2.12: Correlation between Y-ON ganglion cells in VirtualRetina
29
31. Figure 2.13: Correlation between X-OFF ganglion cells in VirtualRetina
Figure 2.14: Correlation between Y-OFF ganglion cells in VirtualRetina
30
32. 2.6 Conclusion
We have shown in this chapter the statistical tools we are based on to perform statistics
with spike trains. We also explained the mathematical, numerical and algorithmic
frameworks that are behind. With these tools we have shown the statistics for real
acquisitions, simulated and synthetic data. We have shown the difference between the
between the Bernoulli model data, the VirtualRetina data and real acquisitions by
studying the correlation in term of the raster length in each of these cases.
a
In synthetic data, the correlation increases as the line √T increases. In VirtualRetina
simulations, the correlation follows also this line. On the opposite, the correlation
between different couples of neurons in real acquisitions doesn’t verify this property,
which means that the ganglions cells in VirtualRetina are not correlated.
However, there exist connections between cells in the VirtualRetina model but at
the lower layers, not at the ganglion cells layer. The results have shown also that the
connections between the retina cells at the lower layer doesn’t imply automatically that
the ganglion cells are correlated. We need also that the ganglion cells have connections
between themselves.
In the scope of the second part of the project, we would like to add connections be-
tween the ganglion cells in VirtualRetina in order to enhance the biological plausibility,
i.e., we will translate the values of the statistical model parameters (for real ganglion
cells) in connections and add them to the ganglion cells in the VirtualRetina.
31
33. Chapter 3
Infering connectivity between
Retinal Ganglion Cells
3.1 introduction
In the first section of this chapter we will show the results that give an idea about the
connectivity between retinal ganglion cells from real data acquisition. The second sec-
tion will be a bibliographical study. This chapter is in fact complementary to the second
one; both explain about how to know about connectivity between retinal ganglion cells
but in two different ways: previousely we measured the evolving of the correlation in
term of raster length, and now, we measure quatitatively the connectivity using the
parameters of Gibbs Models.
3.2 The connectivity between ganglion cells from
real data acquisition
Thanks to some acquired data by one of our collaborator (Kolomiets Bodgan, Institue
de vision de Paris), we could use the power of EnaS library to make some statistics
about connectivity. For technical details, we can take an idea about the connectivity
between two neurons from the λl in the Gibbs Potential equation:
L
ψ= λl φl (3.1)
l=1
This equation is described previousely in the second chapter (Section 2.3.1).
In fact, the bigger the λl , the more the occurence of the observable in the train spike.
By consequence, if we measure different λl for several couples of neuron, we will have
an idea about their connectivity.
Recalling the acquisition electrode MEA (fig. 3.1):
In the following we will:
32
34. Figure 3.1: The MEA chip for ganglion recordings
1. Take a collection of six neurons.
2. Apply the Gibbs model.
3. Use Enas to estimate the parameters λl .
33
35. Figure 3.2: The 6 graphes show the value of λ0j in term of distance for different net-
work arrangement ( Vertical (Ex: The cells 12,13,14,15,16,17) ,Horizontal , Random).
The idea here is to see how does the distance affect the connectivity factor.The x-axis
represents the distance between the first cell (called Cell 0) and the j-th cell (Called
Cell j). The y-axis represents the connectivity factor λij between the cell 0 and the cell
j (j=1,2,...5). In all the graphs we can observe that: A part or the whole graph behaves
like a increasing at the beginning then decreasing after some distance (commonly here
between 400 and 1000 µm).
34
36. Bibliography
[1] Adrien Wohrer, Model and large-scale simulator of a biological retina, with contrast
gain control. University of Nice Sophia-Antipolis, INRIA, 2009.
[2] J.C. Vasquez1, T. Viville, B. Cessac, Entropy-based parametric estimation of spike
train statistics. INRIA, 2009
[3] S. Coccoa, S. Leiblerb and R. Monassond Neuronal couplings between retinal gan-
glion cells inferred by efficient inverse statistical physics methods PNAS, 2009
[4] C. Shalizi, K. Shalizi Blind Construction of Optimal Nonlinear Recursive Predictors
for Discrete Sequences CoRR, 2004
[5] Authors: R. Haslinger, K. Klinkner, C. Shalizi The Computational Structure of
Spike Trains Neural Computation, vol. 22 (2010), pp. 121–157
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