Innovative applications of associative morphological memories for image processing and pattern recognition

Morphological Associative Memories have been proposed for some image denoising applications. They can be applied to other less restricted domains, like image retrieval and hyper spectral image unsupervised segmentation. In this paper we present these applications. In both cases the key idea is that Autoassociative Morphological Memories selective sensitivity to erosive and dilative noise can be applied to detect the morphological independence between patterns. Linear unmixing based on the sets of morphological independent patterns define a feature extraction process that is the basis for the image processing applications. We discuss some experimental results on the fish shape data base and on a synthetic hyperspectral image, including the comparison with other linear feature extraction algorithms (ICA and CCA)

A Morphological Associative Memory Employing A Stored Pattern Independent Kernel Image and Its Hardware Model

An associative memory provides a convenient way for pattern retrieval and restoration, which has an important role for handling data distorted with noise. As an effective associative memory, we paid attention to a morphological associative memory (MAM) proposed by Ritter. The model is superior to ordinary associative memory models in terms of calculation amount, memory capacity, and perfect recall rate. However, in general, the kernel design becomes difficult as the stored pattern increases because the kernel uses a part of each stored pattern. In this paper, we propose a stored pattern independent kernel design method for the MAM and design the MAM employing the proposed kernel design with a standard digital manner in parallel architecture for acceleration. We confirm the validity of the proposed kernel design method by auto- and hetero-association experiments and investigate the efficiency of the hardware acceleration. A high-speed operation (more than 150 times in comparison with software execution) is achieved in the custom hardware. The proposed model works as an intelligent pre-processor for the Brain-Inspired Systems (Brain-IS) working in real world

A comparative study on associative memories with emphasis on morphological associative memories

Orientador: Peter SussnerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação CientificaResumo: Memórias associativas neurais são modelos do fenômeno biológico que permite o armazenamento de padrões e a recordação destes apos a apresentação de uma versão ruidosa ou incompleta de um padrão armazenado. Existem vários modelos de memórias associativas neurais na literatura, entretanto, existem poucos trabalhos comparando as varias propostas. Nesta dissertação comparamos sistematicamente o desempenho dos modelos mais influentes de memórias associativas neurais encontrados na literatura. Esta comparação está baseada nos seguintes critérios: capacidade de armazenamento, distribuição da informação nos pesos sinápticos, raio da bacia de atração, memórias espúrias e esforço computacional. Especial ênfase dado para as memórias associativas morfológicas cuja fundamentação matemática encontra-se na morfologia matemática e na álgebra de imagensAbstract: Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. There are several models of neural associative memories in the literature, however, there are few works relating them. In this thesis, we present a systematic comparison of the performances of some of the most widely known models of neural associative memories. This comparison is based on the following criteria: storage capacity, distribution of the information over the synaptic weights, basin of attraction, number of spurious memories, and computational effort. The thesis places a special emphasis on morphological associative memories whose mathematical foundations lie in mathematical morphology and image algebraMestradoMatematica AplicadaMestre em Matemática Aplicad

Connectivity in real and evolved associative memories

Peer reviewe

Environmental Effects On Drosophila Brain Development And Learning

Brain development and behavior are sensitive to a variety of environmental influences including social interactions and physicochemical stressors. Sensory input in situ is a mosaic of both enrichment and stress, yet little is known about how multiple environmental factors interact to affect brain anatomical structures, circuits and cognitive function. In this study, we addressed these issues by testing the individual and combined effects of sub-adult thermal stress, larval density and early-adult living spatial enrichment on brain anatomy and olfactory associative learning in adult Drosophila melanogaster. In response to heat stress, the mushroom bodies (MBs) were the most volumetrically impaired among all of the brain structures, an effect highly correlated with reduced odor learning performance. However, MBs were not sensitive to either larval culture density or early-adult living conditions. Extreme larval crowding reduced the volume of the antennal lobes, optic lobes and central complex. Neither larval crowding nor early-adult spatial enrichment affected olfactory learning. These results illustrate that various brain structures react differently to environmental inputs, and that MB development and learning are highly sensitive to certain stressors (pre-adult hyperthermia) and resistant to others (larval crowding). © 2018. Published by The Company of Biologists Ltd

Maintenance of aversive memories shown by fear extinction-impaired phenotypes is associated with increased activity in the amygdaloid-prefrontal circuit

Although aversive memory has been mainly addressed by analysing the changes occurring in average populations, the study of neuronal mechanisms of outliers allows understanding the involvement of individual differences in fear conditioning and extinction. We recently developed an innovative experimental model of individual differences in approach and avoidance behaviors, classifying the mice as Approaching, Balancing or Avoiding animals according to their responses to conflicting stimuli. The approach and avoidance behaviors appear to be the primary reactions to rewarding and threatening stimuli and may represent predictors of vulnerability (or resilience) to fear. We submitted the three mice phenotypes to Contextual Fear Conditioning. In comparison to Balancing animals, Approaching and Avoiding mice exhibited no middle- or long-term fear extinction. The two non-extinguishing phenotypes exhibited potentiated glutamatergic neurotransmission (spontaneous excitatory postsynaptic currents/spinogenesis) of pyramidal neurons of medial prefrontal cortex and basolateral amygdala. Basing on the a priori individuation of outliers, we demonstrated that the maintenance of aversive memories is linked to increased spinogenesis and excitatory signaling in the amygdala-prefrontal cortex fear matri

Reinstatement of long-term memory following erasure of its behavioral and synaptic expression in Aplysia.

Long-term memory (LTM) is believed to be stored in the brain as changes in synaptic connections. Here, we show that LTM storage and synaptic change can be dissociated. Cocultures of Aplysia sensory and motor neurons were trained with spaced pulses of serotonin, which induces long-term facilitation. Serotonin (5HT) triggered growth of new presynaptic varicosities, a synaptic mechanism of long-term sensitization. Following 5HT training, two antimnemonic treatments-reconsolidation blockade and inhibition of PKM--caused the number of presynaptic varicosities to revert to the original, pretraining value. Surprisingly, the final synaptic structure was not achieved by targeted retraction of the 5HT-induced varicosities but, rather, by an apparently arbitrary retraction of both 5HT-induced and original synapses. In addition, we find evidence that the LTM for sensitization persists covertly after its apparent elimination by the same antimnemonic treatments that erase learning-related synaptic growth. These results challenge the idea that stable synapses store long-term memories

Interval-valued and intuitionistic fuzzy mathematical morphologies as special cases of L-fuzzy mathematical morphology

Mathematical morphology (MM) offers a wide range of tools for image processing and computer vision. MM was originally conceived for the processing of binary images and later extended to gray-scale morphology. Extensions of classical binary morphology to gray-scale morphology include approaches based on fuzzy set theory that give rise to fuzzy mathematical morphology (FMM). From a mathematical point of view, FMM relies on the fact that the class of all fuzzy sets over a certain universe forms a complete lattice. Recall that complete lattices provide for the most general framework in which MM can be conducted. The concept of L-fuzzy set generalizes not only the concept of fuzzy set but also the concepts of interval-valued fuzzy set and Atanassov’s intuitionistic fuzzy set. In addition, the class of L-fuzzy sets forms a complete lattice whenever the underlying set L constitutes a complete lattice. Based on these observations, we develop a general approach towards L-fuzzy mathematical morphology in this paper. Our focus is in particular on the construction of connectives for interval-valued and intuitionistic fuzzy mathematical morphologies that arise as special, isomorphic cases of L-fuzzy MM. As an application of these ideas, we generate a combination of some well-known medical image reconstruction techniques in terms of interval-valued fuzzy image processing