MARC record from Internet Archive

LEADER: 04450cam 2200625Ma 4500
001 ocm42274482
003 OCoLC
005 20210930181910.0
008 990824r19991996maua b 001 0 eng
040 $aUKM$beng$cUKM$dBAKER$dDEBBG$dOCLCQ$dBDX$dOCLCF$dOCLCQ$dOCLCO$dOCLCQ$dIPL$dGZH$dOCLCO$dOCLCA$dUKMGB$dOCLCA$dU9X$dOCLCQ
015 $aGB99W1937$2bnb
016 7 $a009703400$2Uk
020 $a0262681080
020 $a9780262681087
020 $a0262181746
020 $a9780262181747
035 $a(OCoLC)42274482
050 4 $aQP364.5$b.S66 1999
082 04 $a612.8$221
084 $aPHY 825f$2stub
084 $aST 300$2rvk
084 $aWW 4120$2rvk
245 00 $aSpikes :$bexploring the neural code /$cFred Rieke [and others].
260 $aCambridge, Mass. ;$aLondon :$bMIT,$c1999.
300 $a416 pages :$billustrations ;$c23 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
500 $aOriginally published: 1996.
504 $aIncludes bibliographical references and index.
505 00 $tThe classical results --$tDefining the problem --$tCentral claims of this book --$tFoundations --$tCharacterizing the neural response --$tProbabilistic responses and Bayes' rule --$tRates, intervals, and correlations --$tInput/output analysis --$tModels for firing statistics --$tTaking the organism's point of view --$tIntervals in the signal and intervals between spikes --$tWhat can small numbers of spikes tell the braind --$tResponse-conditional ensembles --$tReading the code --$tWhy it might work --$tAn experimental strategy --$tQualitative features of a first test --$tQuantifying information transmission --$tWhy information theory? --$tEntropy and available information --$tEntropy of spike trains --$tMutual information and the Gaussian channel --$tTime dependent signals --$tInformation transmission with spike trains --$tCan we really "measure" information transmission? --$tInformation transmission with discrete stimuli --$tStimulus reconstruction and information rates --$tEntropy and information with continuous stimuli --$tMechanical sensors in the cricket cercal system --$tAmphibian eyes and ears --$tFrogs and frog calls --$tReliability of computation --$tReliability of neurons and reliability of perception --$tHistorical background --$tPhoton counting --$tAuditory discrimination --$tMotion discrimination in monkey vision --$tHyperacuity --$tWhere is the limit? --$tExperiments with single neurons --$tTemporal hyperacuity --$tMotion processing in the fly visual system --$tLimits to discrimination --$tDiscrimination experiments with H1 --$tContinuous estimation.
505 80 $tDirections --$tArrays of neurons --$tNatural signals --$tOptimal coding and computation --$tEpilogue: Homage to the single spike --$tMathematical asides --$tRates as expectation values --$tTwo-point functions --$tWiener kernels --$tPoisson model I --$tPoisson model II --$tEstimation from independent responses --$tConditional mean as optimal estimator --$tPractical calculations of reconstruction filters --$tThe "acausal-shifted" calculation --$tPower series expansions of the K[subscript n] --$tEntropy of Gaussian distributions --$tApproximating the entropy of spike trains --$tMaximum entropy and spike counts --$tThe Gaussian channel --$tGaussians and maximum entropy --$tWiener-Khinchine theorem --$tMaximizing information transmission --$tMaximum likelihood --$tPoisson averages --$tSignal to noise ratios with white noise --$tOptimal filters.
650 0 $aSensory neurons.
650 0 $aNeural transmission.
650 2 $aSensory Receptor Cells.
650 2 $aSynaptic Transmission.
650 7 $aNeural transmission.$2fast$0(OCoLC)fst01036280
650 7 $aSensory neurons.$2fast$0(OCoLC)fst01112619
650 7 $aCodierung$2gnd
650 7 $aModell$2gnd
650 7 $aNervenerregung$2gnd
650 7 $aNervennetz$2gnd
650 7 $aSignaltransduktion$2gnd
700 1 $aRieke, Fred.
830 0 $aBradford book.
830 0 $aComputational neuroscience.
938 $aBaker & Taylor$bBKTY$c40.00$d40.00$i0262681080$n0003305724$sactive
938 $aBrodart$bBROD$n54114012$c$25.00
029 1 $aAU@$b000020755790
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029 1 $aHEBIS$b107960699
029 1 $aHEBIS$b227817109
029 1 $aUKMGB$b009703400
994 $aZ0$bP4A
948 $hNO HOLDINGS IN P4A - 75 OTHER HOLDINGS