Merge to Neural coding discuss Talk Neural coding Merger possibilities date July 2010 The sparse code is a kind of neural code in which each item is encoded by the strong activation of a relatively small set of neurons. For each item to be encoded, this is a different subset of all available neurons. As a consequence, sparseness may be focused on temporal sparseness a relatively small number of time periods are active or on the sparseness in an activated population of neurons. In this latter case, this may be defined in one time period as the number of activated neurons relative to the total number of neurons in the population. This seems to be a hallmark of neural computations since compared to traditional computers, information is massively distributed across neurons. A major result in neural coding from Olshausen et al. is that sparsecoding of natural images produces wavelet like oriented filters that resemble the receptive fields of simple cells in the visual cortex. See also Rate coding Independent spike coding Correlation coding Population coding Grandmother cell References F ldi k P, Endres D, http www.scholarpedia.org article SparseCodingSparsecoding , Scholarpedia , 3 1 2984, 2008. Dayan P & Abbott LF. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems . Cambridge, Massachusetts The MIT Press 2001. ISBN 0 262 04199 5 Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W. Spikes Exploring the Neural Code . Cambridge, Massachusetts The MIT Press 1999. ISBN 0 262 68108 0 B. A. Olshausen and D. J. Field. Emergence of simple cell receptive field properties by learning a sparse code for natural images. Nature, 381 6583 607 9, jun 1996. Category Neural coding neuroscience stub ... more details
this a software static analysis tool Infobox software name Sparse logo screenshot caption author Linus ... release date release date 2010 11 03 ref cite mailing list title Sparse 0.4.3 released author Christopher Li date 2010 11 03 mailinglist linux sparse url http marc.info ?l linux sparse&m 128875779125979 ... website http sparse.wiki.kernel.org In computer science , Sparse is a tool designed to find possible coding faults in the Linux kernel . ref cite conference title Semantic patches for documenting and automating ... code. Sparse is a library that, like a compiler front end, provides convenient access to the abstract ... address space . Sparse contains built in checks for known problematic and a set of annotations designed ... to, or what locks a function acquires or releases. Linus Torvalds started writing Sparse in 2003. Josh Triplett was the maintainer of Sparse from 2006, a role taken over by Christopher Li in 2009. ref cite mailing list title Sparse 0.4.2 released author Christopher Li date 2009 10 16 mailinglist linux sparse url http marc.info ?l linux sparse&m 125570805231414&w 2 accessdate 2010 11 06 ref Li is the current maintainer. Sparse is released under the Open Software License , version 1.1. Annotations Some of the checks performed by Sparse require annotating the source code using the tt attribute tt GNU Compiler Collection GCC extension, or the Sparse specific tt context tt specifier. ref cite web ... Compiler Collection GCC publisher Free Software Foundation accessdate 2010 11 13 ref Sparse defines ... bitwise tt attribute is used to mark these types as restricted, so Sparse will give a warning if variables ... force tt attribute is used to avoid Sparse giving a warning. See also Portal Free software List ... links http sparse.wiki.kernel.org Sparse web page http www.mjmwired.net kernel Documentation sparse.txt Using sparse for typechecking , Linux Kernel Documentation man 1 sparse Semantic Parser for C man 1 cgcc Compiler wrapper to run Sparse after compiling Category Static program analysis tools ... more details
Wiktionarypar codingCoding may refer to Channel coding in coding theory Line coding Computer programming The process of Statistical classification of information Coding social sciences Legal coding A coding strand of DNA is translated into a protein product Present progressive tense for Code Blue , which is a patient in Cardiac Arrest or Respiratory Arrest See also Code Encoding Entropy encoding Source coding Medical coding disambig ... more details
wiktionary sparseSparse may refer to Sparse , a software static analysis tool sparse matrix , in numerical analysis a matrix populated primarily with zeros sparse file , a computer file mostly empty See also Prefixindex sparse disambig ... more details
A sparse vector is a Euclidean vector vector whose elements are mostly zeros. Mathematically, a sparse vector has a low L0 norm Zero norm . See also Sparse matrix Norm mathematics Compressed sensing Category Vectors math stub pl Wektor rzadki ... more details
