Using deep learning to probe the neural code for images in primary visual cortex

William F. Kindel; Elijah D. Christensen; Joel Zylberberg

摘要

Primary visual cortex (V1) is the first stage of cortical image processing, and major effort in systems neuroscience is devoted to understanding how it encodes information about visual stimuli. Within V1, many neurons respond selectively to edges of a given preferred orientation: These are known as either simple or complex cells. Other neurons respond to localized center–surround image features. Still others respond selectively to certain image stimuli, but the specific features that excite them are unknown. Moreover, even for the simple and complex cells—the best-understood V1 neurons—it is challenging to predict how they will respond to natural image stimuli. Thus, there are important gaps in our understanding of how V1 encodes images. To fill this gap, we trained deep convolutional neural networks to predict the firing rates of V1 neurons in response to natural image stimuli, and we find that the predicted firing rates are highly correlated ( Display Formula (defupalpha{unicode[Times]{x3B1}})(defupbeta{unicode[Times]{x3B2}})(defupgamma{unicode[Times]{x3B3}})(defupdelta{unicode[Times]{x3B4}})(defupvarepsilon{unicode[Times]{x3B5}})(defupzeta{unicode[Times]{x3B6}})(defupeta{unicode[Times]{x3B7}})(defuptheta{unicode[Times]{x3B8}})(defupiota{unicode[Times]{x3B9}})(defupkappa{unicode[Times]{x3BA}})(defuplambda{unicode[Times]{x3BB}})(defupmu{unicode[Times]{x3BC}})(defupnu{unicode[Times]{x3BD}})(defupxi{unicode[Times]{x3BE}})(defupomicron{unicode[Times]{x3BF}})(defuppi{unicode[Times]{x3C0}})(defuprho{unicode[Times]{x3C1}})(defupsigma{unicode[Times]{x3C3}})(defuptau{unicode[Times]{x3C4}})(defupupsilon{unicode[Times]{x3C5}})(defupphi{unicode[Times]{x3C6}})(defupchi{unicode[Times]{x3C7}})(defuppsy{unicode[Times]{x3C8}})(defupomega{unicode[Times]{x3C9}})(defialpha{oldsymbol{lpha}})(defibeta{oldsymbol{eta}})(defigamma{oldsymbol{gamma}})(defidelta{oldsymbol{delta}})(defivarepsilon{oldsymbol{arepsilon}})(defizeta{oldsymbol{zeta}})(defieta{oldsymbol{eta}})(defitheta{oldsymbol{heta}})(defiiota{oldsymbol{iota}})(defikappa{oldsymbol{kappa}})(defilambda{oldsymbol{lambda}})(defimu{oldsymbol{mu}})(definu{oldsymbol{u}})(defixi{oldsymbol{xi}})(defiomicron{oldsymbol{micron}})(defipi{oldsymbol{pi}})(defirho{oldsymbol{ho}})(defisigma{oldsymbol{sigma}})(defitau{oldsymbol{au}})(defiupsilon{oldsymbol{upsilon}})(defiphi{oldsymbol{phi}})(defichi{oldsymbol{chi}})(defipsy{oldsymbol{psy}})(defiomega{oldsymbol{omega}})(defupalpha{unicode[Times]{x1D6C2}})(defupbeta{unicode[Times]{x1D6C3}})(defupgamma{unicode[Times]{x1D6C4}})(defupdelta{unicode[Times]{x1D6C5}})(defupepsilon{unicode[Times]{x1D6C6}})(defupvarepsilon{unicode[Times]{x1D6DC}})(defupzeta{unicode[Times]{x1D6C7}})(defupeta{unicode[Times]{x1D6C8}})(defuptheta{unicode[Times]{x1D6C9}})(defupiota{unicode[Times]{x1D6CA}})(defupkappa{unicode[Times]{x1D6CB}})(defuplambda{unicode[Times]{x1D6CC}})(defupmu{unicode[Times]{x1D6CD}})(defupnu{unicode[Times]{x1D6CE}})(defupxi{unicode[Times]{x1D6CF}})(defupomicron{unicode[Times]{x1D6D0}})(defuppi{unicode[Times]{x1D6D1}})(defuprho{unicode[Times]{x1D6D2}})(defupsigma{unicode[Times]{x1D6D4}})(defuptau{unicode[Times]{x1D6D5}})(defupupsilon{unicode[Times]{x1D6D6}})(defupphi{unicode[Times]{x1D6D7}})(defupchi{unicode[Times]{x1D6D8}})(defuppsy{unicode[Times]{x1D6D9}})(defupomega{unicode[Times]{x1D6DA}})(defupvartheta{unicode[Times]{x1D6DD}})(defGamma{f{Gamma}})(defDelta{f{Delta}})(defTheta{f{Theta}})(defLambda{f{Lambda}})(defXi{f{Xi}})(defPi{f{Pi}})(defSigma{f{Sigma}})(defUpsilon{f{Upsilon}})(defPhi{f{Phi}})(defPsi{f{Psi}})(defOmega{f{Omega}})(defiGamma{unicode[Times]{x1D6E4}})(defiDelta{unicode[Times]{x1D6E5}})(defiTheta{unicode[Times]{x1D6E9}})(defiLambda{unicode[Times]{x1D6EC}})(defiXi{unicode[Times]{x1D6EF}})(defiPi{unicode[Times]{x1D6F1}})(defiSigma{unicode[Times]{x1D6F4}})(defiUpsilon{unicode[Times]{x1D6F6}})(defiPhi{unicode[Times]{x1D6F7}})(defiPsi{unicode[Times]{x1D6F9}})(defiOmega{unicode[Times]{x1D6FA}})(defiGamma{unicode[Times]{x1D71E}})(defiDelta{unicode[Times]{x1D71F}})(defiTheta{unicode[Times]{x1D723}})(defiLambda{unicode[Times]{x1D726}})(defiXi{unicode[Times]{x1D729}})(defiPi{unicode[Times]{x1D72B}})(defiSigma{unicode[Times]{x1D72E}})(defiUpsilon{unicode[Times]{x1D730}})(defiPhi{unicode[Times]{x1D731}})(defiPsi{unicode[Times]{x1D733}})(defiOmega{unicode[Times]{x1D734}})

