State-dependent memory produced by aerobic exercise

In a free recall experiment, participants learned lists of words in two physiological states: at rest and while exercising aerobically on a bicycle ergometer. Recall of the words was required in either the state consistent with learning or in the alternative state. Word lists learned during aerobic exercise were recalled best during aerobic exercise and vice versa. Greater changes in heart rate in the changed state conditions were associated with greater retrieval decrements. Recall levels for words both learned and recalled at exercise were equivalent to those for words both learned and recalled at rest. This finding rules out the possibility that exercise per se interfered with the original learning. The study is consistent with the view that state-dependent memory should be viewed as a particular form of cue-dependent memory.     

A parallel strategy for biological sequence alignment in restricted memory space

Recently, many organisms had their DNA entirely sequenced, and this reality presents the need for aligning long DNA sequences, which is a challenging task due to its high demands for computational power and memory. The algorithm proposed by Smith–Waterman (SW) is an exact method that obtains optimal local alignments in quadratic space and time. For long sequences, quadratic complexity makes the use of this algorithm impractical. In this scenario, parallel computing is a very attractive alternative. In this paper, we propose and evaluate z-align, a parallel exact strategy based on the divergence concept to locally align long biological sequences using an affine gap function. Z-align runs in limited memory space, where the amount of memory used can be defined by the user. The results collected in a cluster with 16 processors presented very good speedups for long real DNA sequences. With z-align, we were able to compare up to 3 MBP (mega base-pairs) DNA sequences. As far as we know, this is the first time 3 MBP sequences are compared with an affine gap exact variation of the SW algorithm. Also, by comparing the results obtained with z-align and the popular BLAST tool, it is clear that z-align is able to produce longer and more significant alignments.     

Constructing large suffix trees on a computational grid

The suffix tree is a key data structure for biological sequence analysis, since it permits efficient solutions to many string-based problems. Constructing large suffix trees is challenging because of high memory overheads and poor memory locality. Even though efficient suffix tree construction algorithms exist, their run-time is still very high for long DNA sequences such as whole human chromosomes. In this paper, we are using a hierarchical grid system as a computational platform in order to reduce this run-time significantly. To achieve an efficient mapping onto this type of architecture we introduce a parallel suffix tree construction algorithm that makes use of a new data structure called the common prefix suffix tree. Using this algorithm together with a dynamic load balancing strategy we show that our distributed grid implementation leads to significant run-time savings.     

Visual control of autonomous mobile robot based on self-organizing model for pattern learning

This article proposes a self-organizing model for pattern learning together with an application to an autonomous mobile robot system. The self-organizing model consists of a processing rule prescribed and a memory part being blank at the initial stage. To an input signal, the model searches for a similar signal in the memory, and recalls its related information. If the information accompanied with the input signal differs from the recalled information, the model adds the new information to the memory. It influences the subsequent operations. Thus, the model constructs successively a data-base in a self-organizing way. This model can universally learn and reproduce any pattern of input-output response desired. Two principal functions in autonomous movement, i.e., position identification and obstacle avoiding movement were realized based on the self-organizing model. Furthermore, a camera type autonomous mobile robot system for indoor was made up. The size of the robot is about 0.7 × 0.7 × 0.7 m, and the weight is about 30 kg. The speed of movement is less than 3 km/h. A small computer that has a 16 bit microprocessor and a 1 Mbyte RAM controls the motion of the robot with an extended C language.     

The interaction of map resolution and spatial abilities on map learning

This study investigated how the addition of enhanced perceptual detail in a navigation interface interacts with learner characteristics and ultimately impacts learning; specifically memory for a route on a map. Previous research has shown both facilitative and prohibitive effect of adding perceptual detail to user interfaces. However, it is not clear how adding this kind of resolution might also interact with learner abilities. This study evaluated how well routes were remembered from maps that were either enhanced with actual satellite photography or presented in more traditional (low resolution) form by learners who differed in spatial ability. Results indicated that learners recalled a mapped route significantly better in the low perceptual detail condition than in the high detail condition and spatial visualization ability significantly predicted success on these tasks whereas mental rotation ability did not. Thus, it appears that the addition of perceptual detail not only affects learning, but also interacts with learner ability.     