Image Sparse file en .svg thumb A sparse file The empty bytes don t need to be saved, thus they can be represented by metadata . In computer science , a sparse file is a type of computer file that attempts ... to disk as the actual size only when the block contains real non empty data. When reading sparse ... systems support sparse files, including most Unix variants and NTFS ref Dominic Giampaolo Giampaolo ... with the Be File System . Morgan Kaufmann. ISBN 1 55860 497 9 ref , but notably not Apple s HFS . Sparse .... Advantages The advantage of sparse files is that storage is only allocated when actually ... on the file system. Disadvantages Disadvantages are that sparse files may become fragmentation computer ... sparse files can have unexpected effects such as disk full or quota exceeded errors when merely overwriting an existing portion of a file that happened to have been sparse and copying a sparse file with a computer ... size of the file, including the sparse, mostly zero sections which are not on disk&mdash losing the benefits of the sparse property in the file. Sparse files are also not fully supported by all backup software or applications. Sparse Files in Unix Sparse files are typically handled transparently to the user. But the differences between a normal file and sparse file become apparent in some situations. Creation The Unix command dd if dev null of sparse file bs 1k seek 5120 will create a file of five ... Detection Sparse files have different apparent and actual file sizes. This can be detected by comparing the output of du B1 sparse file to that of ls l sparse file Some systems do not support the tt ... version of cp is good at detecting whether a file is sparse, so it suffices to run cp sparse file new file and new file will be sparse. However, GNU cp does have a sparse WHEN option ref http git.savannah.gnu.org ... new option to control creation of sparse files ref . This is especially useful if a sparse file has somehow become non sparse i.e. the empty blocks have been written out to disk in full . Disk space ... more details
In computational complexity theory , a sparse language is a formal language a set of String computer science strings such that the number of strings of length n in the language is bounded by a polynomial ... NP complexity NP with other classes. The complexity class of all sparse languages is called SPARSE . Sparse languages are called sparse because there are a total of 2 sup n sup strings of length n , and if a language ... n that it contains rapidly goes to zero as n grows. All unary language s are sparse. An example of a nontrivial sparse language is the set of binary strings containing exactly k 1 bits for some fixed ... is bounded by n sup k sup . Relationships to other complexity classes SPARSE contains TALLY , the class ... in P poly are sparse, there is a polynomial time Turing reduction from any language in P poly to a sparse language. ref Jin Yi Cai. Lecture 11 P poly, Sparse Sets, and Mahaney s Theorem. CS 810 ... pages.cs.wisc.edu jyc 810notes lecture11.pdf ref Fortune showed in 1979 that if any sparse language is co NP complete , then P NP problem P NP ref S. Fortune. A note on sparse complete sets ... in 1982 that if any sparse language is NP complete , then P NP this is Mahaney s theorem . ref S. R. Mahaney. Sparse complete sets for NP Solution of a conjecture by Berman and Hartmanis ... truth table reducibility of NP sets to sparse sets. SIAM Journal on Computing volume 20, pp.471&ndash 483. 1991. ref E complexity E &ne NE complexity NE if and only if there exist sparse languages in NP that are not in P . ref Juris Hartmanis, Neil Immerman, Vivian Sewelson. Sparse Sets in NP ..., building on work by Ogihara, showed that if there exists a sparse P complete problem, then L complexity L P complexity P . ref Jin Yi Cai and D. Sivakumar. Sparse hard sets for P resolution of a conjecture ... Favorite Theorems Small Sets . April 18, 2006. Bill Gasarch. http weblog.fortnow.com 2007 06 sparse sets tribute to mahaney.html Sparse Sets Tribute to Mahaney . June 29, 2007. CZoo SPARSE S sparse Category ... more details
