Visual information processing using bacteriorhodopsin-based complex LB films

Image extraction and visual information processing using bacteriorhodopsin (bR)-based bioelectronic devices is presented. Image extraction was achieved using a photoreceptor consisting of bR and spiropyran films. The undesired signals from the photoreceptor were automatically eliminated from the whole signal by spiropyran films acting as an optical noise filter that increases the target signal to an undesired signal ratio. For the information processing, the photoreceptor consisting of bR and lipid films deposited with different configurations was used and the target signals were processed to achieve the pattern recognition. The pattern recognition was based on not only the response variability of bacteriorhodopsin, induced by different film configurations, but also on the initial learning process. The input patterns were predicted by simple calculation with the known signals through the initial learning process.     

Vibrotaktile Informationsübertragung mit Folgen binärer Zeichen

Results of experiments in transmitting information by the aid of the sense of touch are presented. The patterns are limited sequences of binary signals, presented as vibrotactile pulses at the forearm. The information, transmitted by a signal decreases with the length of the sequence and the serial number of the signal within the sequence. With increasing difficulty in pattern recognition the information transmitted by the whole pattern exceeds the sum of the information transmitted by the signals. It is shown, that the process during a sequence is not stationary. Finally it is shown, how the recognition of signals is correlated to the adjoining signals in the sequence.     

Storage of a temporal pattern sequence in a network

Learning of single patterns and a temporal pattern sequence in a network when the coupling coefficients between the network elements change their values according to a definite coupling function is described. In contrast to technical systems (e.g. film, tape) where temporal sequences are often encoded in the storage location, the network stores information only by changing the values of the coupling coefficients. A network of 100 elements was simulated on an UNIVAC 1100/80 computer. Eight single patterns and a sequence of these patterns were offered at the input of the network. After the learning process the network reproduces every stored pattern as an output signal when only parts of it are fed in. The activity, that is the sum of all output signals, is regulated by an external control signal. By setting that control signal to a suitable value the network is able to reproduce the stored pattern sequence starting from any arbitrary pattern. Lowering the external control signal during that process causes the network to hold the last presented pattern until the external control signal is changed again. It is speculated that the coupling function implemented in the simulation may be anaogous to a characteristic describing the chemical process of cooperative binding.Supported by DFG (Ha 381/9 and Ha 381/11)     

生命机体危险因子信息特征分析

旨在生命机体与危险因子间建立生命物质信息对话平台,以物质化的信息,或信息化的物质的思维进行理解和推论,建立物质承载信息,信息通过物质的形式传递的思维模式,对生命信息中危险因子的属性、分类以及生命机体对危险因子的识别方式进行分析,从以上两个方面不同角度阐述危险因子与生命机体机制信息的相关互动关系。在分析与解读的过程中,主张建立合乎自然生成逻辑的科学概念,而摒弃一些主观是非逻辑所推导建立起来的概念,还原生命物质和生命过程的本来生物学位置及性质;主张一元化的思考方式和整体性理解生命信息安全控制机制的核心部分。分析结果:就生命信息中危险因子的属性和分类进行了分析,指出危险因子具有生命信息属性和生命安全属性两个属性,依据危险因子物质来源的不同,分外源性和内源性两类,绘出危险因子来源分类汇总图;从机体方面对危险因子识别方式不同,分为PRR识别方式和抗原(样)受体识别方式两类,并绘出危险因子识别方式分类汇总图。综合危险因子的属性、分类和生命机体对危险因子的识别方式,绘制出危险因子与生命机体机制信息的相关互动图,从保守性结构信息、生理产物信息、对机体损伤信息、代谢通路信息、对机体变应信息、精细纯外观构象信息及内在性核心信息7个方面对两者的相互关系进行了引证和分析,进一步阐释了生命信息安全控制机制。     

Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.     

基于表面肌电信号的人体动作识别算法研究进展

表面肌电信号(Surface Electromyography,sEMG)是通过相应肌群表面的传感器记录下来的一维时间序列非平稳生物电信号,不但反映了神经肌肉系统活动,对于反映相应动作肢体活动信息同样重要。而模式识别是肌电应用领域的基础和关键。为了在应用基于表面肌电信号模式识别中选取合适算法,本文拟对基于表面肌电信号的人体动作识别算法进行回顾分析,主要包括模糊模式识别算法、线性判别分析算法、人工神经网络算法和支持向量机算法。模糊模式识别能自适应提取模糊规则,对初始化规则不敏感,适合处理s EMG这样具有严格不重复的生物电信号;线性判别分析对数据进行降维,计算简单,但不适合大数据;人工神经网络可以同时描述训练样本输入输出的线性关系和非线性映射关系,可以解决复杂的分类问题,学习能力强;支持向量机处理小样本、非线性的高维数据优势明显,计算速度快。比较各方法的优缺点,为今后处理此类问题模式识别算法选取提供了参考和依据。     

