Merge to Neural coding discuss Talk Neural coding Merger possibilities date July 2010 The sparse code ... coding from Olshausen et al. is that sparsecoding of natural images produces wavelet like oriented ... a potentially large set of input patterns, sparsecoding algorithms attempt to automatically find a small ... input patterns. The sparsecoding for the input then consists of those representative patterns ..., and so a sparsecoding for English would be those symbols. Linear Generative Model Most models of sparsecoding are based on the linear generative model. ref name Rehn cite journal first1 Martin ... numbered input vectors math vec xi in mathbb R k math , the goal of sparsecoding is to determine ... Alexis last3 Raina first3 Rajat last4 Ng first4 Andrew Y. title Efficient sparsecoding algorithms ... Olhausen first2 David J. last2 Field title SparseCoding with an Overcomplete Basis Set A Strategy ... a 196 dimensional space is coded by roughly 100,000 neurons. ref name Rehn See also Rate coding Independent spike coding Correlation coding Population coding Grandmother cell References references Bibliography F ldi k P, Endres D, http www.scholarpedia.org article SparseCodingSparsecoding ... along with a Sparse vector sparse n dimensional vector of weights or coefficients math vec s in mathbb ... These refer to the distribution of basis vector coefficients for typical inputs. A coding with soft .... ref name Rehn Another measure of coding is whether it is critically complete or overcomplete . If the number of basis vectors n is equal to the dimensionality k of the input set, the coding is said ... in the coefficients, and the coding is not able to gracefully handle small scalings, small translations ... of the input set, the coding is overcomplete . Overcomplete codings smoothly interpolate between ... 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 Torvalds developer Josh Triplett, Christopher Li released 2003 latest release version 0.4.4 latest release date release date 2011 11 25 ref cite mailing list title Sparse 0.4.4 released author Christopher Li date 2011 11 25 mailinglist linux sparse url http www.spinics.net lists linux sparse msg02592.html ... 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 ... C code. Sparse is a library that, like a compiler front end, provides convenient access to the abstract ... address space. copy edit section date October 2011 Sparse contains built in checks for known problematic ... 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 ... accessdate 2010 11 13 ref Sparse defines the following list of attributes tt address space num tt tt ..., so Sparse will give a warning if variables of these types or other integer variables are mixed ... restricted types, a casting with the tt force tt attribute is used to avoid Sparse giving a warning ... 01 thesis.pdf oclc 665146513 External 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 ... more details
wiktionary codingCoding may refer to Channel coding in coding theory Line coding Computer programming , the process of designing, writing, testing, debugging troubleshooting, and maintaining the source code of computer programs The process of Statistical classification of information Coding social sciences , refers to an analytical process in which data, in both quantitative form such as questionnaires results or qualitative such as interview transcripts are categorised to facilitate analysis Coding therapy , a controversial therapy used to treat addictions Legal coding , 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 A coding strand of DNA is translated into a protein product Present progressive tense for Code Blue emergency code Code Blue , which is a patient in Cardiac Arrest or Respiratory Arrest See also Code , a rule for converting a piece of information for example, a letter, word, phrase, or gesture into another form or representation one sign into another sign , not necessarily of the same type Entropy encoding , a lossless data compression scheme that is independent of the specific characteristics of the medium Source coding Medical coding disambiguation ... 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
images, and groups can represent category of objects. Collaborative sparsecoding The original ..., a single point can have more than one sparse representation with similar data fitting errors. In the collaborative sparsecoding model, more than one observation of the same point is available. Hence ... There are several other methods for solving sparse decomposition problems ref cite web url http www.di.ens.fr mairal tutorial iccv09 tuto part1.pdf title SparseCoding and Dictionary Learning for Image ...Sparse approximation also referred to as sparse decomposition is the problem of estimating a Sparsity sparse multi dimensional vector, satisfying a System of linear equations linear system of equations given high dimensional observed data and a design matrix . Sparse approximation techniques have found wide use in applications such as image processing, audio processing, biology, and document analysis. Sparse decomposition Noiseless observations Consider a System of linear equations linear system of equations math x D alpha math , where math D math is an math m times p math matrix mathematics matrix math m ll p math and math x in mathbb R m, alpha in mathbb R p math . math D math , called as the dictionary or the design matrix, is given. The problem is to estimate the signal math alpha math , subject to the constraint that it is sparse. The underlying motivation for sparse decomposition problems is that even though the observed values are in high dimensional math m math space, the actual ... , the basis vectors are not required to be orthogonal. The sparse decomposition problem is represented ..., the sparse decomposition problem is given by, math min alpha in mathbb R p frac 1 2 x D alpha ..., and employing a sparse solution. Variations There are several variations to the basic sparse approximation ... is to represent math x math such that it is sparse in the number of groups selected. Such groups appear ... There are several algorithms that have been developed for solving sparse approximation problem. Matching ... 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 ... 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 ... size in blocks too ls lks sparse file Or use tt h tt to print both in human readable format ... that it can be compared to the tt ls tt output du block size 1 sparse file ls l sparse file Copying Normally, the GNU version of cp Unix 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 code sparse ... GNU cp accept 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 can be recovered by doing cp sparse always formerly sparse file ... more details
In mathematics , 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 Linear algebra stub cs dk vektor pl Wektor rzadki ... 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 or not , indeed Representation Sparse Array can be represented as Sparse Array pos1 val1, pos2 val2,... or br Sparse Array pos1, pos2,... val1, val2,... which yields a sparse array in which values math val i math appear at positions math pos i math . Sparse Array as Linked List An obvious question that might be asked is why we need a linked list to represent a sparse array if we can represent ... a sparse array as a normal array, a lot of space is allocated for zero or null elements. For example ... will require 8 million bytes of memory. Because this is a sparse array, most of its elements ... array. br A sparse array as a linked list contains nodes linked to each other. In a one dimensional sparse array, each node consist of an index position of the non zero element and the value ... as per the index. In the case of a two dimensional sparse array, each node contains a row index, a column ... node. br 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 ... more details
Sparse grids are numerical techniques 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 1 l left Q i 1 Q i 1 1 right otimes Q l i 1 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
Sparse PCA sparse principal component analysis is a specialised technique used in statistical analysis and, in particular, in the analysis of multivariate data set s. Ordinary principal component analysis PCA uses a vector space transform used to reduce multidimensional data sets 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 Systems 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, Journal of Machine Learning Research 11 2010 517 553 url http jmlr.csail.mit.edu papers volume11 journee10a journee10a.pdf arxiv 0811.4724 ... cite journal author Baback Moghaddam, Yair Weiss, Shai Avidan year 2005 title Spectral Bounds for Sparse .... ref cite journal author Yue Guan, Jennifer Dy year 2009 title Sparse Probabilistic Principal ... 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 native data structure for a matrix is a two dimensional array. Each ... code tuple s to values. This format is good for incrementally constructing a sparse array, but poor ... generated scipy.sparse.coo matrix.html scipy.sparse.coo matrix . Yale format The Yale Sparse Matrix Format stores an initial sparse m n matrix, M , in row form using three one dimensional arrays. Let ... NNZ m n 1 1 2 math . 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 ... scipy.sparse.csr matrix . Compressed sparse column CSC or CCS http netlib.org linalg html templates ... a sparse matrix in MATLAB via the code sparse code function . Example A bitmap image having ... as a sparse matrix that contains only row and column numbers for pixels with the non dominant color. Band matrix main Band matrix An important special type of sparse matrices is band matrix , defined ... has lower bandwidth 1 and upper bandwidth 1. As another example, the following sparse matrix has lower ... as band matrices and often lend themselves to simpler algorithms than general sparse matrices or one ... more details
Refimprove date March 2011 Infobox file format name Sparse Disk Image logo icon iconcaption screenshot caption extension .sparseimage mime type code uniform type com.apple.disk image sparse conforms to com.apple.disk image magic owner Apple Inc. released Start date and age YYYY mm dd df yes no latest release version latest release date Start date and age YYYY mm dd df yes no genre disk image container for file system objects contained by extended from extended to standard free url 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 the FileVault feature in Mac OS X Snow Leopard and earlier. 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 ... code man hdiutil code Sparse bundle Mac OS X v10.5 Leopard introduced the concept of the sparse ... FileVault and Sparse Bundle Backups in Leopard ref Instead of a monolithic file, a sparse bundle is a Bundle ... 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 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 Yngve Zotterman 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 Neural coding 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
postscript none ref . Contrast this with sparsecoding . See also Rate coding Temporal codingSparsecoding Independent spike coding Neural coding References Dayan P & Abbott LF. Theoretical ...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 towards a lit target. ref http homepage.psy.utexas.edu homepage class psy394U hayhoe IntroSensoryMotorSystems week6 Ch38.pdf Intro to Sensory Motor Systems Ch. 38 page 766 ref ref Science. 1986 Sep 26 233 4771 1416 9 ref They found that a single neuron would fire for multiple target directions. However it would fire fastest for one direction and more slowly depending on how close the target was to the neuron s 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 ... in the set of neurons. The Johnson Georgopoulos vector coding is an example of simple averaging ... Category Neural coding ... more details