MPI framework for parallel searching in large biological databases

In this paper, we address the problem of searching huge biological databases on the scale of at least several gigabytes by utilizing parallel processing. Biological databases storing DNA sequences, protein sequences, or mass spectra are growing exponentially. Searches through these databases consume exponentially growing computational resources as well. We demonstrate herein a general use, MPI based, C++ framework for generically splitting databases amongst several computational nodes. The combined RAM of the nodes working in tandem is often sufficient to keep the entire database in memory, and therefore to search it efficiently without paging to disk. The framework runs as a persistent service, processing all submitted queries. This allows for query reordering and better utilization of the memory. Thereby, we achieve superlinear speedups compared to single processor implementations. We demonstrate the utility and speedup of the framework using a real biological database and an actual searching algorithm for mass spectrometry.     

Constructing suffix tree for gigabyte sequences with megabyte memory

Mammalian genomes are typically 3 Gbps (gibabase pairs) in size. The largest public database NCBI (National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov)) of DNA contains more than 20 Gbps. Suffix trees are widely acknowledged as a data structure to support exact/approximate sequence matching queries as well as repetitive structure finding efficiently when they can reside in main memory. But, it has been shown as difficult to handle long DNA sequences using suffix trees due to the so-called memory bottleneck problems. The most space efficient main-memory suffix tree construction algorithm takes nine hours and 45 GB memory space to index the human genome [S. Kurtz (1999)]. We show that suffix trees for long DNA sequences can be efficiently constructed on disk using small bounded main memory space and, therefore, all existing algorithms based on suffix trees can be used to handle long DNA sequences that cannot be held in main memory. We adopt a two-phase strategy to construct a suffix tree on disk: 1) to construct a diskbase suffix-tree without suffix links and 2) rebuild suffix links upon the suffix-tree being constructed on disk, if needed. We propose a new disk-based suffix tree construction algorithm, called DynaCluster, which shows O(nlogn) experimental behavior regarding CPU cost and linearity for I/O cost. DynaCluster needs 16 MB main memory only to construct more than 200 Mbps DNA sequences and significantly outperforms the existing disk-based suffix-tree construction algorithms using prepartitioning techniques in terms of both construction cost and query processing cost. We conducted extensive performance studies and report our findings in this paper.     

Computational Technique for an Efficient Classification of Protein Sequences With Distance‐Based Sequence Encoding Algorithm

Machine learning is being implemented in bioinformatics and computational biology to solve challenging problems emerged in the analysis and modeling of biological data such as DNA, RNA, and protein. The major problems in classifying protein sequences into existing families/superfamilies are the following: the selection of a suitable sequence encoding method, the extraction of an optimized subset of features that possesses significant discriminatory information, and the adaptation of an appropriate learning algorithm that classifies protein sequences with higher classification accuracy. The accurate classification of protein sequence would be helpful in determining the structure and function of novel protein sequences. In this article, we have proposed a distance‐based sequence encoding algorithm that captures the sequence's statistical characteristics along with amino acids sequence order information. A statistical metric‐based feature selection algorithm is then adopted to identify the reduced set of features to represent the original feature space. The performance of the proposed technique is validated using some of the best performing classifiers implemented previously for protein sequence classification. An average classification accuracy of 92% was achieved on the yeast protein sequence data set downloaded from the benchmark UniProtKB database.     

The effects of alcohol on learning as a function of drinking habits

Thirty-six subjects were categorized as light social drinkers (LSD) and 36 as heavy social drinkers (HSD) on the basis of both biological criteria, gamma-glutamyl transferase (GGT) and the mean corpuscular volume (MCV) and by self-reports of drinking habits. This study examined the effects of alcohol (0.7 ml/kg) and of noise (75 dB(A)) on learning and performance, in choice RT and in immediate memory tasks. Each subject was tested in two consecutive sessions (the time factor) under one of the following experimental conditions: (a) silence+placebo; (b) silence-I-alcohol; (c) noise+placebo; and (d) noise+alcohol. On immediate memory, there was a strong effect of time which interacted with 'drinker’ category, suggesting that HSD subjects learn less rapidly than LSD. Time interacted also with state (sober or intoxicated), showing that learning might be impaired even by a moderate dose of alcohol. On the reaction time task no main effects were found but the time × drinker category interaction was significant; in session 1 LSD's RT were shorter than those of HSD. It is suggested that regular heavy drinking might affect the learning abilities of those who are not yet labelled as alcoholics.     

