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There are two basic problems in the statistical analysis of neural data. The "encoding" problem concerns how information is encoded in neural spike trains: can we predict the spike trains of a neuron (or population of neurons), given an arbitrary stimulus or observed motor response? Conversely, the "decoding" problem concerns how much information is in a(More)
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(More)
Adaptively optimizing experiments can significantly reduce the number of trials needed to characterize neural responses using parametric statistical models. However , the potential for these methods has been limited to date by severe computational challenges: choosing the stimulus which will provide the most information about the (typically(More)
Adaptive stimulus design methods can potentially improve the efficiency of sensory neurophysiology experiments significantly; however, designing optimal stimulus sequences in real time remains a serious technical challenge. Here we describe two approximate methods for generating informative stimulus sequences: the first approach provides a fast method for(More)
Active learning can significantly reduce the amount of training data required to fit para-metric statistical models for supervised learning tasks. Here we present an efficient algorithm for choosing the optimal (most informative) query when the output labels are related to the inputs by a generalized linear model (GLM). The algorithm is based on a Laplace(More)
We apply an adaptive approach to optimal experimental design in the context of estimating the unknown parameters of a model of a neuron's response. We present an algorithm to choose the optimal (most informative) stimulus on each trial; this algorithm can be implemented efficiently even for high-dimensional stimulus and parameter spaces (in particular, no(More)
Sequential optimal design methods hold great promise for improving the efficiency of neurophysiology experiments. However, previous methods for optimal experimental design have incorporated only weak prior information about the underlying neural system (e.g., the sparseness or smoothness of the receptive field). Here we describe how to use stronger prior(More)
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