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.     

混沌神经网络在分离叠加模式和多对多联想记忆中的应用

混沌是不含外加随机因素的完全确定性的系统表现出来的界于规则和随机之间的内秉随机行为。脑神经系统是由神经细胞组成的网络。类似于人脑思维的人工神经网络与冯·诺依曼计算机相比,在信息处理方面有很大的优越性。混沌和神经网络相互融合的研究是从90年代开始的,其主要的目标是通过分析大脑的混沌现象,建立含有混沌动力学的神经网络模型(即混沌神经网络模型),将混沌的遍历性、对初始值敏感等特点与神经网络的非线性、自适应、并行处理优势相结合,     

Competition and cooperation in neuronal processing

A new type of model neuron is introduced as a building block of an associative memory. The neuron, which has a number of receptor zones, processes both the amplitude and the frequency of input signals, associating a small number of features encoded by those signals. Using this two-parameter input in our model compared to the one-dimensional inputs of conventional model neurons (e.g., the McCulloch Pitts neuron) offers an increased memory capacity. In our model, there is a competition among inputs in each zone with a subsequent cooperation of the winners to specify the output. The associative memory consists of a network of such neurons. A state-space model is used to define the neurodynamics. We explore properties of the neuron and the network and demonstrate its favorable capacity and recall capabilities. Finally, the network is used in an application designed to find trademarks that sound alike.     

Multilayer associative neural networks (MANN''s): storage capacityversus perfect recall

The objective of this paper is to to resolve important issues in artificial neural nets-exact recall and capacity in multilayer associative memories. These problems have imposed restrictions on coding strategies. We propose the following triple-layered hybrid neural network: the first synapse is a one-shot associative memory using the modified Kohonen's adaptive learning algorithm with arbitrary input patterns; the second one is Kosko's bidirectional associative memory consisting of orthogonal input/output basis vectors such as Walsh series satisfying the strict continuity condition; and finally, the third one is a simple one-shot associative memory with arbitrary output images. A mathematical framework based on the relationship between energy local minima (capacity of the neural net) and noise-free recall is established. The robust capacity conditions of this multilayer associative neural network that lead to forming the local minima of the energy function at the exact training pairs are derived. The chosen strategy not only maximizes the total number of stored images but also completely relaxes any code-dependent conditions of the learning pairs.     

A modular computer vision system for picture segmentation and interpretation

The objective of a computer vision system is to outline the objects in a picture and label them with an appropriate interpretation. This paper proposes a new paradigm for a modular computer vision system which is both data directed and knowledge based. The system consists of three different types of units, two of which are associative data memories implemented as relational databases. The short-term memory (STM) contains the raw color picture data and the most current interpretations and deductions about the original scene. The long-term memory (LTM) contains a detailed model of the scene under consideration. A collection of analysis processors, each of which is specialized for a particular task, can communicate with both of these memories. The information in the LTM remains unchanged during the analysis, while the STM is being continually updated and revised by the appropriate processors. The latter may be conceived of as being activated by certain data conditions in the STM, and using the information in both the LTM and STM to alter the status of the STM.     

A transient-chaotic autoassociative network (TCAN) based on Lee oscillators.

In the past few decades, neural networks have been extensively adopted in various applications ranging from simple synaptic memory coding to sophisticated pattern recognition problems such as scene analysis. Moreover, current studies on neuroscience and physiology have reported that in a typical scene segmentation problem our major senses of perception (e.g., vision, olfaction, etc.) are highly involved in temporal (or what we call "transient") nonlinear neural dynamics and oscillations. This paper is an extension of the author's previous work on the dynamic neural model (EGDLM) of memory processing and on composite neural oscillators for scene segmentation. Moreover, it is inspired by the work of Aihara et al. and Wang on chaotic neural oscillators in pattern association. In this paper, the author proposes a new transient chaotic neural oscillator, namely the "Lee oscillator," to provide temporal neural coding and an information processing scheme. To illustrate its capability for memory association, a chaotic autoassociative network, namely the Transient-Chaotic Auto-associative Network (TCAN) was constructed based on the Lee oscillator. Different from classical autoassociators such as the celebrated Hopfield network, which provides a "time-independent" pattern association, the TCAN provides a remarkable progressive memory association scheme [what we call "progressive memory recalling" (PMR)] during the transient chaotic memory association. This is exactly consistent with the latest research in psychiatry and perception psychology on dynamic memory recalling schemes.     