Sparse grids are a numerical technique to represent, integrate or interpolate high dimension al functions. They were originally found by the Russia n mathematician Smolyak and are based on a sparse tensor product construction. Computer algorithms for efficient implementations of such grids were later developed by Michael Griebel and Christoph Zenger . Curse of dimensionality The standard way of representing multidimensional functions are tensor or full grids. The number of basis functions or nodes grid points that have to be stored and processed exponential function depend exponentially on the number of dimensions. Even with today s computational power it is not possible to process functions with more than 4 or 5 dimensions. The curse of dimension is expressed in the order of the integration error that is made by a quadrature of level math l math , with math N l math points. The function has regularity math r math , i.e. is math r math times differentiable. The number of dimensions is math d math . math E l O N l frac r d math Smolyak s quadrature rule Smolyak found a computationally more efficient method of integrating multidimensional functions based on a univariate quadrature rule math Q 1 math . The math d math dimensional Smolyak integral math Q d math of a function math f math can be written as a recursion formula with the tensor product. math Q l d f left sum i 0 l left Q i 1 Q i 1 1 right otimes Q l i d 1 right f math The index to math Q math is the level of the discretization ... A memory efficient data structure for regular sparse grids http sparse grids.de Code to generate ... zumbusch fd.html Finite difference scheme on sparse grids http www.vis.uni stuttgart.de ger research fields recent sparse Visualization on sparse grids http wissrech.iam.uni bonn.de research pub garcke kdd.pdf Datamining on sparse grids, J.Garcke, M.Griebel pdf http www.math.tu berlin.de garcke paper sparseGridTutorial.pdf Sparse Grid Tutorial, J.Garcke pdf Mathanalysis stub Category Numerical analysis ... more details
Image Finite element sparse matrix.png right thumb A sparse matrix obtained when solving a finite element ... of numerical analysis , a sparse matrix is a matrix mathematics matrix populated primarily ... by springs from one to the next this is a sparse system. By contrast, if the same line of balls ... , which have a low density of significant data or connections. Huge sparse matrices often appear ... sparse matrices on a computer , it is beneficial and often necessary to use specialized algorithm s and data structure s that take advantage of the sparse structure of the matrix. Operations using standard ... memory when applied to large sparse matrices. Sparse data is by nature easily data compression compressed .... Indeed, some very large sparse matrices are infeasible to manipulate with the standard dense algorithms. Storing a sparse matrix The na ve data structure for a matrix is a two dimensional array. Each ... row, column code tuple s to values. This format is good for incrementally constructing a sparse array .... This is the traditional format for specifying a sparse matrix in Matlab via the code sparse code ... help techdoc ref sparse.html Matlab sparse . Yale format The Yale Sparse Matrix Format stores an initial sparse m n matrix, M , in row form using three one dimensional arrays. Let code NNZ code denote ... . Compressed sparse row CSR or CRS http netlib.org linalg html templates node91.html SECTION00931100000000000000 CSR is effectively identical to the Yale Sparse Matrix format, except that the column ... matrix . Compressed sparse column CSC or CCS http netlib.org linalg html templates node92.html ... of them dominant say a file that stores a handwritten signature can be encoded as a sparse matrix that contains ... matrix An important special type of sparse matrices is band matrix , defined as follows. The lower ... bandwidth 1. As another example, the following sparse matrix has lower and upper bandwidth both ... lend themselves to simpler algorithms than general sparse matrices or one can sometimes apply ... more details
Sparse PCA Principal Component Analysis is a specialised technique used in statistical analysis and, in particular, in the analysis of multivariate datasets. Ordinary principal component analysis PCA uses a vector space transform used to reduce multidimensional data set s to lower dimensions for analysis. It finds linear combination s of variables called principal components that correspond to directions of maximal variance in the data. The number of new variables created by these linear combinations is usually much lower than the number of variables in the original dataset. Sparse PCA finds sets of sparse vector s for use as weights in the linear combinations while still explaining most of the variance present in the data. Several approaches have been proposed, including a regression framework ref cite journal author H. Zou and T. Hastie and R. Tibshirani year 2006 title Sparse principal component analysis journal Jcgs 2006 15 2 262 286 url http www stat.stanford.edu hastie Papers spc jcgs.pdf ref , a convex relaxation semidefinite programming framework ref cite journal author Alexandre d Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet year 2004 title A Direct Formulation for Sparse PCA