机译：初级视觉皮层（V1）是皮质图像处理的第一阶段，系统神经科学方面的主要工作致力于理解其如何编码有关视觉刺激的信息。在V1内，许多神经元选择性地响应给定首选方向的边缘：这些被称为简单或复杂细胞。其他神经元对局部中心-周围图像特征作出反应。还有一些选择性地对某些图像刺激作出反应，但是激发它们的具体特征尚不清楚。此外，即使对于简单和复杂的细胞-最好理解的V1神经元-预测它们对自然图像刺激的反应也具有挑战性。因此，在我们对V1如何编码图像的理解上存在重大差距。为了填补这一空白，我们训练了深度卷积神经网络来预测V1神经元对自然图像刺激的激发速率，并且发现预测的激发速率高度相关（显示公式（ def upalpha { unicode [ Times] {x3B1}} ）（ def upbeta { unicode [Times] {x3B2}} ）（ def upgamma { unicode [Times] {x3B3}} def updelta { unicode [Times] {x3B4}} ）（ def upvarepsilon { unicode [Times] {x3B5}} ）（ def upzeta { unicode [Times {x3B6}}} ）（ def upeta { unicode [Times] {x3B7}} ）（ def uptheta { unicode [Times] {x3B8}} ）（ def upiota { unicode [Times] {x3B9}} ）（ def upkappa { unicode [Times] {x3BA}} ）（ def uplambda { unicode [Times] {x3BB}} def upmu { unicode [Times] {x3BC}} ）（ def upnu { unicode [Times] {x3BD}} ）（ def upxi { unicode [Times] {x3BE}} ）（ def upomicron { unicode [Times] {x3BF}} ）（ def uppi { unicode [Times] {x3C0}} ）（ def uprho { unicode [Times] {x3C1}} ）（ def upsigma { unicode [Times] {x3C3}} ）（ def uptau { unicode [Times] {x3C4}} ）（ def upupsilon { unicode [Times {x3C5}}} ）（ def upphi { unicode [Times] {x3C6}} ）（ def upchi { unicode [Times] {x3C7}} ）（ def uppsy { unicode [Times {x3C8}}} ）（ def upomega { unicode [Times] {x3C9}} ）（ def bialpha { boldsymbol { alpha}} ）（ def bibeta { boldsymbol { beta}} ）（ def bigamma { boldsymbol { gamma}} ）（ def bidelta { boldsymbol { delta}} ）（ def bivarepsilon { boldsymbol { varepsilon}} ）（ def bizeta { boldsymbol { zeta}} } ）（ def bieta { boldsymbol { eta}} ）（ def bitheta { boldsymbol { theta}} ）（ def biiota { boldsymbol { iota}} ）（ def bikappa { boldsymbol { kappa}} ）（ def bilambda { boldsymbol { lambda}} ）（ def bimu { boldsymbol { mu}} ）（ def binu { boldsymbol { nu}} ））（ def bixi { boldsymbol { xi}} ））（ def biomicron { boldsymbol { micron}} ）（ def bipi { boldsymbol { pi}} ）（ def birho { boldsymbol { rho}} def bisigma { boldsymbol { sigma}} ）（ def bitau { boldsymbol { tau}} ）（ def biupsilon { boldsymbol { upsilon}} ）（ def biphi { boldsymbol { phi}} ）（ def bichi { boldsymbol { chi}} ）（ def bipsy { boldsymbol { psy}}} ）（ def biomega { boldsymbol { omega} } ）（ def bupalpha { unicode [Times] {x1D6C2}} ）（ def bupbeta { unicode [Times {x1D6C3}}} ）（ def bupgamma { unicode [Times] {x1D6C4 }} ）（ def bupdelta { unicode [Times] {x1D6C5}} （ def bupepsilon { unicode [Times] {x1D6C6}} ）（ def bupvarepsilon { unicode [ Times] {x1D6DC}} ）（ def bupzeta { unicode [Times] {x1D6C7}} ）（ def bupeta { unicode [Times {x1D6C8}}} ）（ def buptheta { unicode [Times] {x1D6C9}} ）（ def bupiota { unicode [Times] {x1D6CA}} ）（ def bupkappa { unicode [Times {x1D6CB}} ））（ def buplambda { unicode [Times] {x1D6CC}} ）（ def bupmu { unicode [Times] {x1D6CD}} ）（ def bupnu { unicode [Times] {x1D6CE}} ）（ def bupxi { unicode [Times] {x1D6CF}} ）（ def bupomicron { unicode [Times] {x1D6D0}} ）（ def buppi { unicode [Times] {x1D6D1}} ）（ def buprho { unicode [Times] {x1D6D2}} ）（ def bupsigma { unicode [Times] {x1D6D4}} ）（ def buptau { unicode [Times] {x1D6D5}} ）（ def bupupsilon { unicode [Times] {x1D6D6}} ）（ def bupphi { unicode [Times {x1D6D7}}} ））（ def bupchi { unicode [Times] {x1D6D8}} ）（ def buppsy { unicode [Times] {x1D6D9}} ）（ def bupomega { unicode [Times] {x1D6DA}} ）（ def bupvartheta { unicode [Times {x1D6DD}} ）（ def bGamma { bf { Gamma}} ）（ def bDelta { bf { Delta}} ）（ def bTheta { bf { Theta}} ）（ def bLambda { bf { Lambda}} ）（ def bXi { bf { Xi}} ）（ def bPi { bf { Pi}} def bSigma { bf { Sigma}} ）（ def bUpsilon { bf { Upsilon}} ）（ def bPhi { bf { Phi}} ）（ def bPsi { bf { Psi}}} ）（ def bOmega { bf { Omega}} ）（ def iGamma { unicode [Times} {x1D6E4}}} ）（ def iDelta { unicode [Times] {x1D6E5}} ）（ def iTheta { unicode [时间] {x1D6E9}} ）（ def iLambda { unicode [时间] {x1D6EC}} ）（ def iXi { unicode [时间] {x1D6EF}} ））（ def iPi { unicode [Times] {x1D6F1}} ）（ def iSigma { unicode [Times] {x1D6F4}} ）（ def iUpsilon { unicode [Times] {x1D6F6}} ）（ def iPhi { unicode [Times] {x1D6F7}} ）（ def iPsi { unicode [Times] {x1D6F9}} ）（ def iOmega { unicode [Times] { x1D6FA}} ）（ def biGamma { unicode [Times] {x1D71E}} ）（ def biDelta { unicode [Times] {x1D71F}} ）（ def biTheta { unicode [Times] {x1D723}} ）（ def biLambda { unicode [Times] {x1D726}} ）（ def biXi { unicode [Times] { x1D729}} ）（ def biPi { unicode [Times] {x1D72B}} ）（ def biSigma { unicode [Times] {x1D72E}} ））（ def biUpsilon { unicode [Times] {x1D730}} ）（ def biPhi { unicode [Times] {x1D731}} ）（ def biPsi { unicode [Times] {x1D733}} ）（ def biOmega { unicode [Times] {x1D734}} ）

Using deep learning to probe the neural code for images in primary visual cortex

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