21,680 research outputs found
Complex networks in brain electrical activity
We analyze the complex networks associated with brain electrical activity.
Multichannel EEG measurements are first processed to obtain 3D voxel
activations using the tomographic algorithm LORETA. Then, the correlation of
the current intensity activation between voxel pairs is computed to produce a
voxel cross-correlation coefficient matrix. Using several correlation
thresholds, the cross-correlation matrix is then transformed into a network
connectivity matrix and analyzed. To study a specific example, we selected data
from an earlier experiment focusing on the MMN brain wave. The resulting
analysis highlights significant differences between the spatial activations
associated with Standard and Deviant tones, with interesting physiological
implications. When compared to random data networks, physiological networks are
more connected, with longer links and shorter path lengths. Furthermore, as
compared to the Deviant case, Standard data networks are more connected, with
longer links and shorter path lengths--i.e., with a stronger ``small worlds''
character. The comparison between both networks shows that areas known to be
activated in the MMN wave are connected. In particular, the analysis supports
the idea that supra-temporal and inferior frontal data work together in the
processing of the differences between sounds by highlighting an increased
connectivity in the response to a novel sound.Comment: 22 pages, 5 figures. Starlab preprint. This version is an attempt to
include better figures (no content change
Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence
No abstract availabl
Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI
Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor.
Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery.
Deep-learning techniques are becoming popular in medical applications and image-based diagnosis.
Convolutional Neural Networks are the preferred architecture for object detection and classification in images.
In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma
detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing
the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using
Fuzzy C-Means.Ministerio de Economía y Competitividad TEC2016-77785-
Random Sketching, Clustering, and Short-Term Memory in Spiking Neural Networks
We study input compression in a biologically inspired model of neural computation. We demonstrate that a network consisting of a random projection step (implemented via random synaptic connectivity) followed by a sparsification step (implemented via winner-take-all competition) can reduce well-separated high-dimensional input vectors to well-separated low-dimensional vectors. By augmenting our network with a third module, we can efficiently map each input (along with any small perturbations of the input) to a unique representative neuron, solving a neural clustering problem.
Both the size of our network and its processing time, i.e., the time it takes the network to compute the compressed output given a presented input, are independent of the (potentially large) dimension of the input patterns and depend only on the number of distinct inputs that the network must encode and the pairwise relative Hamming distance between these inputs. The first two steps of our construction mirror known biological networks, for example, in the fruit fly olfactory system [Caron et al., 2013; Lin et al., 2014; Dasgupta et al., 2017]. Our analysis helps provide a theoretical understanding of these networks and lay a foundation for how random compression and input memorization may be implemented in biological neural networks.
Technically, a contribution in our network design is the implementation of a short-term memory. Our network can be given a desired memory time t_m as an input parameter and satisfies the following with high probability: any pattern presented several times within a time window of t_m rounds will be mapped to a single representative output neuron. However, a pattern not presented for c?t_m rounds for some constant c>1 will be "forgotten", and its representative output neuron will be released, to accommodate newly introduced patterns
Theoretical Interpretations and Applications of Radial Basis Function Networks
Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains
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