After-effects of the common cold on mood and performance

The present study examined whether volunteers who had recently had common colds showed impairments in mood and performance in the weeks following the illness. All volunteers (n = 24) were tested when healthy to provide baseline data for simple and choice reaction time tasks, attention and memory tasks and ratings of mood. When participants developed a cold (n = 13) they returned to the laboratory so that the illness could be verified. When they were symptom free they returned to the laboratory and repeated the procedure. They then completed the study with a final session 1 week later. Volunteers (n = 11) who remained healthy over 10 weeks were recalled as controls and also repeated the procedures. The results showed that those who had recently had colds showed few impairments in mental performance and mood. Taken together with the results of previous studies, this suggests that after-effects of viral infection are largely restricted to severe illnesses such as infectious mononucleosis and influenza. After-effects of colds may occur but these probably reflect poor learning at the time of the illness.     

Grammatical inference in bioinformatics

Bioinformatics is an active research area aimed at developing intelligent systems for analyses of molecular biology. Many methods based on formal language theory, statistical theory, and learning theory have been developed for modeling and analyzing biological sequences such as DNA, RNA, and proteins. Especially, grammatical inference methods are expected to find some grammatical structures hidden in biological sequences. In this article, we give an overview of a series of our grammatical approaches to biological sequence analyses and related researches and focus on learning stochastic grammars from biological sequences and predicting their functions based on learned stochastic grammars.     

A hierarchy of associations in hippocampo-cortical systems: cognitive maps and navigation strategies

In this letter we describe a hippocampo-cortical model of spatial processing and navigation based on a cascade of increasingly complex associative processes that are also relevant for other hippocampal functions such as episodic memory. Associative learning of different types and the related pattern encoding-recognition take place at three successive levels: (1) an object location level, which computes the landmarks from merged multimodal sensory inputs in the parahippocampal cortices; (2) a subject location level, which computes place fields by combination of local views and movement-related information in the entorhinal cortex; and (3) a spatiotemporal level, which computes place transitions from contiguous place fields in the CA3-CA1 region, which form building blocks for learning temporospatial sequences.At the cell population level, superficial entorhinal place cells encode spatial, context-independent maps as landscapes of activity; populations of transition cells in the CA3-CA1 region encode context-dependent maps as sequences of transitions, which form graphs in prefrontal-parietal cortices. The model was tested on a robot moving in a real environment; these tests produced results that could help to interpret biological data. Two different goal-oriented navigation strategies were displayed depending on the type of map used by the system.Thanks to its multilevel, multimodal integration and behavioral implementation, the model suggests functional interpretations for largely unaccounted structural differences between hippocampo-cortical systems. Further, spatiotemporal information, a common denominator shared by several brain structures, could serve as a cognitive processing frame and a functional link, for example, during spatial navigation and episodic memory, as suggested by the applications of the model to other domains, temporal sequence learning and imitation in particular.     

A competitive associative memory model and its dynamics

Conventional associative memory networks perform "noncompetitive recognition" or "competitive recognition in distance". In this paper a "competitive recognition" associative memory model is introduced which simulates the competitive persistence of biological species. Unlike most of the conventional networks, the proposed model takes only the prototype patterns as its equilibrium points, so that the spurious points are effectively excluded. Furthermore, it is shown that, as the competitive parameters vary, the network has a unique stable equilibrium point corresponding to the winner competitive parameter and, in this case, the unique stable equilibrium state can be recalled from any initial key.     

