Raju S. Bapi

Evidence for effector independent and dependent representations and their differential time course of acquisition during motor sequence learning

Abstract. To investigate the representation of motor sequence, we tested transfer effects in a motor sequence learning paradigm. We hypothesize that there are two sequence representations, effector independent and dependent. Further, we postulate that the effector independent representation is in visual/spatial coordinates, that the effector dependent representation is in motor coordinates, and that their time courses of acquisition during learning are different. Twelve subjects were tested in a modified 2×10 task. Subjects learned to press two keys (called a set) successively on a keypad in response to two lighted squares on a 3×3 display. The complete sequence to be learned was composed of ten such sets, called a hyperset. Training was given in the normal condition and sequence recall was assessed in the early, intermediate, and late stages in three conditions, normal, visual, and motor. In the visual condition, finger–keypad mapping was rotated 90° while the keypad–display mapping was kept identical to normal. In the motor condition, the keypad–display mapping was also rotated 90°, resulting in an identical finger–display mapping as in normal. Subjects formed two groups with each group using a different normal condition. One group learned the sequence in a standard keypad–hand setting and subsequently recalled the sequence using a rotated keypad–hand setting in the test conditions. The second group learned the sequence with a rotated keypad–hand setting and subsequently recalled the sequence with a standard keypad–hand setting in the test conditions. Response time (RT) and sequencing errors during recall were recorded. Although subjects committed more sequencing errors in both testing conditions, visual and motor, as compared to the normal condition, the errors were below chance level. Sequencing errors did not differ significantly between visual and motor conditions. Further, the sequence recall accuracy was over 70% even by the early stage when the subjects performed the sequence for the first time with the altered conditions, visual and motor. There were parallel improvements thereafter in all the conditions. These results of positive transfer of sequence knowledge across conditions that use dissimilar finger movements point to an effector independent sequence representation, possibly in visual/spatial coordinates. Initially the RTs were similar in the visual and the motor conditions, but with training RTs in the motor condition became significantly shorter than in the visual condition, as revealed by significant interaction for the testing stage and condition term in the repeated measures ANOVA. Moreover, using RTs for single key pressing in the three conditions as baseline indices, it was again observed that RTs in the visual and motor conditions were not significantly different in the early stage, but motor RTs became significantly shorter by the late testing stage. These results support the hypothesis that the motor condition benefits more than the visual because it uses identical effector movements to the normal condition. Further, these results argue for the existence of effector dependent sequence representation, in motor coordinates, which is acquired relatively slowly. The difference in the time course of learning of these two representations may account for the differential involvement of brain areas in early and late learning phases found in lesion and imaging studies.

What do the basal ganglia do? A modeling perspective

Basal ganglia (BG) constitute a network of seven deep brain nuclei involved in a variety of crucial brain functions including: action selection, action gating, reward based learning, motor preparation, timing, etc. In spite of the immense amount of data available today, researchers continue to wonder how a single deep brain circuit performs such a bewildering range of functions. Computational models of BG have focused on individual functions and fail to give an integrative picture of BG function. A major breakthrough in our understanding of BG function is perhaps the insight that activities of mesencephalic dopaminergic cells represent some form of ‘reward’ to the organism. This insight enabled application of tools from ‘reinforcement learning,’ a branch of machine learning, in the study of BG function. Nevertheless, in spite of these bright spots, we are far from the goal of arriving at a comprehensive understanding of these ‘mysterious nuclei.’ A comprehensive knowledge of BG function has the potential to radically alter treatment and management of a variety of BG-related neurological disorders (Parkinson’s disease, Huntington’s chorea, etc.) and neuropsychiatric disorders (schizophrenia, obsessive compulsive disorder, etc.) also. In this article, we review the existing modeling literature on BG and hypothesize an integrative picture of the function of these nuclei.

fMRI investigation of cortical and subcortical networks in the learning of abstract and effector-specific representations of motor sequences

