基于多电极阵列的神经元锋电位分类算法研究

基于多电极阵列的神经元锋电位分类算法研究基于多电极阵列的神经元锋电位分类算法研究

摘要：

神经元锋电位是神经元活动的重要信号，在神经科学和神经工程领域中有着广泛的应用。现有的神经元锋电位分类算法主要基于单一电极记录，限制了信号的捕捉和分类能力。本文提出一种基于多电极阵列的神经元锋电位分类算法，通过建立神经元活动模型并采用机器学习方法，实现了多电极阵列信号处理和分类。具体地，首先搭建了一个神经元活动的数学模型，将神经元的电活动转化为数字信号，并采用多电极阵列进行信号采集。其次，对采集的信号进行信号预处理，包括信号滤波、降噪和去基线等，减少信号噪声对分类效果的影响。随后，选取自适应的特征提取算法，对信号进行特征提取，提取出对神经元活动描述最为充分、鲁棒性最好的特征。最后，通过神经网络进行神经元锋电位分类，实现对神经元活动的准确分类和识别。实验结果表明，本文提出的算法相比于其他分类算法，具有更好的稳定性和精度，可以为神经科学和神经工程领域中神经元活动研究提供有效的技术支持。

关键词：神经元锋电位分类；多电极阵列；特征提取；神经网络；机器学习

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