Locally excitatory globally inhibitory oscillator networks

A novel class of locally excitatory, globally inhibitory oscillator networks (LEGION) is proposed and investigated. The model of each oscillator corresponds to a standard relaxation oscillator with two time scales. In the network, an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing other oscillators from jumping up. Computer simulations demonstrate that the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. This model lays a physical foundation for the oscillatory correlation theory of feature binding and may provide an effective computational framework for scene segmentation and figure/ground segregation in real time.     

刺激下可变耦合神经振子群活动的非线性随机演化模型

考虑耦合强度随时间变化,提出在外刺激及噪声共同作用下神经振子群活动的动力学模型,并引入平均耦合对数密度作为神经振子群分布式时空编码模式.通过数值分析表明,一阶弱谐波刺激对神经振子群体编码没有显著的影响;强刺激或高阶谐波刺激加强了神经振子群的同步化活动,并增强了神经振子之间的耦合;不同频率谐波的组合刺激对神经编码的影响并不是相互独立的,而是具有某种非线性关系,且刺激强度较大的谐波主导神经编码.     

A Network of Neural Oscillators for Fractal Pattern Recognition

Biological neural networks are high dimensional nonlinear systems, which presents complex dynamical phenomena, such as chaos. Thus, the study of coupled dynamical systems is important for understanding functional mechanism of real neural networks and it is also important for modeling more realistic artificial neural networks. In this direction, the study of a ring of phase oscillators has been proved to be useful for pattern recognition. Such an approach has at least three nontrivial advantages over the traditional dynamical neural networks: first, each input pattern can be encoded in a vector instead of a matrix; second, the connection weights can be determined analytically; third, due to its dynamical nature, it has the ability to capture temporal patterns. In the previous studies of this topic, all patterns were encoded as stable periodic solutions of the oscillator network. In this paper, we continue to explore the oscillator ring for pattern recognition. Specifically, we propose algorithms, which use the chaotic dynamics of the closed loops of Stuart–Landau oscillators as artificial neurons, to recognize randomly generated fractal patterns. We manipulate the number of neurons and initial conditions of the oscillator ring to encode fractal patterns. It is worth noting that fractal pattern recognition is a challenging problem due to their discontinuity nature and their complex forms. Computer simulations confirm good performance of the proposed algorithms.     

Scene analysis by integrating primitive segmentation andassociative memory

Scene analysis is a major aspect of perception and continues to challenge machine perception. This paper addresses the scene-analysis problem by integrating a primitive segmentation stage with a model of associative memory. The model is a multistage system that consists of an initial primitive segmentation stage, a multimodule associative memory, and a short-term memory (STM) layer. Primitive segmentation is performed by a locally excitatory globally inhibitory oscillator network (LEGION), which segments the input scene into multiple parts that correspond to groups of synchronous oscillations. Each segment triggers memory recall and multiple recalled patterns then interact with one another in the STM layer. The STM layer projects to the LEGION network, giving rise to memory-based grouping and segmentation. The system achieves scene analysis entirely in phase space, which provides a unifying mechanism for both bottom-up analysis and top-down analysis. The model is evaluated with a systematic set of three-dimensional (3-D) line drawing objects, which are arranged in an arbitrary fashion to compose input scenes that allow object occlusion. Memory-based organization is responsible for a significant improvement in performance. A number of issues are discussed, including input-anchored alignment, top-down organization, and the role of STM in producing context sensitivity of memory recall.     

Image analysis and computer vision: 1990

This paper presents a bibliography of over 1600 references related to computer vision and image analysis, arranged by subject matter. The topics covered include architectures; computational techniques; feature detection, segmentation, and image analysis; matching, stereo, and time-varying imagery; shape and pattern; color and texture; and three-dimensional scene analysis. A few references are also given on related topics, such as computational geometry, computer graphics, image input/output and coding, image processing, optical processing, visual perception, neural nets, pattern recognition, and artificial intelligence.     

On connectedness: a solution based on oscillatory correlation

A long-standing problem in Neural Comp has been the problem of connectedness, first identified by Minsky and Papert (1969). This problem served as the cornerstone for them to establish analytically that perceptrons are fundamentally limited in computing geometrical (topological) properties. A solution to this problem is offered by a different class of neural networks: oscillator networks. To solve the problem, the representation of oscillatory correlation is employed, whereby one pattern is represented as a synchronized block of oscillators and different patterns are represented by distinct blocks that desynchronize from each other. Oscillatory correlation emerges from LEGION (locally excitatory globally inhibitory oscillator network), whose architecture consists of local excitation and global inhibition among neural oscillators. It is further shown that these oscillator networks exhibit sensitivity to topological structure, which may lay a neurocomputational foundation for explaining the psychophysical phenomenon of topological perception.     

