Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules

Ursino, Mauro; Magosso, Elisa; Cara, Giuseppe-Emiliano La; Cuppini, Cristiano

doi:10.1016/j.biosystems.2006.01.005

Cited by 6 publications

(5 citation statements)

References 42 publications

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“…In fact, there are many models of oscillatory neural networks that are able to transform spatial structure of visual input into temporal structure of neural activity. These models, which were originally developed to simulate cortical computations, are built with phase-coupled oscillatory neurons (e.g., Baldi and Meir, 1990; Sompolinsky et al, 1991; Sporns et al, 1991; von der Malsburg and Buhmann, 1992; Schillen and Koenig, 1994; Wang and Terman, 1997; Ursino et al, 2006). It would be useful to develop such models to explore retinal and thalamic function.…”

Section: Potential New Roles For Oscillationsmentioning

confidence: 99%

Exploring the function of neural oscillations in early sensory systems

et al. 2010

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Neuronal oscillations appear throughout the nervous system, in structures as diverse as the cerebral cortex, hippocampus, subcortical nuclei and sense organs. Whether neural rhythms contribute to normal function, are merely epiphenomena, or even interfere with physiological processing are topics of vigorous debate. Sensory pathways are ideal for investigation of oscillatory activity because their inputs can be defined. Thus, we will focus on sensory systems as we ask how neural oscillations arise and how they might encode information about the stimulus. We will highlight recent work in the early visual pathway that shows how oscillations can multiplex different types of signals to increase the amount of information that spike trains encode and transmit. Last, we will describe oscillation-based models of visual processing and explore how they might guide further research.

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Section: Potential New Roles For Oscillationsmentioning

confidence: 99%

Exploring the function of neural oscillations in early sensory systems

et al. 2010

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“…Inspired by the previous encouraging results of neural network modeling and by recent studies supporting the role of gamma-band synchronization in higher cortical functions, we recently proposed a network of Wilson-Cowan oscillators, which aspires to simulate segmentation at high cognitive levels, rather than at low sensory levels [61]. The network realizes separation of simultaneous objects and their recognition (that is reconstruction from memory) at a single processing layer, by grouping together a set of fundamental features on the basis of two high-level Gestalt rules: similarity and previous knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, each neural oscillator has four nearest neighbors with a distance conventionally assumed equal to 1, and four neighbors with a distance . By contrast, in the previous paper [61], we used a monodimensional chain in which each oscillator has just two nearest neighbors.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Abstract Objects Via Neural Oscillators: Interaction Among Topological Organization, Associative Memory and Gamma Band Synchronization

Ursino

Magosso

Cuppini

2009

IEEE Trans. Neural Netw.

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Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.

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“…In the model, features of the same object are linked together, and separated from those of different objects, via synchronization of neural oscillators in the gamma band. The network was able to recognize objects, and separate them from other objects simultaneously present, even in case of partial or corrupted information, and when objects share some common features [7, 8]. In a more recent version of the same model, this object representation, spreading across different feature areas, is linked with a lexical area devoted to word representation, so that correct object retrieval can evoke the corresponding word, and vice versa.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Model to Study Neural Organization of Language in Bilingualism

Ursino

Cuppini

Magosso

2010

Computational Intelligence and Neuroscience

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A neural network model of object semantic representation is used to simulate learning of new words from a foreign language. The network consists of feature areas, devoted to description of object properties, and a lexical area, devoted to words representation. Neurons in the feature areas are implemented as Wilson-Cowan oscillators, to allow segmentation of different simultaneous objects via gamma-band synchronization. Excitatory synapses among neurons in the feature and lexical areas are learned, during a training phase, via a Hebbian rule. In this work, we first assume that some words in the first language (L1) and the corresponding object representations are initially learned during a preliminary training phase. Subsequently, second-language (L2) words are learned by simultaneously presenting the new word together with the L1 one. A competitive mechanism between the two words is also implemented by the use of inhibitory interneurons. Simulations show that, after a weak training, the L2 word allows retrieval of the object properties but requires engagement of the first language. Conversely, after a prolonged training, the L2 word becomes able to retrieve object per se. In this case, a conflict between words can occur, requiring a higher-level decision mechanism.

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Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules

Cited by 6 publications

References 42 publications

Exploring the function of neural oscillations in early sensory systems

Exploring the function of neural oscillations in early sensory systems

Recognition of Abstract Objects Via Neural Oscillators: Interaction Among Topological Organization, Associative Memory and Gamma Band Synchronization

A Semantic Model to Study Neural Organization of Language in Bilingualism

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