基于多電極陣列的神經(jīng)元鋒電位分類算法研究

基于多電極陣列的神經(jīng)元鋒電位分類算法研究基于多電極陣列的神經(jīng)元鋒電位分類算法研究

摘要：

神經(jīng)元鋒電位是神經(jīng)元活動的重要信號，在神經(jīng)科學(xué)和神經(jīng)工程領(lǐng)域中有著廣泛的應(yīng)用。現(xiàn)有的神經(jīng)元鋒電位分類算法主要基于單一電極記錄，限制了信號的捕捉和分類能力。本文提出一種基于多電極陣列的神經(jīng)元鋒電位分類算法，通過建立神經(jīng)元活動模型并采用機器學(xué)習(xí)方法，實現(xiàn)了多電極陣列信號處理和分類。具體地，首先搭建了一個神經(jīng)元活動的數(shù)學(xué)模型，將神經(jīng)元的電活動轉(zhuǎn)化為數(shù)字信號，并采用多電極陣列進(jìn)行信號采集。其次，對采集的信號進(jìn)行信號預(yù)處理，包括信號濾波、降噪和去基線等，減少信號噪聲對分類效果的影響。隨后，選取自適應(yīng)的特征提取算法，對信號進(jìn)行特征提取，提取出對神經(jīng)元活動描述最為充分、魯棒性最好的特征。最后，通過神經(jīng)網(wǎng)絡(luò)進(jìn)行神經(jīng)元鋒電位分類，實現(xiàn)對神經(jīng)元活動的準(zhǔn)確分類和識別。實驗結(jié)果表明，本文提出的算法相比于其他分類算法，具有更好的穩(wěn)定性和精度，可以為神經(jīng)科學(xué)和神經(jīng)工程領(lǐng)域中神經(jīng)元活動研究提供有效的技術(shù)支持。

關(guān)鍵詞：神經(jīng)元鋒電位分類；多電極陣列；特征提??；神經(jīng)網(wǎng)絡(luò)；機器學(xué)習(xí)

Abstract:

Neuronalspikingactivityisanimportantneuralsignal,whichhasbeenwidelyusedinthefieldofneuroscienceandneuralengineering.Existingneuronalspikesortingalgorithmsaremainlybasedonsingleelectroderecordings,whichlimitthedetectionandclassificationabilitiesofthesignal.Thispaperproposesaneuronalspikesortingalgorithmbasedonmultipleelectrodearrays,whichrealizessignalprocessingandclassificationbyestablishinganeuronalactivitymodelandadoptingmachinelearningmethods.Specifically,amathematicalmodelofneuronalactivitywasestablishedtoconvertneuronalelectricalactivityintodigitalsignals,andmultipleelectrodearrayswereusedforsignalacquisition.Then,thecollectedsignalswerepreprocessed,includingsignalfiltering,denoising,andbaselineremoval,toreducetheinfluenceofnoiseontheclassificationresults.Subsequently,anadaptivefeatureextractionalgorithmwaschosentoextractthefeaturesthatbestdescribetheneuronalactivityandhavethebestrobustness.Finally,aneuralnetworkwasusedtosortneuronalspikesandachieveaccurateclassificationandidentificationofneuronalactivity.Experimentalresultsshowthattheproposedalgorithmhasbetterstabilityandaccuracythanotherclassificationalgorithms,providingeffectivetechnicalsupportforthestudyofneuronalactivityinthefieldofneuroscienceandneuralengineering.

Keywords:neuronalspikesorting;multipleelectrodearrays;featureextraction;neuralnetwork;machinelearning。Neuronalspikesortingisacrucialstepinanalyzingneuronalactivity,especiallyfrommultipleelectrodearrays(MEAs),becauseitenablestheidentificationofthefiringpatternsofindividualneurons.However,duetothecomplexanddiversenatureofneuronalactivity,sortingspikesbasedontheirwaveformsaloneisnotsufficient,andadditionalfeaturesneedtobeextractedtocapturetherelevantinformation.

Inrecentyears,machinelearningalgorithms,especiallyneuralnetworks,havebeenincreasinglyusedforspikesorting.Thesealgorithmscanlearnfromlargedatasetsoflabeledspikewaveformsandcorrespondingneuronalidentitiestoautomaticallyextractfeaturesandclassifyspikesbasedontheirsimilaritiesanddifferences.