听觉中枢神经元对声信号的识别和处理

人类听觉的基本特性和机制与其他哺乳动物相似,因此,利用动物所作的听觉研究和获得的结果,有助于认识人类自身的听觉.围绕听觉中枢神经元对不同模式的声信号的识别和处理,简要综述了这方面的研究.声信号和声模式识别在听觉中枢对声信号的感受和加工中具有重要意义.听神经元作为声模式识别的结构和功能基础,对不同的声刺激模式产生不同反应,甚至是在同一声刺激模式下,改变其中的某个声参数,神经元的反应也会发生相应改变,而其反应的特性和机制均需要更多研究来解答.另外,声信号作为声信息的载体,不同的声信息寓于不同的声参数和声特征之中,研究发现,听觉中枢神经元存在相应的声信息甄别和选择的神经基础,能对动态变化的声频率、幅度和时程等进行反应和编码,并且,在不同种类动物上获得的研究结果极为相似,表明听觉中枢对不同声信号和声刺激模式的识别、分析和加工,具有共同性和普遍性.     

Learning and recall in a dynamic theory of coordination patterns

A dynamic theory of learning and recall of coordination patterns is developed in the context of relative timing skills. Characterizing the coordination patterns in such skills by the collective variable, relative phase, we choose a model system in which the intrinsic pattern dynamics as well as the influence of environmental and memorized information are well understood from previous experimental and theoretical work. To describe learning we endow memorized information with dynamics which is determined by a phenomenological strategy. Similarly, additional degrees of freedom must be introduced to understand recall. As such recall variables we choose the relative strengths with which each memorized pattern acts on the pattern dynamics and model their dynamics phenomenologically. The resulting dynamical system that resembles models used in pattern recognition theory is shown to adequately describe the learning and recall processes. Moreover, due to the operational character of the theory, several predictions emerge that are open to experimental test. In particular, we show under which conditions phase transitions occur in the dynamics of the coordination patterns during learning and during recall. Considering different time scales and their relations we demonstrate how these phase transitions can be identified and observed. Other predictions include the influence of the intrinsic pattern dynamics on the recall process and the existence of history and hysteresis effects in recall. We discuss different forms of forgetting and differentiation of memorized information. The results show how a new theoretical view of learning and recall as change of behavioral dynamics can lead to a different understanding of these processes by providing testable predictions.     

Classification using sparse representations: a biologically plausible approach

Representing signals as linear combinations of basis vectors sparsely selected from an overcomplete dictionary has proven to be advantageous for many applications in pattern recognition, machine learning, signal processing, and computer vision. While this approach was originally inspired by insights into cortical information processing, biologically plausible approaches have been limited to exploring the functionality of early sensory processing in the brain, while more practical applications have employed non-biologically plausible sparse coding algorithms. Here, a biologically plausible algorithm is proposed that can be applied to practical problems. This algorithm is evaluated using standard benchmark tasks in the domain of pattern classification, and its performance is compared to a wide range of alternative algorithms that are widely used in signal and image processing. The results show that for the classification tasks performed here, the proposed method is competitive with the best of the alternative algorithms that have been evaluated. This demonstrates that classification using sparse representations can be performed in a neurally plausible manner, and hence, that this mechanism of classification might be exploited by the brain.     

Automatically inferred Markov network models for classification of chromosomal band pattern structures

A structural pattern recognition approach to the analysis and classification of metaphase chromosome band patterns is presented. An operational method of representing band pattern profiles as sharp edged idealized profiles is outlined. These profiles are nonlinearly scaled to a few, but fixed number of "density" levels. Previous experience has shown that profiles of six levels are appropriate and that the differences between successive bands in these profiles are suitable for classification. String representations, which focuses on the sequences of transitions between local band pattern levels, are derived from such "difference profiles." A method of syntactic analysis of the band transition sequences by dynamic programming for optimal (maximal probability) string-to-network alignments is described. It develops automatic data-driven inference of band pattern models (Markov networks) per class, and uses these models for classification. The method does not use centromere information, but assumes the p-q-orientation of the band pattern profiles to be known a priori. It is experimentally established that the method can build Markov network models, which, when used for classification, show a recognition rate of about 92% on test data. The experiments used 200 samples (chromosome profiles) for each of the 22 autosome chromosome types and are designed to also investigate various classifier design problems. It is found that the use of a priori knowledge of Denver Group assignment only improved classification by 1 or 2%. A scheme for typewise normalization of the class relationship measures prove useful, partly through improvements on average results and partly through a more evenly distributed error pattern. The choice of reference of the p-q-orientation of the band patterns is found to be unimportant, and results of timing of the execution time of the analysis show that recent and efficient implementations can process one cell in less than 1 min on current standard hardware. A measure of divergence between data sets and Markov network models is shown to provide usable estimates of experimental classification performance.     