Using Semidefinite Programming journal Advances in Neural Information Processing Sytems NIPS , MIT Press url http www.princeton.edu aspremon sparsesvd.pdf ref , a generalized power method framework ref cite journal author Michel Journee, Yurii Nesterov, Peter Richtarik, Rodolphe Sepulchre year 2008 pages 4724 title Generalized Power Method for Sparse Principal Component Analysis volume 0811 journal CORE Discussion Paper 2008 70 url http www.optimization online.org DB FILE 2008 11 2156.pdf bibcode 2008arXiv0811.4724J ref , forward backward greedy search and exact methods using branch and bound techniques ref cite journal author Baback Moghaddam, Yair Weiss, Shai Avidan year 2005 title Spectral Bounds for Sparse PCA Exact and Greedy Algorithms journal Advances in Neural ... more details
Refimprove date March 2011 A sparse image is a type of disk image file that can be created under Mac OS X using Disk Utility . Encrypted sparse image files are used to secure a user s home directory by Mac OS X s FileVault feature. Unlike a full image file .dmg , which takes up as much actual space as the real disk it represents regardless of the amount of unused space , a sparse image file .sparseimage takes up only as much actual disk space as the data contained within. This can be advantageous when backing up data. For example, assume a user would like to make a backup of a hard disk volume named INFO that is 5GB in size, yet only contains 2.5GB of actual data. While a single layer DVD R with around 4.7GB capacity would be too small to store a full sized INFO.dmg image file, an INFO.sparseimage image file will fit. This is because the unused 2.5GB from the original volume is not actually allocated within the sparse image file. The INFO.sparseimage file will occupy only 2.5GB. Note that if the 2.5GB INFO.sparseimage backup on the DVD is subsequently opened and mounted as a clone of the source volume, the Finder will report the clone as having the original 5GB capacity, with 2.5GB available, but the virtual disk will be read only , because the source image file resides on read only media. However, the first generation 2.5GB INFO.sparseimage on a hard drive, when mounted, may have files added to it until the originally designated 5GB capacity is reached. The .sparseimage file will expand in size to accommodate the additional data. Deleting files from the mounted virtual volume ... the following in the Terminal code man hdiutil code Sparse bundle Mac OS X v10.5 Leopard introduced the concept of the sparse bundle . ref http macosx.com article live filevaultsparse bundle backups in leopard.html Live FileVault and Sparse Bundle Backups in Leopard ref Instead of a monolithic file, a sparse bundle is a Bundle NEXTSTEP bundle directory that stores the disk image as banded data files ... more details
In computer science , a sparse array is an Array data structure array in which most of the elements have the same value known as the default value usually 0 or nullable type null . A naive implementation of an array may allocate space for the entire array, but in the case where there are few non default values, this implementation is inefficient. Typically the algorithm used instead of an ordinary array is determined by other known features or statistical features of the array, for instance if the sparsity is known in advance, or if the elements are arranged according to some function e.g. occur in blocks . As an example, a spreadsheet containing 100 100 mostly empty cells might be more efficiently stored as a linked list rather than an array containing ten thousand array elements. A heap malloc memory allocator inside a program might choose to store regions of blank space inside a linked list rather than storing all of the allocated regions in, say a bit array . so what? blank space is not an indexed array sparse or not , indeed See also Sparse matrix External links http boost.org libs numeric ublas doc vector sparse.htm Boost sparse vector class http portal.acm.org citation.cfm?id 1363086&jmp cit&coll GUIDE&dl GUIDE Rodolphe Buda, Two Dimensional Aggregation Procedure An Alternative to the Matrix Algebraic Algorithm , Computational Economics , 31 4 , May, pp.397 408, 2008. Category Arrays datastructure stub ru ... more details