A visuo-motor sequence can be learned as a series of visuo-spatial cues or as a sequence of effector movements. Earlier imaging studies have revealed that a network of brain areas is activated in the course of motor sequence learning. However, these studies do not address the question of the type of representation being established at various stages of visuo-motor sequence learning. In an earlier behavioral study, we demonstrated that acquisition of visuo-spatial sequence representation enables rapid learning in the early stage and progressive establishment of somato-motor representation helps speedier execution by the late stage. We conducted functional magnetic resonance imaging (fMRI) experiments wherein subjects learned and practiced the same sequence alternately in normal and rotated settings. In one rotated setting (visual), subjects learned a new motor sequence in response to an identical sequence of visual cues as in normal. In another rotated setting (motor), the display sequence was altered as compared to normal, but the same sequence of effector movements was used to perform the sequence. Comparison of different rotated settings revealed analogous transitions both in the cortical and subcortical sites during visuo-motor sequence learning-a transition of activity from parietal to parietal-premotor and then to premotor cortex and a concomitant shift was observed from anterior putamen to a combined activity in both anterior and posterior putamen and finally to posterior putamen. These results suggest a putative role for engagement of different cortical and subcortical networks at various stages of learning in supporting distinct sequence representations.

Rough clustering of sequential data

This paper presents a new indiscernibility-based rough agglomerative hierarchical clustering algorithm for sequential data. In this approach, the indiscernibility relation has been extended to a tolerance relation with the transitivity property being relaxed. Initial clusters are formed using a similarity upper approximation. Subsequent clusters are formed using the concept of constrained-similarity upper approximation wherein a condition of relative similarity is used as a merging criterion. We report results of experimentation on msnbc web navigation dataset that are intrinsically sequential in nature. We have compared the results of the proposed approach with that of the traditional hierarchical clustering algorithm using vector coding of sequences. The results establish the viability of the proposed approach. The rough clusters resulting from the proposed algorithm provide interpretations of different navigation orientations of users present in the sessions without having to fit each object into only one group. Such descriptions can help web miners to identify potential and meaningful groups of users.

Changing the structure of complex visuo-motor sequences selectively activates the fronto-parietal network.

Previous brain imaging studies investigating motor sequence complexity have mainly examined the effect of increasing the length of pre-learned sequences. The novel contribution of this research is that we varied the structure of complex visuo-motor sequences along two different dimensions using mxn paradigm. The complexity of sequences is increased from 12 movements (organized as a 2×6 task) to 24 movements (organized as 4×6 and 2×12 tasks). Behavioral results indicate that although the success rate attained was similar across the two complex tasks (2×12 and 4×6), a greater decrease in response times was observed for the 2×12 compared to the 4×6 condition at an intermediate learning stage. This decrease is possibly related to successful chunking across sets in the 2×12 task. In line with this, we observed a selective activation of the fronto-parietal network. Shifts of activation were observed from the ventral to dorsal prefrontal, lateral to medial premotor and inferior to superior parietal cortex from the early to intermediate learning stage concomitant with an increase in hyperset length. We suggest that these selective activations and shifts in activity during complex sequence learning are possibly related to chunking of motor sequences.

Short Communication: Protein ligand interaction database (PLID)

A comprehensive database named, protein ligand interaction database (PLID), is created with 6295 ligands bound to proteins extracted from the protein data bank (PDB). This is by far the most comprehensive database of physico-chemical properties, quantum mechanical descriptors and the residues present in the active site of proteins. It is a publicly available web-based database (via the Internet) at http://203.199.182.73/gnsmmg/databases/plid/.

