317 research outputs found
A Novel Model-Free Data Analysis Technique Based on Clustering in a Mutual Information Space: Application to Resting-State fMRI
Non-parametric data-driven analysis techniques can be used to study datasets with few assumptions about the data and underlying experiment. Variations of independent component analysis (ICA) have been the methods mostly used on fMRI data, e.g., in finding resting-state networks thought to reflect the connectivity of the brain. Here we present a novel data analysis technique and demonstrate it on resting-state fMRI data. It is a generic method with few underlying assumptions about the data. The results are built from the statistical relations between all input voxels, resulting in a whole-brain analysis on a voxel level. It has good scalability properties and the parallel implementation is capable of handling large datasets and databases. From the mutual information between the activities of the voxels over time, a distance matrix is created for all voxels in the input space. Multidimensional scaling is used to put the voxels in a lower-dimensional space reflecting the dependency relations based on the distance matrix. By performing clustering in this space we can find the strong statistical regularities in the data, which for the resting-state data turns out to be the resting-state networks. The decomposition is performed in the last step of the algorithm and is computationally simple. This opens up for rapid analysis and visualization of the data on different spatial levels, as well as automatically finding a suitable number of decomposition components
Large-Scale Modeling – a Tool for Conquering the Complexity of the Brain
Is there any hope of achieving a thorough understanding of higher functions such as perception, memory, thought and emotion or is the stunning complexity of the brain a barrier which will limit such efforts for the foreseeable future? In this perspective we discuss methods to handle complexity, approaches to model building, and point to detailed large-scale models as a new contribution to the toolbox of the computational neuroscientist. We elucidate some aspects which distinguishes large-scale models and some of the technological challenges which they entail
Learning representations in Bayesian Confidence Propagation neural networks
Unsupervised learning of hierarchical representations has been one of the
most vibrant research directions in deep learning during recent years. In this
work we study biologically inspired unsupervised strategies in neural networks
based on local Hebbian learning. We propose new mechanisms to extend the
Bayesian Confidence Propagating Neural Network (BCPNN) architecture, and
demonstrate their capability for unsupervised learning of salient hidden
representations when tested on the MNIST dataset
Hebbian fast plasticity and working memory
Theories and models of working memory (WM) were at least since the mid-1990s
dominated by the persistent activity hypothesis. The past decade has seen
rising concerns about the shortcomings of sustained activity as the mechanism
for short-term maintenance of WM information in the light of accumulating
experimental evidence for so-called activity-silent WM and the fundamental
difficulty in explaining robust multi-item WM. In consequence, alternative
theories are now explored mostly in the direction of fast synaptic plasticity
as the underlying mechanism.The question of non-Hebbian vs Hebbian synaptic
plasticity emerges naturally in this context. In this review we focus on fast
Hebbian plasticity and trace the origins of WM theories and models building on
this form of associative learning.Comment: 12 pages, 2 figures, 1 box, submitte
Лечение и реабилитация детей с аллергическими заболеваниями
аллергические заболеванияАСТМА БРОНХИАЛЬНАЯДЕТИциклоферонгипооксибаротерапияреабилитация дете
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