基于混沌神经网络的发电机定子绕组匝间短路故障诊断

本文采用耦合的混沌振荡子作为单个混沌神经元构造混沌神经网络模型，用改进Hebb算法设计网络的连接权值。在此基础上，实现了混沌神经网络的动态联想记忆并应用该混沌神经网络模型对发电机定子绕组匝间短路故障进行诊断。结果表明，该种方法有助于故障模式的记     

Modeling segmentation of a visual scene via neural oscillators: fragmentation, discovery of details and attention

The present study analyses the problem of binding and segmentation of a visual scene by means of a network of neural oscillators, laying emphasis on the problems of fragmentation, perception of details at different scales and spatial attention. The work is based on a two-layer model: a second layer of Wilson-Cowan oscillators is inhibited by information from the first layer. Moreover, the model uses a global inhibitor (GI) to segment objects. Spatial attention consists of an excitatory input, surrounded by an inhibitory annulus. A single object is identified by synchronous oscillatory activity of neural groups. The main idea of this work is that segmentation of objects at different detail levels can be achieved by linking parameters of the GI (i.e. the threshold and the inhibition strength) with the dimension of the zone selected by attention and with the dimension of the smaller objects to be detected. Simulations show that three possible kinds of behavior can be attained with the model, through proper choice of the GI parameters and attention input: (i) large objects in the visual scene are perceived, while small details are suppressed; (ii) large objects are perceived, while details are assembled together to constitute a single 'noise term'; (iii) if attention is focused on a smaller area and the GI parameters modulated accordingly (i.e. the threshold and attention strength are reduced) details are individually perceived as separate objects. These results suggest that the GI and attention may represent two concurrent aspects of the same attentive mechanism, i.e. they should work together to provide flexible management of a visual scene at different levels of detail.     

Range image segmentation using a relaxation oscillator network

A locally excitatory globally inhibitory oscillator network (LEGION) is constructed and applied to range image segmentation, where each oscillator has excitatory lateral connections to the oscillators in its local neighborhood as well as a connection with a global inhibitor. A feature vector, consisting of depth, surface normal, and mean and Gaussian curvatures, is associated with each oscillator and is estimated from local windows at its corresponding pixel location. A context-sensitive method is applied in order to obtain more reliable and accurate estimations. The lateral connection between two oscillators is established based on a similarity measure of their feature vectors. The emergent behavior of the LEGION network gives rise to segmentation. Due to the flexible representation through phases, our method needs no assumption about the underlying structures in image data and no prior knowledge regarding the number of regions. More importantly, the network is guaranteed to converge rapidly under general conditions. These unique properties may lead to a real-time approach for range image segmentation in machine perception.     

A Dynamic Network Model of the Color Visual Pathways for Attentive Recognition

A neural network architecture for the segmentation and recognition of colored and textured visual stimuli is presented. The architecture is based on the Boundary Contour System and Feature Contour System (BCS/FCS) of S. Grossberg and E. Mingolla. The architecture proposes a biologically-inspired mechanism for color processing based on antagonist interactions. It suggests how information from different modalities (i.e. color or texture) can be fused together to form a coherent segmentation of the visual scene. It identifies two stages of visual pattern recognition, namely, a global preattentive recognition of the visual scene followed by a local attentive recognition within a particular visual context. The global and local classification and recognition of visual stimuli use ART-type models of G. Carpenter and S. Grossberg for pattern learning and recognition based on color and texture. One example is presented corresponding to an figure-figure separation task. The architecture provides a mechanism for segmentation, categorization and recognition of images from different classes based on self-organizing principles of perception and pattern recognition.     

Experimental study on arranging music by chaotic neural network

Based on the recalling ability on dynamic (chaotic) associative memory of neural networks, we have proposed two methods for making variations of an original melody. By computer simulations, we have shown candidates for the variation of the original melody taken from the first 16 bars of Minuet G major by Bach. The results obtained in this article may suggest a possibility that chaotic neural networks can excuse such a creative task as making variations of an original melody. © 1997 John Wiley & Sons, Inc.     

Emergent synchrony in locally coupled neural oscillators

The discovery of long range synchronous oscillations in the visual cortex has triggered much interest in understanding the underlying neural mechanisms and in exploring possible applications of neural oscillations. Many neural models thus proposed end up relying on global connections, leading to the question of whether lateral connections alone can produce remote synchronization. With a formulation different from frequently used phase models, we find that locally coupled neural oscillators can yield global synchrony. The model employs a previously suggested mechanism that the efficacy of the connections is allowed to change on a fast time scale. Based on the known connectivity of the visual cortex, the model outputs closely resemble the experimental findings. Furthermore, we illustrate the potential of locally connected oscillator networks in perceptual grouping and pattern segmentation, which seems missing in globally connected ones.     

Synchronization and desynchronization in a network of locallycoupled Wilson-Cowan oscillators

A network of Wilson-Cowan (WC) oscillators is constructed, and its emergent properties of synchronization and desynchronization are investigated by both computer simulation and formal analysis. The network is a 2D matrix, where each oscillator is coupled only to its neighbors. We show analytically that a chain of locally coupled oscillators (the piecewise linear approximation to the WC oscillator) synchronizes, and we present a technique to rapidly entrain finite numbers of oscillators. The coupling strengths change on a fast time scale based on a Hebbian rule. A global separator is introduced which receives input from and sends feedback to each oscillator in the matrix. The global separator is used to desynchronize different oscillator groups. Unlike many other models, the properties of this network emerge from local connections that preserve spatial relationships among components and are critical for encoding Gestalt principles of feature grouping. The ability to synchronize and desynchronize oscillator groups within this network offers a promising approach for pattern segmentation and figure/ground segregation based on oscillatory correlation.     

