Computing with arrays of coupled oscillators: An application to preattentive texture discrimination

Recent experimental findings (Gray et al. 1989; Eckhorn et al. 1988) seem to indicate that rapid oscillations and phase-lockings of different populations of cortical neurons play an important role in neural computations. In particular, global stimulus properties could be reflected in the correlated firing of spatially distant cells. Here we describe how simple coupled oscillator networks can be used to model the data and to investigate whether useful tasks can be performed by oscillator architectures. A specific demonstration is given for the problem of preattentive texture discrimination. Texture images are convolved with different sets of Gabor filters feeding into several corresponding arrays of coupled oscillators. After a brief transient, the dynamic evolution in the arrays leads to a separation of the textures by a phase labeling mechanism. The importance of noise and of long range connections is briefly discussed

Cortical Synchronization and Perceptual Framing

How does the brain group together different parts of an object into a coherent visual object representation? Different parts of an object may be processed by the brain at different rates and may thus become desynchronized. Perceptual framing is a process that resynchronizes cortical activities corresponding to the same retinal object. A neural network model is presented that is able to rapidly resynchronize clesynchronized neural activities. The model provides a link between perceptual and brain data. Model properties quantitatively simulate perceptual framing data, including psychophysical data about temporal order judgments and the reduction of threshold contrast as a function of stimulus length. Such a model has earlier been used to explain data about illusory contour formation, texture segregation, shape-from-shading, 3-D vision, and cortical receptive fields. The model hereby shows how many data may be understood as manifestations of a cortical grouping process that can rapidly resynchronize image parts which belong together in visual object representations. The model exhibits better synchronization in the presence of noise than without noise, a type of stochastic resonance, and synchronizes robustly when cells that represent different stimulus orientations compete. These properties arise when fast long-range cooperation and slow short-range competition interact via nonlinear feedback interactions with cells that obey shunting equations.Office of Naval Research (N00014-92-J-1309, N00014-95-I-0409, N00014-95-I-0657, N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0334, F49620-92-J-0225)

Efficient Image Processing Via Compressive Sensing Of Integrate-And-Fire Neuronal Network Dynamics

Integrate-and-fire (I&F) neuronal networks are ubiquitous in diverse image processing applications, including image segmentation and visual perception. While conventional I&F network image processing requires the number of nodes composing the network to be equal to the number of image pixels driving the network, we determine whether I&F dynamics can accurately transmit image information when there are significantly fewer nodes than network input-signal components. Although compressive sensing (CS) theory facilitates the recovery of images using very few samples through linear signal processing, it does not address whether similar signal recovery techniques facilitate reconstructions through measurement of the nonlinear dynamics of an I&F network. In this paper, we present a new framework for recovering sparse inputs of nonlinear neuronal networks via compressive sensing. By recovering both one-dimensional inputs and two-dimensional images, resembling natural stimuli, we demonstrate that input information can be well-preserved through nonlinear I&F network dynamics even when the number of network-output measurements is significantly smaller than the number of input-signal components. This work suggests an important extension of CS theory potentially useful in improving the processing of medical or natural images through I&F network dynamics and understanding the transmission of stimulus information across the visual system

Role of Alpha Oscillations During Short Time Memory Task Investigated by Graph Based Partitioning

In this study, we investigate the clustering pattern of alpha band (8 Hz - 12 Hz) electroencephalogram (EEG) oscillations obtained from healthy individuals during a short time memory task with 3 different memory loads. The retention period during which subjects were asked to memorize a pattern in a square matrix is analyzed with a graph theoretical approach. The functional coupling among EEG electrodes are quantified via mutual information in the time-frequency plane. A spectral clustering algorithm followed by bootstrapping is used to parcellate memory related circuits and for identifying significant clusters in the brain. The main outcome of the study is that the size of the significant clusters formed by alpha oscillations decreases as the memory load increases. This finding corroborates the active inhibition hypothesis about alpha oscillations

Children, Humanoid Robots and Caregivers

This paper presents developmental learning on a humanoid robot from human-robot interactions. We consider in particular teaching humanoids as children during the child's Separation and Individuation developmental phase (Mahler, 1979). Cognitive development during this phase is characterized both by the child's dependence on her mother for learning while becoming awareness of her own individuality, and by self-exploration of her physical surroundings. We propose a learning framework for a humanoid robot inspired on such cognitive development

Cortical synchronization as a neural basis for visual perception

Cortical synchronization has been suggested as a neural mechanism that is able to solve the feature binding problem. This idea has been intensively studied at neurophysiological, psychophysical and computational level. In this paper, arguments for and against the role of cortical synchronization in visual perception are critically examined. Initial neurophysiological findings of correlated neural activity in the primary visual cortex have been questioned by studies which reveal enhanced firing rate to the figure region compared to the background. Computational investigations reveal that synchronization has capacity limit. At the behavioural level, change blindness has been used as the evidence for capacity limit of visual perception. However, further examination of this issue showed that detailed visual representation exists but it is obscured by limitation of attention and visual working memory. Other behavioural phenomena, such as perceptual asynchrony, also point to the fact that there is dissociation between correlated neural activity and perception. Therefore, at present there is no sufficient evidence to support the conclusion that cortical synchronization plays a crucial role in visual perception

Source separation with one ear : proposition for an anthropomorphic approach

Abstract : We present an example of an anthropomorphic approach, in which auditory-based cues are combined with temporal correlation to implement a source separation system. The auditory features are based on spectral amplitudemodulation and energy information obtained through 256 cochlear filters. Segmentation and binding of auditory objects are performed with a two-layered spiking neural network. The first layer performs the segmentation of the auditory images into objects, while the second layer binds the auditory objects belonging to the same source. The binding is further used to generate a mask (binary gain) to suppress the undesired sources fromthe original signal. Results are presented for a double-voiced (2 speakers) speech segment and for sentences corrupted with different noise sources. Comparative results are also given using PESQ (perceptual evaluation of speech quality) scores. The spiking neural network is fully adaptive and unsupervised