Problem solving through imitation

This paper presents an approach to problem solving through imitation. It introduces the Statistical and Temporal Percept Action Coupling (ST-PAC) System which statistically models the dependency between the perceptual state of the world and the resulting actions that this state should elicit. The ST-PAC system stores a sparse set of experiences provided by a teacher. These memories are stored to allow efficient recall and generalisation over novel systems states. Random exploration is also used as a fall-back “brute-force” mechanism should a recalled experience fail to solve a scenario. Statistical models are used to couple groups of percepts with similar actions and incremental learning used to incorporate new experiences into the system. The system is demonstrated within the problem domain of a children’s shape sorter puzzle. The ST-PAC system provides an emergent architecture where competence is implicitly encoded within the system. In order to train and evaluate such emergent architectures, the concept of the Complexity Chain is proposed. The Complexity Chain allows efficient structured learning in a similar fashion to that used in biological system and can also be used as a method for evaluating a cognitive system’s performance. Tests demonstrating the Complexity Chain in learning are shown in both simulated and live environments. Experimental results show that the proposed methods allowed for good generalisation and concept refinement from an initial set of sparse examples provided by a tutor.     

Protein-coding region discovery in organisms underrepresented in databases.

The prediction of coding sequences has received a lot of attention during the last decade. We can distinguish two kinds of methods, those that rely on training with sets of example and counter-example sequences, and those that exploit the intrinsic properties of the DNA sequences to be analyzed. The former are generally more powerful but their domains of application are limited by the availability of a training set. The latter avoid this drawback but can only be applied to sequences that are long enough to allow computation of the statistics. Here, we present a method that fills the gap between the two approaches. A learning step is applied using a set of sequences that are assumed to contain coding and non-coding regions, but with the boundaries of these regions unknown. A test step then uses the discriminant function obtained during the learning to predict coding regions in sequences from the same organism. The learning relies upon a correspondence analysis and prediction is presented on a graphical display. The method has been evaluated on a sample of yeast sequences, and the analysis of a set of expressed sequence tags from the Eucalyptus globulus-Pisolithus tinctorius ectomycorrhiza illustrates the relevance of the approach in its biological context.     

A reinforcement learning method based on an immune network adapted to a semi-Markov decision process

The immune system is attracting attention as a new biological information processing-type paradigm. It is a large-scale system equipped with a complicated biological defense function. It has functions of memory and learning that use interactions such as stimulus and suppression between immune cells. In this article, we propose and construct a reinforcement learning method based on an immune network adapted to a semi-Markov decision process (SMDP). We show that the proposed method is capable of dealing with a problem which is modeled as a SMDP environment through computer simulation. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Context-dependent memory under stressful conditions: the case of skydiving

Two experiments examined the effect of differing levels of emotional arousal on learning and memory for words in matching and mismatching contexts. In Experiment 1, experienced skydivers learned words either in the air or on the ground and recalled them in the same context or in the other context. Experiment 2 replicated the stimuli and design of the first experiment except that participants were shown a skydiving video in lieu of skydiving. Recall was poor in air-learning conditions with actual skydiving, but when lists were learned on land, recall was higher in the matching context than in the mismatching context. In the skydiving video experiment, recall was higher in matching learn-recall contexts regardless of the situation in which learning occurred. We propose that under extremely emotionally arousing circumstances, environmental and/or mood cues are unlikely to become encoded or linked to newly acquired information and thus cannot serve as cues to retrieval. Results can be applied to understanding variations in context-dependent memory in occupations (e.g., police, military special operations, and Special Weapons and Tactics teams) in which the worker experiences considerable emotional stress while learning or recalling new information.     

Design and integration of a spatio-temporal memory with emotional influences to categorize and recall the experiences of an autonomous mobile robot

Autonomous robots cohabiting with humans will have to achieve recurring tasks while adapting to the changing conditions of the world. A spatio-temporal memory categorizes the experiences of a robot to improve its ability to adapt to its environment. In this paper, we present a spatio-temporal (ST) memory model consisting of a cascade of two adaptive resonance theory (ART) networks: one to categorize spatial events and the other to extract temporal episodes from the robot’s experiences. Artificial emotions are used to dynamically modulate learning and recall of the ART networks based on how the robot is able to carry its task, using a simple model of artificial emotions. Once an episode is recalled, future events can be predicted and used to influence the intentions of the robot. Evaluation of our ST model is done using an autonomous robotic platform that has to deliver objects to people within an office area. Results demonstrate that our model can memorize and recall the experiences of a robot, and that emotional episodes are recalled more often, allowing the robot to use its memory of past experiences early on when repeating a task.