Browsing by Subject "learning"

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Open Access
Behavioural analyses of quinine processing in choice, feeding and learning of larval drosophila
(2012) El-Keredy, A.; Schleyer, M.; König, C.; Ekim, A.; Gerber, B.
Gustatory stimuli can support both immediate reflexive behaviour, such as choice and feeding, and can drive internal reinforcement in associative learning. For larval Drosophila, we here provide a first systematic behavioural analysis of these functions with respect to quinine as a study case of a substance which humans report as "tasting bitter". We describe the dose-effect functions for these different kinds of behaviour and find that a half-maximal effect of quinine to suppress feeding needs substantially higher quinine concentrations (2.0 mM) than is the case for internal reinforcement (0.6 mM). Interestingly, in previous studies (Niewalda et al. 2008, Schipanski et al 2008) we had found the reverse for sodium chloride and fructose/sucrose, such that dose-effect functions for those tastants were shifted towards lower concentrations for feeding as compared to reinforcement, arguing that the differences in dose-effect function between these behaviours do not reflect artefacts of the types of assay used. The current results regarding quinine thus provide a starting point to investigate how the gustatory system is organized on the cellular and/or molecular level to result in different behavioural tuning curves towards a bitter tastant. © 2012 El-Keredy et al.
Open Access
A comparison of different approaches to target differentiation with sonar
(2001) Ayrulu (Erdem), Birsel
This study compares the performances of di erent classication schemes and fusion techniques for target di erentiation and localization of commonly encountered features in indoor robot environments using sonar sensing Di erentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identication map building navigation obstacle avoidance and target tracking The classication schemes employed include the target di erentiation algorithm developed by Ayrulu and Barshan statistical pattern recognition techniques fuzzy c means clustering algorithm and articial neural networks The fusion techniques used are Dempster Shafer evidential reasoning and di erent voting schemes To solve the consistency problem arising in simple ma jority voting di erent voting schemes including preference ordering and reliability measures are proposed and veried experimentally To improve the performance of neural network classiers di erent input signal representations two di erent training algorithms and both modular and non modular network structures are considered The best classication and localization scheme is found to be the neural network classier trained with the wavelet transform of the sonar signals This method is applied to map building in mobile robot environments Physically di erent sensors such as infrared sensors and structured light systems besides sonar sensors are also considered to improve the performance in target classication and localization.
Open Access
Designing a decision support system for debt payment planning under inflation
(1994) Özkan, Mehmet
Computer technology has been developed very rapidly in recent years and now computers are even replacing human in some areas. Use of judgment, however, is still very important in many other fields. Financial management is one of the areas that need managerial judgment and intuition while making decisions. In this study, we propose a methodology for designing a system to assist the decision maker (DM) in using his judgment to make effective decisions. The system also has to facilitate and enhance learning since judgment is excelled by experience. We specificly analyze decisions regarding the Debt Payment Planning (DPP) problem. This problem, which may be briefly stated as ‘development of an operational plan for the liquidation of debts’, is a new problem and does not exist in the literature. The analyses are conducted keeping in mind that the uncertainty of the financial environment and burden of inflation increase the complexity of the decisions. A model which we call, ‘Growth Model of Debt’ will be used in the analyses and a sample session will be shown to provide a clear understanding of the system operation.
Open Access
Odour intensity learning in fruit flies
(2009) Yarali, A.; Ehser, S.; Hapil F.Z.; Huang J.; Gerber, B.
Animals' behaviour towards odours depends on both odour quality and odour intensity. While neuronal coding of odour quality is fairly well studied, how odour intensity is treated by olfactory systems is less clear. Here we study odour intensity processing at the behavioural level, using the fruit fly Drosophila melanogaster. We trained flies by pairing a MEDIUM intensity of an odour with electric shock, and then, at a following test phase, measured flies' conditioned avoidance of either this previously trained MEDIUM intensity or a LOWer or a HIGHer intensity. With respect to 3-octanol, n-amylacetate and 4-methylcyclohexanol, we found that conditioned avoidance is strongest when training and test intensities match, speaking for intensity-specific memories. With respect to a fourth odour, benzaldehyde, on the other hand, we found no such intensity specificity. These results form the basis for further studies of odour intensity processing at the behavioural, neuronal and molecular level. © 2009 The Royal Society.