A New Associative Model with Dynamical Synapses

The brain is not a huge fixed neural network, but a dynamic, changing neural network that continuously adapts to meet the demands of communication and computational needs. In classical neural networks approaches, particularly associative memory models, synapses are only adjusted during the training phase. After this phase, synapses are no longer adjusted. In this paper we describe a new dynamical model where synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. We provide some propositions that guarantee perfect and robust recall of the fundamental set of associations. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model.     

Autonomous learning with complex dynamics

Traditionally, associative memory models are based on point attractor dynamics, where a memory state corresponds to a stationary point in state space. However, biological neural systems seem to display a rich and complex dynamics whose function is still largely unknown. We use a neural network model of the olfactory cortex to investigate the functional significance of such dynamics, in particular with regard to learning and associative memory. the model uses simple network units, corresponding to populations of neurons connected according to the structure of the olfactory cortex. All essential dynamical properties of this system are reproduced by the model, especially oscillations at two separate frequency bands and aperiodic behavior similar to chaos. By introducing neuromodulatory control of gain and connection weight strengths, the dynamics can change dramatically, in accordance with the effects of acetylcholine, a neuromodulator known to be involved in attention and learning in animals. With computer simulations we show that these effects can be used for improving associative memory performance by reducing recall time and increasing fidelity. the system is able to learn and recall continuously as the input changes, mimicking a real world situation of an artificial or biological system in a changing environment. © 1995 John Wiley & Sons, Inc.     

Part 3: Brain science, information science and associative memory model

I review recent progress on the associative memory model, which is a kind of neural network model. First, I introduce this model and a mathematical theory called statistical neurodynamics describing its properties. Next, I discuss an associative memory model with hierarchically correlated memory patterns. Initially, in this model, the state approaches a mixed state that is a superposition of memory patterns. After that, it diverges from the mixed state, and finally converges to a memory pattern. I show that this retrieval dynamics can qualitatively replicate the temporal dynamics of face-responsive neurons in the inferior temporal cortex, which is considered to be the final stage of visual perception in the brain. Finally, I show an unexpected link between associative memory and mobile phones (CDMA). The mathematical structure of the CDMA multi-user detection problem resembles that of the associative memory model. It enables us to apply a theoretical framework of the associative memory model to CDMA.     

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 Bidirectional Hetero-Associative Memory for True-Color Patterns

Classical bidirectional associative memories (BAM) have poor memory storage capacity, are sensitive to noise, are subject to spurious steady states during recall, and can only recall bipolar patterns. In this paper, we introduce a new bidirectional hetero-associative memory model for true-color patterns that uses the associative model with dynamical synapses recently introduced in Vazquez and Sossa (Neural Process Lett, Submitted, 2008). Synapses of the associative memory could be adjusted even after the training phase as a response to an input stimulus. Propositions that guarantee perfect and robust recall of the fundamental set of associations are provided. In addition, we describe the behavior of the proposed associative model under noisy versions of the patterns. At last, we present some experiments aimed to show the accuracy of the proposed model with a benchmark of true-color patterns.     

Distributed associative memory for use in scene analysis

A distributed associative memory system which is ideal for scene analysis is described. Recall of associated patterns using incomplete originals is made possible by the use of a distributed storage mechanism and a novel recall procedure. The memory is shown to store associations between patterns more efficiently than a conventional file store. The paper describes the memory structure, the recall process and its storage abilities, as well as an example of its implementation in hardware.     

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.     

Adaptive associative memories capable of pattern segmentation

This paper presents an adaptive type of associative memory (AAM) that can separate patterns from composite inputs which might be degraded by deficiency or noise and that can recover incomplete or noisy single patterns. The behavior of AAM is analyzed in terms of stability, giving the stable solutions (results of recall), and the recall of spurious memories (the undesired solutions) is shown to be greatly reduced compared with earlier types of associative memory that can perform pattern segmentation. Two conditions that guarantee the nonexistence of undesired solutions are also given. Results of computer experiments show that the performance of AAM is much better than that of the earlier types of associative memory in terms of pattern segmentation and pattern recovery.     

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.     