Chaotic exploration and learning of locomotion behaviors

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage.     

Temporal coding with synchronization and acceleration as complementary mechanisms

Temporal coding requires an appropriate combination of synchronizing and desynchronizing mechanisms. We study temporal coding with a desynchronizing mechanism, referred to as acceleration, that causes the units of the network to oscillate with higher phase velocity in case of stronger and/or more coherent input. Recently, it was demonstrated that combining synchronization with acceleration implies a competition for coherence that has a profound and favorable effect on the segmentation of overlapping patterns. Here, we demonstrate, first, an effect of coupling non-identical oscillators. Second, we discuss how including inhibitory couplings enforces the segmenting effect of acceleration. Third, we show how stronger couplings between the units of a pattern may support it in winning this competition for coherence.     

Separation of speech from interfering sounds based on oscillatorycorrelation

A multistage neural model is proposed for an auditory scene analysis task-segregating speech from interfering sound sources. The core of the model is a two-layer oscillator network that performs stream segregation on the basis of oscillatory correlation. In the oscillatory correlation framework, a stream is represented by a population of synchronized relaxation oscillators, each of which corresponds to an auditory feature, and different streams are represented by desynchronized oscillator populations. Lateral connections between oscillators encode harmonicity, and proximity in frequency and time. Prior to the oscillator network are a model of the auditory periphery and a stage in which mid-level auditory representations are formed. The model has been systematically evaluated using a corpus of voiced speech mixed with interfering sounds, and produces improvements in terms of signal-to-noise ratio for every mixture. A number of issues including biological plausibility and real-time implementation are also discussed.     

Motion segmentation based on motion/brightness integration andoscillatory correlation

A segmentation method based on the integration of motion and brightness is proposed for image sequences. The method is composed of two parallel pathways that process motion and brightness, respectively, Inspired by the visual system, the motion pathway has two stages. The first stage estimates local motion at locations with reliable information. The second stage performs segmentation based on local motion estimates. In the brightness pathway, the input scene is segmented into regions based on brightness distribution. Subsequently, segmentation results from the two pathways are integrated to refine motion estimates. The final segmentation is performed in the motion network based on refined estimates. For segmentation, locally excitatory globally inhibitory oscillator network (LEGION) architecture is employed whereby the oscillators corresponding to a region of similar motion/brightness oscillate in synchrony and different regions attain different phases. Results on synthetic and real image sequences are provided, and comparisons with other methods are made.     

Temporal coding: assembly formation through constructive interference

Temporal coding is studied for an oscillatory neural network model with synchronization and acceleration. The latter mechanism refers to increasing (decreasing) the phase velocity of each unit for stronger (weaker) or more coherent (decoherent) input from the other units. It has been demonstrated that acceleration generates the desynchronization that is needed for self-organized segmentation of two overlapping patterns. In this letter, we continue the discussion of this remarkable feature, giving also an example with several overlapping patterns. Due to acceleration, Hebbian memory implies a frequency spectrum for pure pattern states, defined as coherent patterns with decoherent overlapping patterns. With reference to this frequency spectrum and related frequency bands, the process of pattern retrieval, corresponding to the formation of temporal coding assemblies, is described as resulting from constructive interference (with frequency differences due to acceleration) and phase locking (due to synchronization).     

Neural oscillator based control for pathological tremor suppression via functional electrical stimulation

This paper explores the possibility to adopt neural oscillators for pathological tremor attenuation. The objective is to suppress the tremor of a single joint of upper limb via functional electrical stimulation (FES). A biologically inspired neural oscillator is developed, which generates the anti-tremor rhythmic stimulation patterns to stimulate a pair of antagonist muscles. Surface electromyographic (EMG) signal is used to entrain the neural oscillator reciprocally and shape the stimulation pattern adaptively. The neural oscillator serves as an adaptive feedforward controller, which is combined with a feedback regulator. Simulation study is performed on musculoskeletal models of wrist joint and elbow joint separately, and some promising results are presented.     

Synchronization of nonlinear electronic oscillators for neural computation.

This paper deals with coupled oscillators as the building blocks of a bioinspired computing paradigm and their implementation. In order to accomplish the low-power and fast-processing requirements of autonomous applications, we study the microelectronic analog implementation of physical oscillators, instead of the software computer-simulated implementation. With this aim, the original oscillator has been adapted to a suitable microelectronic form. So as to study the hardware nonlinear oscillators, we propose two macro models, demonstrating that they preserve the synchronization properties. Secondary effects such as mismatch and output delay and their relation to network synchronization are analyzed and discussed. We show the correct operation of the proposed electronic oscillators with simulations and experimental results from a manufactured integrated test circuit. The proposed architecture is intended to perform the scene segmentation stage of an autonomous focal-plane self-contained visual processing system for artificial vision applications.