Sparse approximation is the problem of finding a signal or vector estimate with sparseness property, that is having a small number of nonzero elements, that satisfies approximately a system of equations. For example, consider a System of linear equations linear system of equations y Ax , where A is a real M by N matrix mathematics matrix and M N . In general, this problem is ill posed as there are infinitely many x that solve this system. One way to enforce sparsity is to choose x such that as many components as possible are zero. In other words, we want to solve math min x x 0, text such that y A x, math where the objective function is defined by math x 0 k x k neq 0, , k 1, ldots,N math and denotes the cardinality of the set. However, this problem is NP complete because classical combinatorial optimization can be reduced to it. Approximations There are several algorithms that find a suboptimal sparse approximation Matching pursuit sets elements of math x math in a greedy way. Basis pursuit solves math min x x 1, text such that y A x math instead, using linear programming . The Method Of Frames solves math min x x 2, text such that y A x math . Dantzig selector References Citation last1 Gribonval first1 R. last2 Figueras i Ventura first2 R. M. last3 Vandergheynst first3 P. title A simple test to check the optimality of a sparse signal approximation doi 10.1016 j.sigpro.2005.05.026 year 2006 journal Signal Processing volume 86 issue 3 pages 496 510 . Numerical linear algebra Category Numerical linear algebra ... more details
A sparse ruler is a ruler in which some of the distance marks are missing, yet which allows you to measure any integer distance up to its full length. More abstractly, a sparse ruler of length math L math with math m math marks is a sequence of integers math a 1, a 2, ..., a m math where math 0 a 1 a 2 ... a m L math . The marks math a 1 math and math a m math correspond to the ends of the ruler. In order to measure the distance math K math , with math 0 K L math there must be marks math a i math and math a j math such that math a j a i K math . A sparse ruler is called minimal if there is no sparse ruler of length math L math with math m 1 math marks. In other words, if any of the marks is removed one can no longer measure all of the distances. A sparse ruler is called maximal if there is no sparse ruler of length math L 1 math with math m math marks. A sparse ruler is called optimal if it is both minimal and maximal. Since the number of distinct pairs of marks is math m m 1 2 math , this is an upper bound on the length math L math of any maximal sparse ruler with math m math marks. This upper bound can be achieved only for 2, 3 or 4 marks. For larger numbers of marks, the difference between the optimal length and the bound grows gradually, and unevenly. For example, for 6 marks the upper bound is 15, but the maximal length is 13. There are 3 different configurations of sparse rulers of length 13 with 6 marks. One is 0, 1, 2, 6, 10, 13 . To measure a length of 7, say, with this ruler you would take the distance between the marks at 6 and 13. Sparse rulers are closely related ... longer than an optimal sparse ruler with math m math marks, since math m m 1 2 math is a lower bound ... The following are examples of minimal sparse rulers. Optimal rulers are highlighted. When there are too ... sparse rulers.html http www.maa.org editorial mathgames mathgames 11 15 04.html http www.contestcen.com scale.htm http members.cox.net wnmyers sparse rulers.txt Category Number theory Category Combinatorics ... more details
Merge to Neural coding discuss Talk Neural coding Merger possibilities date July 2010 The temporal coding is a type of neural coding which relies on precise timing of action potential s or inter spike interval s. Combined with traditional rate coding models, temporal coding can provide additional information with the same rate. There is no precise definition of temporal code. Actually almost any coding scheme that is not rate coding can be referred to as a temporal code. Unlike rate coding, the temporal coding model tries to account for short term stimuli producing a small number of spikes. For example, if a neuron is capable of firing at a maximum rate of 100 spikes per second, then a stimulus 10ms would likely elicit only a single spike. This model is especially important for sound localization , which occurs within the brain on the order of milliseconds. The discovery of spike timing dependent plasticity , where synaptic efficacy is modulated by the precise timing of spikes, provides strong evidence that temporal codes are used for cortical information transmission. It should not be confused with the coding of the temporal information. See also Sparsecoding Independent spike coding Correlation coding Population coding Neural synchronization References Vanrullen, R., Guyonneau, R., and Thorpe, S. 2005 . Spike times make sense. Trends in Neurosciences , 28 1 1 4. Rullen, R. V. and Thorpe, S. J. 2001 . Rate Coding Versus Temporal Order Coding What the Retinal Ganglion Cells Tell the Visual Cortex. Neural Computation , 13 6 1255 1283. Dayan P, Abbott LF. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems . Cambridge, Massachusetts The MIT Press 2001. ISBN 0 262 04199 5 Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W. Spikes Exploring the Neural Code . Cambridge, Massachusetts The MIT Press 1999. ISBN 0 262 68108 0 Theunissen F, Miller ..., 2, 149 162 1995. Category Neural coding neuroscience stub ... more details