Working memory modeling using inverse fuzzy relational approach

The paper proposes a novel approach to fuzzy modeling of human working memory (WM) using electroencephalographic (EEG) signals, acquired during human face encoding and recall experiments in connection with a face recognition problem. The EEG signals acquired from the short term memory (STM) during memory encoding instances are considered as the input of the proposed working memory model. On the other hand, the EEG response of the WM to visual stimuli acquired during WM recall instances are considered as the output of the proposed working memory model. The entire experiment is primarily divided into two phases. In the first phase, the WM of a human subject is modeled by a fuzzy implication relation, describing a mapping from the STM response (during encoding) to the WM responses (during recall) to visual stimuli. During STM encoding, the subject is visually presented with the full face stimulus of a person. During WM recall, four partial face stimuli of the same person (made familiar during encoding) are used for the subject to recall the respective full face.The second phase is undertaken to validate the WM model by visually stimulating the subject again with randomly selected partial faces of people, being familiar in the first phase and the WM EEG responses are recorded. The WM responses along with the WM model, developed in the first phase, are used to retrieve the STM information by using an inverse fuzzy (implication) relation. Besides WM modeling, another important contribution of the paper lies in devising a solution to the inverse fuzzy relation computation in the settings of an optimization problem. An error metric is then defined to measure the discrepancy between the model-predicted STM encoding pattern and the actual pattern encoded by the STM (as captured by the EEG signal during encoding in the first phase). Apparently, smaller the error magnitude better is the accuracy of the proposed model to effectively differentiate people with memory failures. Experimentally it is observed that the proposed model yields a very small error, in the order of 10⁻⁴, thus showing a high level of similarity between actual and model predicted STM response for all the healthy subjects. An experiment undertaken using eLORETA software confirms that the orbito-frontal cortex of prefrontal lobe is responsible for STM encoding whereas dorsolateral prefrontal region is responsible for WM recall. An analysis undertaken reveals that the proposed WM model produces the best response in the theta frequency band of EEG spectra, thus assuring the association of the theta frequency range in the face recognition task. Comparative analysis performed also substantiates that the proposed technique of computing max–min inverse fuzzy relation outperforms the existing techniques for inverse fuzzy computation, with a successful retrieval accuracy of 87.92%. The proposed study would find interesting applications to diagnose memory failures for people with Pre-frontal lobe amnesia.     

对数-指数形态学联想记忆

利用对数和指数算子构建了一种新的形态学联想记忆方法,简称LEMAM.理论分析表明:自联想LEMAM(简称ALEMAM)具有无限存储能力、一步回忆记忆、一定的抵抗腐蚀噪声或膨胀噪声的能力,在输入完全或在一定的噪声范围内,能够保证完全回忆记忆;异联想LEMAM(简称HLEMAM)在输入完全情况下,不能保证完全回忆记忆,但当满足一定条件时,也能够达到完美联想记忆.对比实验结果表明:在一些情况下,LEMAM能够取得较好的联想记忆效果.总体来说,LEMAM丰富了形态学联想记忆的理论和实践,可以作为一种神经计算模型加以研究和利用.     

An autoassociative neural network model of paired-associate learning

Hebbian heteroassociative learning is inherently asymmetric. Storing a forward association, from item A to item B, enables recall of B (given A), but does not permit recall of A (given B). Recurrent networks can solve this problem by associating A to B and B back to A. In these recurrent networks, the forward and backward associations can be differentially weighted to account for asymmetries in recall performance. In the special case of equal strength forward and backward weights, these recurrent networks can be modeled as a single autoassociative network where A and B are two parts of a single, stored pattern. We analyze a general, recurrent neural network model of associative memory and examine its ability to fit a rich set of experimental data on human associative learning. The model fits the data significantly better when the forward and backward storage strengths are highly correlated than when they are less correlated. This network-based analysis of associative learning supports the view that associations between symbolic elements are better conceptualized as a blending of two ideas into a single unit than as separately modifiable forward and backward associations linking representations in memory.     

Dynamics of selective recall in an associative memory model with one-to-many associations.

The dynamics of selective recall in an associative memory model are analyzed in the scenario of one-to-many association. The present model, which can deal with one-to-many association, consists of a heteroassociative network and an autoassociative network. In the heteroassociative network, a mixture of associative items in one-to-many association is recalled by a key item. In the autoassociative network, the selective recall of one of the associative items is examined by providing a seed of a target item either to the heteroassociative network (Model 1) or to the autoassociative network (Model 2). We show that the critical similarity of Model 2 is not sensitive to the change in the dimension ratio of key vectors to associative vectors, and it has smaller critical similarity than Model 1 for a large initial overlap. On the other hand, we show that Model 1 has smaller critical similarity for a small initial overlap. We also show that unreachable equilibrium states exist in the proposed model.     

A neural network for visual pattern recognition

A model of a neural network is presented that offers insight into the brain's complex mechanisms as well as design principles for information processors. The model has properties and abilities that most modern computers and pattern recognizers do not possess; pattern recognition, selective attention, segmentation, and associative recall. When a composite stimulus consisting of two or more patterns is presented, the model pays selective attention to each of the patterns one after the other, segments a pattern from the rest, and recognizes it separately in contrast to earlier models. This model has perfect associative recall, even for deformed patterns, without regard to their positions. It can be trained to recognize any set of patterns