Browsing by Subject "human learning"

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Electrical Stimulation of Lateral Habenula during Learning: Frequency-Dependent Effects on Acquisition but Not Retrieval of a Two-Way Active Avoidance Response
(PLOS One, 2013-06-28) Ilango, Anton; Shumake, Jason; Wetsel, Wolfram; Scheich, Henning; Ohl, Frank W.
The lateral habenula (LHb) is an epithalamic structure involved in signaling reward omission and aversive stimuli, and it inhibits dopaminergic neurons during motivated behavior. Less is known about LHb involvement in the acquisition and retrieval of avoidance learning. Our previous studies indicated that brief electrical stimulation of the LHb, time-locked to the avoidance of aversive footshock (presumably during the positive affective “relief” state that occurs when an aversive outcome is averted), inhibited the acquisition of avoidance learning. In the present study, we used the same paradigm to investigate different frequencies of LHb stimulation. The effect of 20 Hz vs. 50 Hz vs. 100 Hz stimulation was investigated during two phases, either during acquisition or retrieval in Mongolian gerbils. The results indicated that 50 Hz, but not 20 Hz, was sufficient to produce a long-term impairment in avoidance learning, and was somewhat more effective than 100 Hz in this regard. None of the stimulation parameters led to any effects on retrieval of avoidance learning, nor did they affect general motor activity. This suggests that, at frequencies in excess of the observed tonic firing rates of LHb neurons (>1–20 Hz), LHb stimulation may serve to interrupt the consolidation of new avoidance memories. However, these stimulation parameters are not capable of modifying avoidance memories that have already undergone extensive consolidation.
Prediction and Validation of Gene-Disease Associations Using Methods Inspired by Social Network Analyses
(PLOS One, 2013-05-01) Singh-Blom, U. Martin; Natarajan, Nagarajan; Tewari, Ambuj; Woods, John O.; Dhillon, Inderjit S.; Marcotte, Edward M.
Correctly identifying associations of genes with diseases has long been a goal in biology. With the emergence of large-scale gene-phenotype association datasets in biology, we can leverage statistical and machine learning methods to help us achieve this goal. In this paper, we present two methods for predicting gene-disease associations based on functional gene associations and gene-phenotype associations in model organisms. The first method, the Katz measure, is motivated from its success in social network link prediction, and is very closely related to some of the recent methods proposed for gene-disease association inference. The second method, called Catapult (Combining dATa Across species using Positive-Unlabeled Learning Techniques), is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. We study the performance of the proposed methods and related state-of-the-art methods using two different evaluation strategies, on two distinct data sets, namely OMIM phenotypes and drug-target interactions. Finally, by measuring the performance of the methods using two different evaluation strategies, we show that even though both methods perform very well, the Katz measure is better at identifying associations between traits and poorly studied genes, whereas Catapult is better suited to correctly identifying gene-trait associations overall. The authors want to thank Jon Laurent and Kris McGary for some of the data used, and Li and Patra for making their code available. Most of Ambuj Tewari's contribution to this work happened while he was a postdoctoral fellow at the University of Texas at Austin.