59,954 research outputs found
Most Likely Separation of Intensity and Warping Effects in Image Registration
This paper introduces a class of mixed-effects models for joint modeling of
spatially correlated intensity variation and warping variation in 2D images.
Spatially correlated intensity variation and warp variation are modeled as
random effects, resulting in a nonlinear mixed-effects model that enables
simultaneous estimation of template and model parameters by optimization of the
likelihood function. We propose an algorithm for fitting the model which
alternates estimation of variance parameters and image registration. This
approach avoids the potential estimation bias in the template estimate that
arises when treating registration as a preprocessing step. We apply the model
to datasets of facial images and 2D brain magnetic resonance images to
illustrate the simultaneous estimation and prediction of intensity and warp
effects
Neural system identification for large populations separating "what" and "where"
Neuroscientists classify neurons into different types that perform similar
computations at different locations in the visual field. Traditional methods
for neural system identification do not capitalize on this separation of 'what'
and 'where'. Learning deep convolutional feature spaces that are shared among
many neurons provides an exciting path forward, but the architectural design
needs to account for data limitations: While new experimental techniques enable
recordings from thousands of neurons, experimental time is limited so that one
can sample only a small fraction of each neuron's response space. Here, we show
that a major bottleneck for fitting convolutional neural networks (CNNs) to
neural data is the estimation of the individual receptive field locations, a
problem that has been scratched only at the surface thus far. We propose a CNN
architecture with a sparse readout layer factorizing the spatial (where) and
feature (what) dimensions. Our network scales well to thousands of neurons and
short recordings and can be trained end-to-end. We evaluate this architecture
on ground-truth data to explore the challenges and limitations of CNN-based
system identification. Moreover, we show that our network model outperforms
current state-of-the art system identification models of mouse primary visual
cortex.Comment: NIPS 201
Nonlinear Hebbian learning as a unifying principle in receptive field formation
The development of sensory receptive fields has been modeled in the past by a
variety of models including normative models such as sparse coding or
independent component analysis and bottom-up models such as spike-timing
dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic
plasticity. Here we show that the above variety of approaches can all be
unified into a single common principle, namely Nonlinear Hebbian Learning. When
Nonlinear Hebbian Learning is applied to natural images, receptive field shapes
were strongly constrained by the input statistics and preprocessing, but
exhibited only modest variation across different choices of nonlinearities in
neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse
network activity are necessary for the development of localized receptive
fields. The analysis of alternative sensory modalities such as auditory models
or V2 development lead to the same conclusions. In all examples, receptive
fields can be predicted a priori by reformulating an abstract model as
nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural
statistics can account for many aspects of receptive field formation across
models and sensory modalities
Penalized Likelihood and Bayesian Function Selection in Regression Models
Challenging research in various fields has driven a wide range of
methodological advances in variable selection for regression models with
high-dimensional predictors. In comparison, selection of nonlinear functions in
models with additive predictors has been considered only more recently. Several
competing suggestions have been developed at about the same time and often do
not refer to each other. This article provides a state-of-the-art review on
function selection, focusing on penalized likelihood and Bayesian concepts,
relating various approaches to each other in a unified framework. In an
empirical comparison, also including boosting, we evaluate several methods
through applications to simulated and real data, thereby providing some
guidance on their performance in practice
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