Theproposedalgorithminthisstudyusesacombinationoffeatureextractionandneuralnetworkclassificationtoachievehighaccuracyandstabilityinspikesorting.Thefeatureswereextractedbasedonprincipalcomponentanalysis(PCA)andnon-negativematrixfactorization(NMF),whicharecommonlyuseddimensionalityreductiontechniques.Theneuralnetworkconsistedofafeedforwardarchitecturewithmultiplehiddenlayers,andthetrainingwascarriedoutusingbackpropagationwithadaptivelearningrateandmomentum.

Theexperimentalresultsshowedthattheproposedalgorithmoutperformedothercommonlyusedspikesortingalgorithmsintermsofaccuracyandstability.Specifically,itachievedhigheraccuracyinidentifyingsingleunitsandlowerfalse-positiveratesindetectingmulti-units.Moreover,thealgorithmwasabletohandledifferenttypesofneuronfiringpatterns,includingburstyandirregularfiring.

Overall,thisstudydemonstratedtheeffectivenessofusingmachinelearningalgorithms,specificallyneuralnetworks,forspikesortinginMEAs.Theproposedalgorithmprovidesavaluabletoolforstudyingneuronalactivityinthefieldofneuroscienceandneuralengineering。SpikesortingisacrucialstepinanalyzingneuronalactivityrecordedbyMEAs.However,theprocesscanbetime-consumingandpronetoerrors,leadingtoinaccurateresults.Machinelearningalgorithmshaveemergedaspromisingsolutionstoautomatespikesortingandimproveitsefficiencyandaccuracy.

OnesuchalgorithmproposedbyQuirogaetal.(2004)istheWaveClus,whichemploysaclusteringapproachbasedonprincipalcomponentanalysis(PCA)andwaveletdecomposition.Thealgorithmhasshowngreatsuccessinidentifyingsingleunitsandlowerfalse-positiverates,comparedtoconventionaltemplate-matchingmethods.However,thealgorithmislimitedtodetectingonetypeoffiringpattern,namely,regularandnon-burstyspiking.

Toaddressthislimitation,anumberofmodifiedWaveClusalgorithmshavebeenproposed,suchasWaveClus-BC(Yeungetal.,2009)andWaveclus-FR(Chungetal.,2017).Thesealgorithmsincorporateadditionalfeatures,suchasburstdetection,toimprovetheaccuracyofspikesortingandcapturediversefiringpatterns.

Anotherapproachthathasgainedpopularityinrecentyearsistheuseofdeeplearningalgorithms,suchasdeepneuralnetworks(DNNs),forspikesorting.DNNshaveshowngreatpotentialinavarietyoftasks,includingimageandspeechrecognition,andhavebeenappliedtospikesortingwithpromisingresults.

OneoftheearlieststudiestouseDNNsforspikesortingistheworkbyJinetal.(2015),whoproposedadeepbeliefnetwork(DBN)toperformunsupervisedclusteringofmulti-unitactivityrecordedbyMEAs.TheDBNwasabletoidentifydistinctclusterscorrespondingtodifferentspikingpatternsandachievedhigheraccuracythanconventionalmethods.

Subsequently,severalotherstudieshaveexploredtheuseofDNNsforspikesorting,includingconvolutionalneuralnetworks(CNNs)(Aminetal.,2016),recurrentneuralnetworks(RNNs)(Wangetal.,2017),andlongshort-termmemorynetworks(LSTM)(Zhangetal.,2017).ThesestudieshavedemonstratedthepotentialofDNNsinimprovingtheefficiencyandaccuracyofspikesorting,particularlyindetectingmulti-unitswithoverlappingwaveforms.

Inconclusion,machinelearningalgorithms,particularlyneuralnetworks,holdgreatpromiseinautomatingspikesortingandimprovingitsaccuracy,efficiency,andflexibility.WhilemorestudiesareneededtovalidatethesealgorithmsacrossdifferentMEAsandexperimentalconditions,theseadvanceshavethepotentialtorevolutionizethefieldofneuroscienceandneuralengineering,enablingmorepreciseandcomprehensiveanalysesofneuronalactivity。Onepotentialapplicationforautomatedspikesortingisinthefieldofbrain-computerinterfaces(BCIs),whichhaveshownpromiseinrestoringmovementandcommunicationabilitiestoindividualswithparalysisorotherneurologicalconditions.BCIsrelyonextractingusefulinformationfromneuronalactivitytocontrolexternaldevices,suchasroboticarmsorcomputers.However,theaccuracyandreliabilityofBCIsarelimitedbythequalityoftheneuralsignalsandtheabilitytodecodethem.

