Structure learning in action.

'Learning to learn' phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated-a process termed 'learning to learn'. From a computational point of view such an extraction of invariants can be regarded as learning of an underlying structure. Here we review the evidence for structure learning as a 'learning to learn' mechanism, especially in sensorimotor control where the motor system has to adapt to variable environments. We review studies demonstrating that common features of variable environments are extracted during sensorimotor learning and exploited for efficient adaptation in novel tasks. We conclude that structure learning plays a fundamental role in skill learning and may underlie the unsurpassed flexibility and adaptability of the motor system.

Accelerated evolution of the pituitary adenylate cyclase-activating polypeptide precursor gene during human origin

Pituitary adenylate cyclase-activating polypeptide (PACAP) is a neuropeptide abundantly expressed in the central nervous system and involved in regulating neurogenesis and neuronal signal transduction. The amino acid sequence of PACAP is extremely conserved across vertebrate species, indicating a strong functional constraint during the course of evolution. However, through comparative sequence analysis, we demonstrated that the PACAP precursor gene underwent an accelerated evolution in the human lineage since the divergence from chimpanzees, and the amino acid substitution rate in humans is at least seven times faster than that in other mammal species resulting from strong Darwinian positive selection. Eleven human-specific amino acid changes were identified in the PACAP precursors, which are conserved from murine to African apes. Protein structural analysis suggested that a putative novel Deuropeptide might have originated during human evolution and functioned in the human brain. Our data suggested that the PACAP precursor gene underwent adaptive changes during human origin and may have contributed to the formation of human cognition.

Genetic evidence of a strong functional constraint of neurotrypsin during primate evolution

Neurotrypsin is one of the extra-cellular serine proteases that are predominantly expressed in the brain and involved in neuronal development and function. Mutations in humans are associated with autosomal recessive non-syndromic mental retardation (MR). We studied the molecular evolution of neurotrypsin by sequencing the coding region of neurotrypsin in 11 representative non-human primate species covering great apes, lesser apes, Old World monkeys and New World monkeys. Our results demonstrated a strong functional constraint of neurotrypsin that was caused by strong purifying selection during primate evolution, an implication of an essential functional role of neurotrypsin in primate cognition. Further analysis indicated that the purifying selection was in fact acting on the SRCR domains of neurotrypsin, which mediate the binding activity of neurotrypsin to cell surface or extracellular proteins. In addition, by comparing primates with three other mammalian orders, we demonstrated that the absence of the first copy of the SRCR domain (exon 2 and 3) in mouse and rat was due to the deletion of this segment in the murine lineage. Copyright (C) 2005 S. Karger AG, Basel.

Functional characterization of the human-specific (type II) form of kallikrein 8, a gene involved in learning and memory

Kallikrein 8 (KLK8) is a serine protease functioning in the central nervous system, and essential in many aspects of neuronal activities. Sequence comparison and gene expression analysis among diverse primate species identified a human-specific splice for

Extract of Ginkgo biloba leaves reverses yohimbine-induced spatial working memory deficit in rats

Extract of Ginkgo biloba is used to alleviate age-related decline in cognitive function, which may be associated with the loss of catecholamines in the prefrontal cortex. The purpose of this study was to verify whether alpha-2 adrenergic activity is involved in the facilitative effects of extract of Ginkgo biloba on prefrontal cognitive function. Male Wistar rats were trained to reach criterion in the delayed alternation task (0, 25, and 50-s delay intervals). A pilot study found that 3 or 4 mg/kg of yohimbine (intraperitoneal) reduced the choice accuracy of the delayed alternation task in a dose and delay-dependent manner, without influencing motor ability or perseverative behaviour. Acute oral pre-treatment with doses of 50, 100, or 200 mg/kg (but not 25 mg/kg) of extract of Ginkgo biloba prevented the reduction in choice accuracy induced by 4 mg/kg yohimbine. These data suggest that the prefrontal cognition-enhancing effects of extract of Ginkgo biloba are related to its actions on alpha-2-adrenoceptors.

Optimal inference of sameness.

