Unsupervised learning as a complement to convolutional neural network classification in the analysis of saccadic eye movement in spino-cerebellar ataxia type 2

IWANN es un congreso internacional que se celebra bienalmente desde 1991. Su campo de estudio se centra en la fundamentación y aplicación de las distintas técnicas de Inteligencia Computacional : Redes Neuronales Artificiales, Algoritmos Genéticos, Lógica Borrosa, Aprendizaje Automático. En esta edición han participado 150 investigadores.This paper aims at assessing spino-cerebellar type 2 ataxiaby classifying electrooculography records into registers corresponding to healthy, presymptomatic and ill individuals. The primary used technique is the convolutional neural network applied to the time series of eye movements, called saccades. The problem is exceptionally hard, though, because the recorded saccadic movements for presymptomatic cases often do not substantially di er from those of healthy individuals. Precisely this distinction is of the utmost clinical importance, since early intervention on presymptomatic patients can ameliorate symptoms or at least slow their progression. Yet, each register contains a number of saccades that, although not consistent with the current label, have not been considered indicative of another class by the examining physicians. As a consequence, an unsupervised learning mechanism may be more suitable to handle this form of misclassi cation. Thus, our proposal introduces the k-means approach and the SOM method, as complementary techniques to analyse the time series. The three techniques operating in tandem lead to a well performing solution to this diagnosis problem.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Universidad de Granada, Universitat Politècnica de Catalunya, Universidad de Las Palmas de Gran Canaria, Springe

Selective Decoding in Associative Memories Based on Sparse-Clustered Networks

Associative memories are structures that can retrieve previously stored information given a partial input pattern instead of an explicit address as in indexed memories. A few hardware approaches have recently been introduced for a new family of associative memories based on Sparse-Clustered Networks (SCN) that show attractive features. These architectures are suitable for implementations with low retrieval latency, but are limited to small networks that store a few hundred data entries. In this paper, a new hardware architecture of SCNs is proposed that features a new data-storage technique as well as a method we refer to as Selective Decoding (SD-SCN). The SD-SCN has been implemented using a similar FPGA used in the previous efforts and achieves two orders of magnitude higher capacity, with no error-performance penalty but with the cost of few extra clock cycles per data access.Comment: 4 pages, Accepted in IEEE Global SIP 2013 conferenc

Quantum Associative Memory

This paper combines quantum computation with classical neural network theory to produce a quantum computational learning algorithm. Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper combines two quantum computational algorithms to produce such a quantum associative memory. The result is an exponential increase in the capacity of the memory when compared to traditional associative memories such as the Hopfield network. The paper covers necessary high-level quantum mechanical and quantum computational ideas and introduces a quantum associative memory. Theoretical analysis proves the utility of the memory, and it is noted that a small version should be physically realizable in the near future

Automatic programming methodologies for electronic hardware fault monitoring

This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.This research was supported by the International Joint Research Grant of the IITA (Institute of Information Technology Assessment) foreign professor invitation program of the MIC (Ministry of Information and Communication), Korea