Solution structure of a zinc substituted eukaryotic rubredoxin from the cryptomonad alga Guillardia theta

The rubredoxin from the cryptomonad Guillardia theta is one of the first examples of a rubredoxin encoded in a eukaryotic organism. The structure of a soluble zinc-substituted 70-residue G. theta rubredoxin lacking the membrane anchor and the thylakoid targeting sequence was determined by multidimensional heteronuclear NMR, representing the first three-dimensional (3D) structure of a eukaryotic rubredoxin. For the structure calculation a strategy was applied in which information about hydrogen bonds was directly inferred from a long-range HNCO experiment, and the dynamics of the protein was deduced from heteronuclear nuclear Overhauser effect data and exchange rates of the amide protons. The structure is well defined, exhibiting average root-mean-square deviations of 0.21 A for the backbone heavy atoms and 0.67 A for all heavy atoms of residues 7-56, and an increased flexibility toward the termini. The structure of this core fold is almost identical to that of prokaryotic rubredoxins. There are, however, significant differences with respect to the charge distribution at the protein surface, suggesting that G. theta rubredoxin exerts a different physiological function compared to the structurally characterized prokaryotic rubredoxins. The amino-terminal residues containing the putative signal peptidase recognition/cleavage site show an increased flexibility compared to the core fold, but still adopt a defined 3D orientation, which is mainly stabilized by nonlocal interactions to residues of the carboxy-terminal region. This orientation might reflect the structural elements and charge pattern necessary for correct signal peptidase recognition of the G. theta rubredoxin precursor.     

Vibrotactile feedback for conveying object shape information as perceived by artificial sensing of robotic arm

This work is a preliminary study towards developing an alternative communication channel for conveying shape information to aid in recognition of items when tactile perception is hindered. Tactile data, acquired during object exploration by sensor fitted robot arm, are processed to recognize four basic geometric shapes. Patterns representing each shape, classified from tactile data, are generated using micro-controller-driven vibration motors which vibrotactually stimulate users to convey the particular shape information. These motors are attached on the subject’s arm and their psychological (verbal) responses are recorded to assess the competence of the system to convey shape information to the user in form of vibrotactile stimulations. Object shapes are classified from tactile data with an average accuracy of 95.21 %. Three successive sessions of shape recognition from vibrotactile pattern depicted learning of the stimulus from subjects’ psychological response which increased from 75 to 95 %. This observation substantiates the learning of vibrotactile stimulation in user over the sessions which in turn increase the system efficacy. The tactile sensing module and vibrotactile pattern generating module are integrated to complete the system whose operation is analysed in real-time. Thus, the work demonstrates a successful implementation of the complete schema of artificial tactile sensing system for object-shape recognition through vibrotactile stimulations.     

Nonvisual motor training influences biological motion perception

Experimental evidence suggests a link between perception and the execution of actions . In particular, it has been proposed that motor programs might directly influence visual action perception . According to this hypothesis, the acquisition of novel motor behaviors should improve their visual recognition, even in the absence of visual learning. We tested this prediction by using a new experimental paradigm that dissociates visual and motor learning during the acquisition of novel motor patterns. The visual recognition of gait patterns from point-light stimuli was assessed before and after nonvisual motor training. During this training, subjects were blindfolded and learned a novel coordinated upper-body movement based only on verbal and haptic feedback. The learned movement matched one of the visual test patterns. Despite the absence of visual stimulation during training, we observed a selective improvement of the visual recognition performance for the learned movement. Furthermore, visual recognition performance after training correlated strongly with the accuracy of the execution of the learned motor pattern. These results prove, for the first time, that motor learning has a direct and highly selective influence on visual action recognition that is not mediated by visual learning.     

Identifying DNA splice sites using hypernetworks with artificial molecular evolution

Identifying DNA splice sites is a main task of gene hunting. We introduce the hyper-network architecture as a novel method for finding DNA splice sites. The hypernetwork architecture is a biologically inspired information processing system composed of networks of molecules forming cells, and a number of cells forming a tissue or organism. Its learning is based on molecular evolution. DNA examples taken from GenBank were translated into binary strings and fed into a hypernetwork for training. We performed experiments to explore the generalization performance of hypernetwork learning in this data set by two-fold cross validation. The hypernetwork generalization performance was comparable to well known classification algorithms. With the best hypernetwork obtained, including local information and heuristic rules, we built a system (HyperExon) to obtain splice site candidates. The HyperExon system outperformed leading splice recognition systems in the list of sequences tested.     