Deciding whether a set of objects are the same or different is a cornerstone of perception and cognition. Surprisingly, no principled quantitative model of sameness judgment exists. We tested whether human sameness judgment under sensory noise can be modeled as a form of probabilistically optimal inference. An optimal observer would compare the reliability-weighted variance of the sensory measurements with a set size-dependent criterion. We conducted two experiments, in which we varied set size and individual stimulus reliabilities. We found that the optimal-observer model accurately describes human behavior, outperforms plausible alternatives in a rigorous model comparison, and accounts for three key findings in the animal cognition literature. Our results provide a normative footing for the study of sameness judgment and indicate that the notion of perception as near-optimal inference extends to abstract relations.

Self-stabilization and behavioral diversity of embodied adaptive locomotion

Locomotion is of fundamental importance in understanding adaptive behavior. In this paper we present two case studies of robot locomotion that demonstrate how higher level of behavioral diversity can be achieved while observing the principle of cheap design. More precisely, it is shown that, by exploiting the dynamics of the system-environment interaction, very simple controllers can be designed which is essential to achieve rapid locomotion. Special consideration must be given to the choice of body materials. We conclude with some speculation about the importance of locomotion for understanding cognition. © Springer-Verlag Berlin Heidelberg 2004.

Study on Closed-set Speaker Identification Based on Biomimetic Pattern Recognition

In this paper, a new classifier of speaker identification has been proposed, which is based on Biomimetic pattern recognition (BPR). Distinguished from traditional speaker recognition methods, such as DWT, HMM, GMM, SVM and so on, the proposed classifier is constructed by some finite sub-space which is reasonable covering of the points in high dimensional space according to distributing characteristic of speech feature points. It has been used in the system of speaker identification. Experiment results show that better effect could be obtained especially with lesser samples. Furthermore, the proposed classifier employs a much simpler modeling structure as compared to the GMM. In addition, the basic idea "cognition" of Biomimetic pattern recognition (BPR) results in no requirement of retraining the old system for enrolling new speakers.

Biomimetic pattern recognition theory and its applications

Biomimetic pattern recogntion (BPR), which is based on "cognition" instead of "classification", is much closer to the function of human being. The basis of BPR is the Principle of homology-continuity (PHC), which means the difference between two samples of the same class must be gradually changed. The aim of BPR is to find an optimal covering in the feature space, which emphasizes the "similarity" among homologous group members, rather than "division" in traditional pattern recognition. Some applications of BPR are surveyed, in which the results of BPR are much better than the results of Support Vector Machine. A novel neuron model, Hyper sausage neuron (HSN), is shown as a kind of covering units in BPR. The mathematical description of HSN is given and the 2-dimensional discriminant boundary of HSN is shown. In two special cases, in which samples are distributed in a line segment and a circle, both the HSN networks and RBF networks are used for covering. The results show that HSN networks act better than RBF networks in generalization, especially for small sample set, which are consonant with the results of the applications of BPR. And a brief explanation of the HSN networks' advantages in covering general distributed samples is also given.

Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

Differences and commonalities between connectionism and symbolicism

The differences between connectionism and symbolicism in artificial intelligence (AI) are illustrated on several aspects in details firstly; then after conceptually decision factors of connectionism are proposed, the commonalities between connectionism and symbolicism are tested to make sure, by some quite typical logic mathematics operation examples such as "parity"; At last, neuron structures are expanded by modifying neuron weights and thresholds in artificial neural networks through adopting high dimensional space geometry cognition, which give more overall development space, and embodied further both commonalities.

Application of multi-weighted neuron for iris recognition

In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective.

Study on text-dependent speaker recognition based on Biomimetic Pattern Recognition

We studied the application of Biomimetic Pattern Recognition to speaker recognition. A speaker recognition neural network using network matching degree as criterion is proposed. It has been used in the system of text-dependent speaker recognition. Experimental results show that good effect could be obtained even with lesser samples. Furthermore, the misrecognition caused by untrained speakers occurring in testing could be controlled effectively. In addition, the basic idea "cognition" of Biomimetic Pattern Recognition results in no requirement of retraining the old system for enrolling new speakers.

A new development on ANN in China - Biomimetic pattern recognition and multi weight vector neurons

A new model of pattern recognition principles-Biomimetic Pattern Recognition, which is based on "matter cognition" instead of "matter classification", has been proposed. As a important means realizing Biomimetic Pattern Recognition, the mathematical model and analyzing method of ANN get breakthrough: a novel all-purpose mathematical model has been advanced, which can simulate all kinds of neuron architecture, including RBF and BP models. As the same time this model has been realized using hardware; the high-dimension space geometry method, a new means to analyzing ANN, has been researched.