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How Close Are We to Understanding V1?
, 2005
"... A wide variety of papers have reviewed what is known about the function of primary visual cortex. In this review, rather than stating what is known, we attempt to estimate how much is still unknown about V1 function. In particular, we identify five problems with the current view of V1 that stem larg ..."
Abstract

Cited by 51 (1 self)
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A wide variety of papers have reviewed what is known about the function of primary visual cortex. In this review, rather than stating what is known, we attempt to estimate how much is still unknown about V1 function. In particular, we identify five problems with the current view of V1 that stem largely from experimental and theoretical biases, in addition to the contributions of nonlinearities in the cortex that are not well understood. Our purpose is to open the door to new theories, a number of which we describe, along with some proposals for testing them.
Sequential optimal design of neurophysiology experiments
, 2008
"... Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are hi ..."
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Cited by 42 (8 self)
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Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are high dimensional systems, optimizing neurophysiology experiments requires computing highdimensional integrations and optimizations in real time. Here we present a fast algorithm for choosing the most informative stimulus by maximizing the mutual information between the data and the unknown parameters of a generalized linear model (GLM) which we want to fit to the neuron’s activity. We rely on important logconcavity and asymptotic normality properties of the posterior to facilitate the required computations. Our algorithm requires only lowrank matrix manipulations and a 2dimensional search to choose the optimal stimulus. The average running time of these operations scales quadratically with the dimensionality of the GLM, making realtime adaptive experimental design feasible even for highdimensional stimulus and parameter spaces. For example, we
ARTICLE Communicated by Christian Machens Sequential Optimal Design of Neurophysiology Experiments
"... Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are hi ..."
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Adaptively optimizing experiments has the potential to significantly reduce the number of trials needed to build parametric statistical models of neural systems. However, application of adaptive methods to neurophysiology has been limited by severe computational challenges. Since most neurons are highdimensional systems, optimizing neurophysiology experiments requires computing highdimensional integrations and optimizations in real time. Here we present a fast algorithm for choosing the most informative stimulus by maximizing the mutual information between the data and the unknown parameters of a generalized linear model (GLM) that we want to fit to the neuron’s activity. We rely on important log concavity and asymptotic normality properties of the posterior to facilitate the required computations. Our algorithm requires only lowrank matrix manipulations and a twodimensional search to choose the optimal stimulus. The average running time of these operations scales quadratically with the dimensionality of the GLM, making realtime
Neural Control: ClosedLoop Human
"... Closedloop experimental testing of single medial temporal lobe neurons in humans reveals topdown effects, opening new possibilities for describing neural representations at the highest level. ..."
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Closedloop experimental testing of single medial temporal lobe neurons in humans reveals topdown effects, opening new possibilities for describing neural representations at the highest level.
Increment threshold
"... This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or sel ..."
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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal noncommercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: