6,447 research outputs found
Magnification Control in Winner Relaxing Neural Gas
An important goal in neural map learning, which can conveniently be
accomplished by magnification control, is to achieve information optimal coding
in the sense of information theory. In the present contribution we consider the
winner relaxing approach for the neural gas network. Originally, winner
relaxing learning is a slight modification of the self-organizing map learning
rule that allows for adjustment of the magnification behavior by an a priori
chosen control parameter. We transfer this approach to the neural gas
algorithm. The magnification exponent can be calculated analytically for
arbitrary dimension from a continuum theory, and the entropy of the resulting
map is studied numerically conf irming the theoretical prediction. The
influence of a diagonal term, which can be added without impacting the
magnification, is studied numerically. This approach to maps of maximal mutual
information is interesting for applications as the winner relaxing term only
adds computational cost of same order and is easy to implement. In particular,
it is not necessary to estimate the generally unknown data probability density
as in other magnification control approaches.Comment: 14pages, 2 figure
Magnification Control in Self-Organizing Maps and Neural Gas
We consider different ways to control the magnification in self-organizing
maps (SOM) and neural gas (NG). Starting from early approaches of magnification
control in vector quantization, we then concentrate on different approaches for
SOM and NG. We show that three structurally similar approaches can be applied
to both algorithms: localized learning, concave-convex learning, and winner
relaxing learning. Thereby, the approach of concave-convex learning in SOM is
extended to a more general description, whereas the concave-convex learning for
NG is new. In general, the control mechanisms generate only slightly different
behavior comparing both neural algorithms. However, we emphasize that the NG
results are valid for any data dimension, whereas in the SOM case the results
hold only for the one-dimensional case.Comment: 24 pages, 4 figure
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
SVITE: A Spike-Based VITE Neuro-Inspired Robot Controller
This paper presents an implementation of a neuro-inspired algorithm
called VITE (Vector Integration To End Point) in FPGA in the spikes domain.
VITE aims to generate a non-planned trajectory for reaching tasks in robots.
The algorithm has been adapted to work completely in the spike domain under
Simulink simulations. The FPGA implementation consists in 4 VITE in parallel
for controlling a 4-degree-of-freedom stereo-vision robot. This work represents
the main layer of a complex spike-based architecture for robot neuro-inspired
reaching tasks in FPGAs. It has been implemented in two Xilinx FPGA
families: Virtex-5 and Spartan-6. Resources consumption comparative between
both devices is presented. Results obtained for Spartan device could allow
controlling complex robotic structures with up to 96 degrees of freedom per
FPGA, providing, in parallel, high speed connectivity with other neuromorphic
systems sending movement references. An exponential and gamma distribution
test over the inter spike interval has been performed to proof the approach to the
neural code proposed.Ministerio de Economía y Competitividad TEC2012-37868-C04-0
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