An information-theoretic characterization of the optimal gradient sensing response of cells

Many cellular systems rely on the ability to interpret spatial heterogeneities in chemoattractant concentration to direct cell migration. The accuracy of this process is limited by stochastic fluctuations in the concentration of the external signal and in the internal signaling components. Here we use information theory to determine the optimal scheme to detect the location of an external chemoattractant source in the presence of noise. We compute the minimum amount of mutual information needed between the chemoattractant gradient and the internal signal to achieve a prespecified chemotactic accuracy. We show that more accurate chemotaxis requires greater mutual information. We also demonstrate that a priori information can improve chemotaxis efficiency. We compare the optimal signaling schemes with existing experimental measurements and models of eukaryotic gradient sensing. Remarkably, there is good quantitative agreement between the optimal response when no a priori assumption is made about the location of the existing source, and the observed experimental response of unpolarized Dictyostelium discoideum cells. In contrast, the measured response of polarized D. discoideum cells matches closely the optimal scheme, assuming prior knowledge of the external gradient-for example, through prolonged chemotaxis in a given direction. Our results demonstrate that different observed classes of responses in cells (polarized and unpolarized) are optimal under varying information assumptions.     

A novel biologically and psychologically inspired fuzzy decision support system: hierarchical complementary learning

A computational intelligent system that models the human cognitive abilities may promise significant performance in problem learning because human is effective in learning and problem solving. Functionally modelling the human cognitive abilities not only avoids the details of the underlying neural mechanisms performing the tasks, but also reduces the complexity of the system. The complementary learning mechanism is responsible for human pattern recognition, i.e. human attends to positive and negative samples when making decision. Furthermore, human concept learning is organized in a hierarchical fashion. Such hierarchical organization allows the divide-and-conquer approach to the problem. Thus, integrating the functional models of hierarchical organization and complementary learning can potentially improve the performance in pattern recognition. Hierarchical complementary learning (HCL) exhibits many of the desirable features of pattern recognition. It is further supported by the experimental results that verify the rationale of the integration and that the HCL system is a promising pattern recognition tool.     

KIN RECOGNITION: FUNCTIONS AND MECHANISMS A REVIEW

1. The aim of this paper has been to review the theory behind kin recognition to examine the benefits individuals obtain by recognizing their kin and to review the mechanisms used by individuals in their recognition of kin. 2. The ability to discriminate between kin and non-kin, and between different classes of kin gives individuals advantages in fitness greater than individuals unable to recognize their kin. Four specific areas of benefit were considered: altruistic behaviour, co-operative behaviour, parental care and mate choice. Finally the possibility that kin recognition has arisen as a byproduct from some other ability was discussed. 3. Mechanisms of kin recognition were considered with respect to three essential components of kin recognition. The cue used to discriminate kin, how individuals classify conspecifics as kin, etc. and how the ability to recognize kin develops. 4. Individuals can use a number of cues to discriminate kin from non-kin. These were divided into cues presented by conspecifics (conspecific cues), of which three types were considered: individual, genetic and group/colony cues, and non-conspecific cues, environmental, state and no cues. Kin recognition could be achieved by use of all these cues. 5. How individuals classify their conspecifics as kin, etc. can be achieved in a number of ways; dishabituation or self-matching, which require no learning of kinship cues, or by phenotype matching or familiarity, both of which require the learning of kinship information. 6. It may be necessary for individuals to acquire information concerning kinship. This may be learned, and can be achieved in a number of ways; physiological imprinting, exposure learning or associative learning. Acquisition by these means is non-selective, in that the cues which are most salient in the individual's environment will be learned. Selectivity can be introduced into this process to increase the probability of acquiring kinship information by a number of means; learning from parents, sensitive periods for learning and prenatal learning. Finally, kinship information could be supplied by recognition genes. 7. A distinction is drawn between cues which are used by an individual in the discrimination of kin, discriminators, and cues which are used by individuals in the acquisition of information about kinship, acquisitors. 8. Experiments used to support previous categories of mechanisms of kin recognition were examined in the light of this discussion and it was found that the results were open to a number of different interpretations and yielded little specific information about the mechanisms of kin recognition. 9. It was concluded that there was much evidence, both theoretical and experimental to support the proposed benefits individuals gain from recognizing kin, but much more research is required before the mechanisms of kin recognition are fully understood.     

Learning to recognize objects

Several aspects of systems for learning pattern or object recognition rules are discussed. First, how are recognition rules developed and to what extent is structural pattern information embedded into these recognition rules. Second, how are these rules applied to the recognition of complex patterns such as objects embedded in scenes and how is evidence from different rules combined into a single evidence vector. Third, how can learned rules be improved through performance evaluation and feedback to rule generation stages.     

A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task.