3,903 research outputs found
Baseline regularized sparse spatial filters
The common spatial pattern (CSP) method has large number of applications in brain machine interfaces (BMI) to extract features from the multichannel neural activity through a set of linear spatial projections. These spatial projections minimize the Rayleigh quotient (RQ) as the objective function, which is the variance ratio of the classes. The CSP method easily overfits the data when the number of training trials is not sufficiently large and it is sensitive to daily variation of multichannel electrode placement, which limits its applicability for everyday use in BMI systems. To overcome these problems, the amount of channels that is used in projections, should be limited to some adequate number. We introduce a spatially sparse projection (SSP) method that renders unconstrained minimization possible via a new objective function with an approximated ℓ1 penalty. We apply our new algorithm with a baseline regularization to the ECoG data involving finger movements to gain stability with respect to the number of sparse channels. © 2013 IEEE
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
Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting
Spectral-spatial classification of hyperspectral images has been the subject
of many studies in recent years. In the presence of only very few labeled
pixels, this task becomes challenging. In this paper we address the following
two research questions: 1) Can a simple neural network with just a single
hidden layer achieve state of the art performance in the presence of few
labeled pixels? 2) How is the performance of hyperspectral image classification
methods affected when using disjoint train and test sets? We give a positive
answer to the first question by using three tricks within a very basic shallow
Convolutional Neural Network (CNN) architecture: a tailored loss function, and
smooth- and label-based data augmentation. The tailored loss function enforces
that neighborhood wavelengths have similar contributions to the features
generated during training. A new label-based technique here proposed favors
selection of pixels in smaller classes, which is beneficial in the presence of
very few labeled pixels and skewed class distributions. To address the second
question, we introduce a new sampling procedure to generate disjoint train and
test set. Then the train set is used to obtain the CNN model, which is then
applied to pixels in the test set to estimate their labels. We assess the
efficacy of the simple neural network method on five publicly available
hyperspectral images. On these images our method significantly outperforms
considered baselines. Notably, with just 1% of labeled pixels per class, on
these datasets our method achieves an accuracy that goes from 86.42%
(challenging dataset) to 99.52% (easy dataset). Furthermore we show that the
simple neural network method improves over other baselines in the new
challenging supervised setting. Our analysis substantiates the highly
beneficial effect of using the entire image (so train and test data) for
constructing a model.Comment: Remote Sensing 201
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