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Normative models and identification of nonlinear neural representations| old_uid | 13074 |
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| title | Normative models and identification of nonlinear neural representations |
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| start_date | 2013/11/26 |
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| schedule | 14h |
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| online | no |
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| location_info | salle Cavaillès |
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| details | GNT Theoretical Neuroscience Seminar Series. Host: Matthew Chalk |
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| summary | Perceptual inference relies on very nonlinear processing of
high-dimensional sensory inputs. This poses a challenge as the space
of possible nonlinearities is huge and each of there functions might
be implemented in many different ways. To gain better insights into
the nonlinear processing of sensory signals in the brain, we are
trying to make progress on two fronts: (1) By learning probabilistic
representations of natural images, we explore which nonlinearities are
most successful in capturing the degrees of freedom of the visual
input. (2) We develop neural system identification methods with the
aim of identifying unknown nonlinear properties of neurons that are
difficult to identify by linear or generalized linear models. In this
talk, I will first give a short summary of our results on natural
image representations and our conclusions regarding effective
nonlinearities (15min). In the second part (30min), I will talk about
a system identification approach based on a new model called the
spike-triggered mixture (STM) model. The model is able to capture
complex dependencies on high-dimensional stimuli with far fewer
parameters than other approaches such as histogram-based methods. The
added flexibility comes at the cost of a non-concave log-likelihood
but we show that in practice this does not have to be an issue. By
fitting the STM model to spike responses of vibrissal afferents we
demonstrate that the STM model outperforms generalized linear and
quadratic models by a large margin (up to 200 bits/s). |
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| responsibles | Koechlin |
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