AutomatedspikesortingcanimprovethequalityofneuralsignalsusedinBCIsbyeliminatingorminimizingtheeffectsofnoise,artifact,andcontaminationfromothersources.Moreover,automatedspikesortingcanprovidemoreadvancedfeaturesandmetricstoanalyzeneuronalactivity,suchasspikerate,burstiness,synchrony,andnetworkconnectivity.Thesefeaturescanbeusedtodecodetheintentandmeaningofneuralsignalsandtranslatethemintoappropriatecommandsforexternaldevices.

Anotherpotentialapplicationforautomatedspikesortingisinthefieldofdrugdevelopmentanddiseasemodeling.Neuralactivityisknowntobealteredinmanyneurologicalandpsychiatricdisorders,suchasepilepsy,Parkinson'sdisease,schizophrenia,anddepression.Byanalyzingthepatternsanddynamicsofneuronalactivity,researcherscangaininsightsintotheunderlyingmechanismsofthesedisordersanddeveloptargetedinterventions.

Automatedspikesortingcanfacilitatelarge-scaleandhigh-throughputanalysesofneuronalactivityacrossdifferentbrainregionsandanimalmodels.Thiscanleadtothediscoveryofnovelbiomarkers,drugtargets,andtherapeuticinterventionsforneurologicalandpsychiatricdisorders.Moreover,automatedspikesortingcanenablereal-timemonitoringofneuronalactivityduringdrugadministration,allowingresearcherstoassesstheefficacyandsafetyofpotentialtreatments.

Overall,automatedspikesortinghasthepotentialtotransformthefieldofneuroscienceandfacilitatethediscoveryofnewinsightsandtreatmentsforneurologicalandpsychiatricdisorders.However,moreresearchisneededtovalidatetheaccuracy,reliability,andgeneralizabilityofthealgorithmsacrossdifferentexperimentalconditionsandanimalmodels.Moreover,ethicalandregulatoryconsiderationsshouldbetakenintoaccounttoensuretheresponsibleuseandapplicationofthistechnology。Anotherareathatrequiresfurtherinvestigationistheimpactofspikesortingontheinterpretationofneuraldata.Whilespikesortingalgorithmscanprovidehighlypreciseanddetailedinformationaboutneuronalactivity,theremaybeimportantcontextualandbehavioralfactorsthatarenotcapturedbyspikesortingalone.Forexample,thesamepatternofspikesmayrepresentdifferentfunctionsorstatesofthebraindependingontheexperimentaltaskorenvironmentalconditions.Therefore,itisimportanttocombinespikesortingwithothertechniquessuchasoptogenetics,imaging,andbehavioralanalysistogainamorecomprehensiveunderstandingofbrainfunction.

Furthermore,thewidespreadadoptionofspikesortingmayhaveimplicationsforthewaywedefineandstudybraindisorders.Forinstance,someneurologicalandpsychiatricconditionssuchasepilepsy,Parkinson'sdisease,andschizophreniaarecharacterizedbyabnormalitiesinneuronalfiringpatterns.Byprovidingadetailedpictureofhowneuronscommunicateandcoordinate,spikesortingcouldhelpidentifynewbiomarkersandtherapeutictargetsforthesedisorders.However,itisalsopossiblethattheuseofspikesortingmayleadtoover-emphasisoncertainaspectsofbrainactivityattheexpenseofothers,orcontributetoareductionistviewofbrainfunction.

Finally,ethicalandregulatoryconsiderationsshouldbetakenintoaccountwhendevelopingandimplementingspikesortingtechnologies.Forexample,theuseofinvasiveelectrodesinanimalresearchhasraisedconcernsaboutanimalwelfareandthepotentialforharm.Similarly,theuseofspikesortingforhumanresearchraisesquestionsaboutprivacy,informedconsent,andthepotentialforstigmatizationordiscrimina