Merge to Neural coding discuss Talk Neural coding Merger possibilities date July 2010 The rate coding model of neuron al firing communication states that as the intensity of a stimulus increases the frequency or rate of action potential s, or spike firing , increases. Rate coding is sometimes called frequency coding . This phenomenon was originally shown by Edgar Adrian ED Adrian and Y Zotterman in 1926. ref cite journal title The impulses produced by sensory nerve endings Part II The response of a single end organ. author Adrian ED & Zotterman Y. journal J Physiol Lond. year 1926 volume 61 pages 151 171 http www.ncbi.nlm.nih.gov pmc articles PMC1514782 ref In this simple experiment different weights were hung from a muscle . As the weight of the stimulus increased, the number of spikes recorded from sensory nerves innervating the muscle also increased. From these original experiments Adrian and Zotterman concluded that action potentials were unitary events, and that the frequency of events, and not individual event magnitude, was the basis for most inter neuronal communication. See also Temporal codingSparsecoding Independent spike coding Correlation coding Population coding References references Dayan P & Abbott LF. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems . Cambridge, Massachusetts The MIT Press 2001. ISBN 978 0 262 04199 7 Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W. Spikes Exploring the Neural Code . Cambridge, Massachusetts The MIT Press 1999. ISBN 978 0 262 68108 7 Category Neural coding neuroscience stub ... more details
Merge to Neural coding discuss Talk Neural coding Merger possibilities date July 2010 The correlation coding model of neuron al firing claims that correlations between action potential s, or spikes , within a spike train may carry additional information above and beyond the simple timing of the spikes. It has been theoretically demonstrated that correlation between spike trains can only reduce, and never increase, the total mutual information present in the two spike trains about a stimulus feature. ref KO Johnson, J Neurophysiol. 1980 Jun 43 6 1793 815. ref Any degree of correlation reduces the total entropy thus, by Fisher information Fisher s Information Theorem , correlations can only reduce information. However, this does not prevent correlations from carrying information not present in the average firing rate of two pairs of neurons. A good example of this exists in the pentobarbital anesthetized marmoset auditory cortex, in which a pure tone causes an increase in the number of correlated spikes, but not an increase in the mean firing rate, of pairs of neurons. ref Nature. 1996 Jun 13 381 6583 610 3 ref Contrast this with independent spike coding . See also Rate coding Temporal codingSparsecoding Independent spike coding Population coding References Dayan P & Abbott LF. Theoretical Neuroscience Computational and Mathematical Modeling of Neural Systems . Cambridge, Massachusetts The MIT Press 2001. ISBN 978 0 262 04199 7 Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W. Spikes Exploring the Neural Code . Cambridge, Massachusetts The MIT Press 1999. ISBN 978 0 262 68108 7 references Category Neural coding neuroscience stub uk ... more details
none ref . Contrast this with sparsecoding . See also Rate coding Temporal codingSparsecoding Independent spike coding Neural coding References Dayan P & Abbott LF. Theoretical Neuroscience ...Merge to Neural coding discuss Talk Neural coding Merger possibilities date July 2010 Population coding is a means by which information is coded in a group of neurons. In population coding, each neuron has a distribution of responses over some set of inputs, and the responses of many neurons may be combined to determine some value about the inputs. In one classic example in primary motor cortex, Georgopoulos and colleagues trained monkeys to move a joystick in one of several directions towards a target. ref Science. 