Exponential Pattern Retrieval Capacity with Non-Binary Associative Memory

We consider the problem of neural association for a network of non-binary neurons. Here, the task is to recall a previously memorized pattern from its noisy version using a network of neurons whose states assume values from a finite number of non-negative integer levels. Prior works in this area consider storing a finite number of purely random patterns, and have shown that the pattern retrieval capacities (maximum number of patterns that can be memorized) scale only linearly with the number of neurons in the network

Molecular associative memory: An associative memory framework with exponential storage capacity for DNA computing

Associative memory problem: Find the closest stored vector (in Hamming distance) to a given query vector. There are different ways to implement an associative memory, including the neural networks and DNA strands. Using neural networks, connection weights are adjusted in order to perform association. Recall procedure is iterative and relies on simple neural operations. In this case, the design criteria is maximizing the number of stored patterns C while having some noise tolerance. The molecular implementation is based on synthesizing C DNA strands as stored vectors. Recall procedure is usually done in one shot via chemical reactions and relies on highly parallelism of DNA computing. Here, the design criteria: finding proper DNA sequences to minimize probability of error during the recall phase. Current molecular associative memories are either low in storage capacity, if implemented using molecular realizations of neural networks, or very complex to implement, if all the stored sequences have to be synthesized. We introduce an associative memory framework with exponential storage capacity based on transcriptional networks of DNA switches. The advantages of the proposed approach over current methods are: 1. Exponential storage capacities with current neural network-based approaches can not be achieved. 2. For other methods, although having exponential storage capacities is possible, it is very complex as it requires synthesizing an extraordinarily large number of DNA strands

Nonbinary Associative Memory With Exponential Pattern Retrieval Capacity and Iterative Learning

We consider the problem of neural association for a network of nonbinary neurons. Here, the task is to first memorize a set of patterns using a network of neurons whose states assume values from a finite number of integer levels. Later, the same network should be able to recall the previously memorized patterns from their noisy versions. Prior work in this area consider storing a finite number of purely random patterns, and have shown that the pattern retrieval capacities (maximum number of patterns that can be memorized) scale only linearly with the number of neurons in the network. In our formulation of the problem, we concentrate on exploiting redundancy and internal structure of the patterns to improve the pattern retrieval capacity. Our first result shows that if the given patterns have a suitable linear-algebraic structure, i.e., comprise a subspace of the set of all possible patterns, then the pattern retrieval capacity is exponential in terms of the number of neurons. The second result extends the previous finding to cases where the patterns have weak minor components, i.e., the smallest eigenvalues of the correlation matrix tend toward zero. We will use these minor components (or the basis vectors of the pattern null space) to increase both the pattern retrieval capacity and error correction capabilities. An iterative algorithm is proposed for the learning phase, and two simple algorithms are presented for the recall phase. Using analytical methods and simulations, we show that the proposed methods can tolerate a fair amount of errors in the input while being able to memorize an exponentially large number of patterns

Exponential pattern retrieval capacity with non-binary associative memory

Partiendo de la idea de que, si bien en todos los trabajos se involucra el cuerpo, en el trabajo de la danza éste se involucra de una manera especial. El trabajo en la danza puede exigir entrenamientos, clases y cuidados que exceden el horario laboral; también puede implicar una iniciación en la danza a edad temprana, una corta vida laboral y un gusto por la danza previo al trabajo de la danza. A esto le agregamos la carencia en Argentina de un marco legal que resguarde a las bailarinas y bailarines, lo cual redunda en una inestabilidad y precariedad característica de este trabajo. La presente investigación se realizó en el año 2017 y fue producto de un taller de investigación llamado "Enfoque biográfico, curso de vida y mundo del trabajo: perspectivas teóricas, epistemológicas y metodológicas para la reestructuración y análisis de historias de vida laborales en Ciencias Sociales" de la carrera de Licenciatura en Sociología de la UNLP, centrado en la metodología del enfoque biográfico. A partir de tres entrevistas biográficas a bailarinas profesionales de La Plata, se intentó reconstruir las trayectorias laborales y las trayectorias en la danza para relacionar los conceptos cuerpo, trabajo y danza, así y responder a la pregunta ¿cómo se trabaja bailando?.Fil: Zappelli Gugliotta, Julia Xanzi. Universidad Nacional de La Plata. Facultad de Humanidades y Ciencias de la Educación; Argentina