Analysis of E.coli promoter recognition problem in dinucleotide feature space

MOTIVATION Patterns in the promoter sequences within a species are known to be conserved but there exist many exceptions to this rule which makes the promoter recognition a complex problem. Although many complex feature extraction schemes coupled with several classifiers have been proposed for promoter recognition in the current literature, the problem is still open. RESULTS A dinucleotide global feature extraction method is proposed for the recognition of sigma-70 promoters in Escherichia coli in this article. The positive data set consists of sigma-70 promoters with known transcription starting points which are part of regulonDB and promec databases. Four different kinds of negative data sets are considered, two of them biological sets (Gordon et al., 2003) and the other two synthetic data sets. Our results reveal that a single-layer perceptron using dinucleotide features is able to achieve an accuracy of 80% against a background of biological non-promoters and 96% for random data sets. A scheme for locating the promoter regions in a given genome sequence is proposed. A deeper analysis of the data set shows that there is a bifurcation of the data set into two distinct classes, a majority class and a minority class. Our results point out that majority class constituting the majority promoter and the majority non-promoter signal is linearly separable. Also the minority class is linearly separable. We further show that the feature extraction and classification methods proposed in the paper are generic enough to be applied to the more complex problem of eucaryotic promoter recognition. We present Drosophila promoter recognition as a case study. AVAILABILITY http://202.41.85.117/htmfiles/faculty/tsr/tsr.html.

Rule extraction using Support Vector Machine based hybrid classifier

Support Vector Machines (SVMs) have become an increasingly popular tool for machine learning tasks involving classification and regression, and have shown superior performance compared to other machine learning techniques. In this paper we propose a hybrid classification technique to extract fuzzy rules from the support vector machine and evaluate the rules against decision tree classifier constructed from the same support vector machine. The hybrid approach proceeds in three major steps. In the first step we use training patterns with class labels to build an SVM model that gives the support vectors with acceptable accuracy. Fuzzy rules are generated using the extracted support vectors during second step. In the final step the resulting rule set is tested using the test data of the problem. The quality of the extracted rules is then evaluated in terms of accuracy and fidelity. It is found that the proposed hybrid approach using fuzzy rules yielded highest accuracy and fidelity compared to hybrid with decision tree classifier.

SeqPAM: A Sequence Clustering Algorithm for Web Personalization

With the growth in the number of Web users and necessity for making information available on the Web, the problem of Web personalization has become very critical and popular. Developers are trying to customize a Web site to the needs of specific users with the help of knowledge acquired from user navigational behavior. Since user page visits are intrinsically sequential in nature, efficient clustering algorithms for sequential data are needed. In this chapter, we introduce a similarity preserving function called sequence and set similarity measure S3M that captures both the order of occurrence of page visits as well as the content of pages. We conducted pilot experiments comparing the results of PAM, a standard clustering algorithm, with two similarity measures: Cosine and S3M. The goodness of the clusters resulting from both the measures was computed using a cluster validation technique based on average levensthein distance. Results on pilot dataset established the effectiveness of S3M for sequential data. Based on these results, we proposed a new clustering algorithm, SeqPAM for clustering sequential data. We tested the new algorithm on two datasets namely, cti and msnbc datasets. We provided recommendations for Web personalization based on the clusters obtained from SeqPAM for msnbc dataset.

Analysis of n-gram based promoter recognition methods and application to whole genome promoter prediction.

Promoter prediction is an important and complex problem. Pattern recognition algorithms typically require features that could capture this complexity. A special bias towards certain combinations of base pairs in the promoter sequences may be possible. In order to determine these biases n-grams are usually extracted and analyzed. An n-gram is a selection of n contiguous characters from a given character stream, DNA sequence segments in this case. Here a systematic study is made to discover the efficacy of n-grams for n = 2, 3, 4, 5 in promoter prediction. A study of n-grams as features for a neural network classifier for E. coli and Drosophila promoters is made. In case of E. coli n=3 and in case of Drosophila n=4 seem to give optimal prediction values. Using the 3-gram features, promoter prediction in the genome sequence of E. coli is done. The results are encouraging in positive identification of promoters in the genome compared to software packages such as BPROM, NNPP, and SAK. Whole genome promoter prediction in Drosophila genome was also performed but with 4-gram features.