1986 Sep 26 233 4771 1416 9 ref Neurons in primary motor cortex responded maximally during movements to their preferred direction, and their response decreased if the animal made movements towards directions increasingly different from the preferred direction. Kenneth Johnson originally derived that if each neuron represents movement in its preferred direction, and the vector sum of all neurons is calculated each neuron has a firing rate and a preferred direction , the sum points in the direction of motion. In this manner, the population of neurons codes the signal for the motion. This particular population code is referred to as population vector coding. This particular study divided the field of motor physiologists between Evarts upper motor neuron group, which followed the hypothesis that motor cortex neurons contributed to control of single muscles, and the Georgopoulos group studying the representation of movement directions in cortex. Typically an encoding function has a peak value such that activity of the neuron is greatest if the perceptual value is close ... of neurons. The Johnson Georgopoulos vector coding is an example of simple averaging. A more sophisticated ... Neural coding ... more details
Elias coding is term used for one of two types of lossless coding schemes used in digital communications Shannon Fano Elias coding , a precursor to arithmetic coding , in which probabilities are used to determine codewords Universal code data compression Universal coding using one of Elias three universal codes, each with predetermined codewords Elias delta coding Elias gamma coding Elias omega coding Disambig cs Eliasovy k dy ... more details
coding Independent spike coding Phase of firing code Population coding Rate codingSparsecoding ...Merge from Correlation coding Independent spike coding Phase of firing code Population coding Rate codingSparsecoding Temporal coding NeuroElectroDynamics discuss Talk Neural coding Merger possibilities date July 2010 Neural coding is a neuroscience related field concerned with how sensory and other ... neural coding is to characterize the relationship between the Stimulus physiology stimulus and the individual ... tlse.fr fr vers documents thorpe sj 90 91.pdf Spike arrival times A highly efficient coding ... and shape , they are typically treated as identical stereotyped events in neural coding studies ... . Cambridge University Press, Cambridge ref . The lengths of interspike intervals Temporal coding ... Reviews Neuroscience 6 389 397 ref . The study of neural coding involves measuring and characterizing ... aspects of that stimulus, from the spike sequences it evokes. Coding schemes A sequence, or train , of spikes may contain information based on different coding schemes. In motor neurons, for example ... States of America 94 12740 12741 ref . Whether neurons use rate coding or temporal coding ... of what these terms mean. Rate coding Rate coding is a traditional coding scheme, assuming that most ... coding is inefficient but highly robust with respect to the ISI noise ref name Stein . The concept of firing ... averaging may be too simplistic to describe brain activity ref name Stein . During rate coding, precisely ... is that it can not be the coding scheme used by neurons in the brain. Neurons can not wait for the stimuli ... repeated runs. Thus, the time dependent firing rate coding relies on the implicit assumption that there are always populations of neurons. Temporal coding Image with unknown copyright status removed ... that precise spike timing is a significant element in neural coding ref name Daniel Daniel A. Butts ... codes ref name Kostal Kostal L, Lansky P, and Rospars JP. 2007. Neuronal coding and spiking randomness ... more details
Orphan date February 2009 Legal coding is the process of creating summary or keyword data from a document. It is widely used in the legal profession to create a fast search index or database of documents for use in litigation . Objective coding Objective coding is creating an index of Objectivity philosophy objective summary data from a document, and if discoverable , would include such information as dates, author, recipient and title. Subjective coding Subjective coding is the indexing of documents around subjectivity subjective data. This may be gleaned from templates, or more usually from a subjective reading by someone familiar with the topic. This is the more realiable way to determine factors such as importance of the document. External links http www.integra india.com litigation services.htm http www.intercomindia.com legal coding objective coding.html http www.intercomindia.com legal coding subjective coding.html http www.cchworkflow.com.au processing services coding.html DEFAULTSORT Legal Coding Category Legal citation Category Document management systems ... more details
Unreferenced date December 2008 Notability Notability date December 2008 Diversity Coding is a particular technique for antenna diversity and is implemented by Space time code space time coding . Category Radio resource management wireless stub ... more details