Correlation coding Independent spike coding Phase of firing code Population coding Rate codingSparse ...Merge from Correlation coding Independent spike coding Phase of firing code Population coding Rate codingSparsecoding Temporal coding NeuroElectroDynamics Neural ensemble discuss Talk Neural coding Merger possibilities date July 2010 Neural coding is a neuroscience related field concerned with how ... goal of studying neural coding is to characterize the relationship between the Stimulus physiology ... A highly efficient coding scheme for neural networks , SJ Thorpe Parallel processing in neural systems ... in neural coding studies. If the brief duration of an action potential about 1ms is ignored, an action ... intervals Temporal coding ISI s between two successive spikes in a spike train often vary, apparently ... or part of the signal? Nature Reviews Neuroscience 6 389 397 ref The study of neural coding involves ... a stimulus, or certain 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 ... coding or temporal coding is a topic of intense debate within the neuroscience community, even though there is no clear definition of what these terms mean. Rate coding Rate coding is a traditional coding .... Consequently, rate coding is inefficient but highly robust with respect to the ISI noise . ref ... name Stein During rate coding, precisely calculating firing rate is very important. In fact, the term ... over time or an average over several repetitions of experiment. In rate coding, learning ... stimuli. The obvious problem with this approach is that it can not be the coding scheme used by neurons ... from a single neuron and average over N repeated runs. Thus, the time dependent firing rate coding relies on the implicit assumption that there are always populations of neurons. Temporal coding ... element in neural coding. ref name thorpe ref name Daniel Daniel A. Butts, Chong Weng ... more details
alike. See also Sparsecoding Independent spike coding Correlation coding Population coding ...Merge to Neural coding discuss Talk Neural coding Merger possibilities date July 2010 Temporal coding is a type of neural coding in which a neuron encodes information through the precise timing of action ... coding almost any coding scheme that is not neural coding Rate coding rate coding may be referred to as a temporal ... within a localized area. The latter is sometimes referred to as correlation coding . ref Dayan P ... the idea of rate coding and temporal coding. However, many systems of the body utilize a more complex coding system than could be considered reasonable for a rate code. Recent research suggests that the more ... of firing rates which could either be noise or carry information. Rate coding models suggest ... in Systems Neuroscience. Oxford Univ. Press, 2006. p.143 158. ref The theory of temporal coding offers ... to mark the spikes 1 for a spike, 0 for no spike. Temporal coding allows the sequence 000111000111 ... neural coding is a complex process. Because it is unclear when a neuron begins encoding a stimulus .... ref name Current Opinion in Neurobiology cite web last Singer first Wolf title Time as coding ... coding for different stimuli, then a neuron trying to discriminate these two stimuli may need to wait ... The mammalian gustatory system is useful for studying temporal coding because of the fairly distinct ... M. Di Lorenzo. 2006 . http dx.doi.org 10.1016 j.neubiorev.2006.07.005 Temporal coding in the gustatory ... between two bitter tastants, such as quinine and denatonium . In this way, both rate coding and temporal coding may be used in the gustatory system rate for basic tastant type, temporal for more specific ... 10.1016 j.tins.2010.04.002 Coding in the mammalian gustatory system . Trends in Neurosciences , 33 ... than that which is determined by rate coding schemes. Groups of neurons may synchronize in response ... situations. For this reason, temporal coding may play a part in coding defined edges rather than ... 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
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 Please do not add links for coding companies, Wikipedia is not for commercial promotion, and these links will be removed thank you 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
cleanup date May 2011 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 ... 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 Bringing Snippets to a New ... www.downloadsquad.com 2010 04 30 if you code html zen coding will change your life Download Squad Review ... at the current cursor position. Unlike other HTML pair matchers, Zen Coding searches from the cursors ... 2009 11 21 zen coding a new way to write html code Smashing Magazine Zen Coding A Speedy ... Coding team Eclipse software Aptana Eclipse cross platform . TextMate Mac . Coda web development ... with the official Zen Coding engine Adobe Dreamweaver Dreamweaver Windows, Mac AkelPad Windows Sublime ... Zen Coding engine Emacs cross platform IntelliJ IDEA WebStorm PHPStorm cross platform ... 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 interfaces ko ru Zen Coding ... more details
unref date December 2007 essay date November 2011 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 voiceband speech 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 ..., some auditory information 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 ... 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 and law ... speech coding scheme is Code Excited Linear Prediction CELP coding, which is used for example in the GSM ... predictive 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 .... 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 ... more details
Universal Coding can refer to one of two concepts in data compression Universal code data compression , a fixed prefix code that, for any probability mass function , has a data compression ratio within a constant of the optimal prefix code Universal source coding , a data compression method that limit mathematics asymptotically approaches the data compression ratio of the optimal data compression method, e.g., LZ77 and LZ78 disambig ... more details
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