Joel Kaardal
Salk Institute
Decoding the computations of high-level auditory neurons
Wednesday 29th of November 2017 at 12:00pm
560 Evans
Characterizing the computations performed by high-level sensory
regions of the brain remains enigmatic due to the many nonlinear signal
transformations that separate the input sensory stimuli from the neural
responses. In order to produce interpretable models of these computations,
dimensionality reduction techniques can be employed to obtain a
description of the neural computation in terms of a relevant,
multicomponent subspace of the stimulus space. While a number of these
techniques have been devised, many rely on computing second-order moments
of the stimulus/response distribution leading to models with many more
parameters than is ultimately necessary to capture the relevant subspace.
For high-level sensory neurons in particular, these models can be prone to
overfitting due to low effective sampling of the stimulus space when
presented with natural stimuli. To address this, we reformulated a maximum
entropy method as a low-rank matrix factorization problem. With the
principled application of regularization, the low-rank method led to
improved prediction accuracy and estimation of the relevant subspace than
prior methods. The low-rank method was deployed to study the computations
of neurons from high-level regions in the songbird brain yielding multiple
relevant components spanning each neuron's receptive field. The relevant
components were then transformed using logical OR and logical AND
operations highlighting potential differences in how regions and sensory
systems process sensory information.(video)
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