Userspace draft source ArticleWizard date July 2010 Zen Coding is a set of Plug in computing plug ins for text editors that allow for high speed coding and editing in HTML , XML , Extensible Stylesheet Language XSL , and other structured code formats. The project was created by Sergey Chikuyonok in 2009 and continues to be actively developed by Chikuyonok and Zen Coding users. The tools have been incorporated into a number of high profile text editor s, some plug ins developed by the Zen Coding team and others implemented independently. However, Zen Coding is primarily independent from any text editor, as the engine works directly with text rather than with any particular software. ref http www.smashingmagazine.com 2009 11 21 zen coding a new way to write html code Smashing Magazine Zen Coding A Speedy Way To Write HTML CSS Code ref Zen Coding is an open source code distributed under an MIT License . Functions Expand abbreviations Zen Coding uses a specific syntax in order to expand small snippets of code, similar to CSS selectors, into full fledged HTML code. ref http mondaybynoon.com 2009 08 17 the art of zen coding bringing snippets to a new level Monday By Noon The Art of zen coding ... http www.downloadsquad.com 2010 04 30 if you code html zen coding will change your life Download ... tag for the tag at the current cursor position. Unlike other HTML pair matchers, Zen Coding searches ... 2009 11 21 zen coding a new way to write html code Smashing Magazine Zen Coding ... by the Zen Coding team Eclipse software Aptana Eclipse crossplatform . TextMate Mac . Coda web ... plug ins were developed by third party groups with the official Zen Coding engine Adobe Dreamweaver ... ins were developed independently and with a different Zen Coding engine IntelliJ IDEA WebStorm PHPStorm ... Reflist External links http code.google.com p zen coding Zen Coding Google Project Page http www.emeditor.com modules feature1 rewrite tc 37.html How to Use Zen Coding Category Application programming ... more details
unref date December 2007 Speech coding is the application of data compression of digital audio signals containing speech . Speech coding uses speech specific parameter estimation using audio signal processing techniques to model the speech signal, combined with generic data compression algorithms to represent the resulting modeled parameters in a compact bitstream. The two most important applications of speech coding are mobile telephony and Voice over IP . The techniques used in speech coding are similar to that in audio data compression and audio coding where knowledge in psychoacoustics is used ... coding, only information in the frequency band 400 Hz to 3500 Hz is transmitted but the reconstructed signal is still adequate for intelligibility. Speech coding differs from other forms of audio coding in that speech is a much simpler signal than most other audio signals, and much a lot more ... which is relevant in audio coding can be unnecessary in the speech coding context. In speech coding, the most important criterion is preservation of intelligibility and pleasantness of speech ... speech applications require low coding delay, as long coding delays interfere with speech interaction. Sample companding viewed as a form of speech coding From this viewpoint, the A law algorithm A law ... coding scheme is Code Excited Linear Prediction CELP coding, which is used for example in the GSM ... model. In addition to the actual speech coding of the signal, it is often necessary to use channel coding for transmission, to avoid losses due to transmission errors. Usually, speech coding and channel coding methods have to be chosen in pairs, with the more important bits in the speech data stream protected by more robust channel coding, in order to get the best overall coding results. The Speex .... Major subfields Wide band speech coding AMR WB for WCDMA networks VMR WB for CDMA2000 networks G.722 , G.722.1 , Speex and others for VoIP and videoconferencing Narrow band speech coding FNBDT for military ... more details
Unreferenced auto yes date December 2009 Orphan date August 2006 att June 2008 Cover coding is a technique for obscuring the data that is transmitted over an insecure link, to reduce the risks of snooping. An example of cover coding would be for the sender to perform a bitwise Exclusive or XOR exclusive OR of the original data with a password or random number which is known to both sender and receiver. The resulting cover coded data is then transmitted from sender to the receiver, who uncovers the original data by performing a further bitwise Exclusive or XOR exclusive OR operation on the received data using the same password or random number. DEFAULTSORT Cover Coding Category Cryptography crypto stub ... more details
Encyclopedia
top
