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Innate attractiveness and associative learnability of odors can be dissociated in larval Drosophila.

ABSTRACT: We investigate olfactory associative learning in larval Drosophila. A reciprocal training design is used, such that one group of animals receives a reward in the presence of odor X but not in the presence of odor Y (Train: X+ // Y), whereas another group is trained reciprocally (Train: X // Y+). After training, differences in odor preference between these reciprocally trained groups in a choice test (Test: X - Y) reflect associative learning. The current study, after showing which odor pairs can be used for such learning experiments, 1) introduces a one-odor version of such reciprocal paradigm that allows estimating the learnability of single odors. Regarding this reciprocal one-odor paradigm, we show that 2) paired presentations of an odor with a reward increase odor preference above baseline, whereas unpaired presentations of odor and reward decrease odor preference below baseline; this suggests that odors can become predictive either of reward or of reward absence. Furthermore, we show that 3) innate attractiveness and associative learnability can be dissociated. These data deepen our understanding of odor-reward learning in larval Drosophila on the behavioral level, and thus foster its neurogenetic analysis.

SUBMITTER: Saumweber T

PROVIDER: S-EPMC3038274 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Innate attractiveness and associative learnability of odors can be dissociated in larval Drosophila.

Saumweber Timo T Husse Jana J Gerber Bertram B

Chemical senses 20110111 3

We investigate olfactory associative learning in larval Drosophila. A reciprocal training design is used, such that one group of animals receives a reward in the presence of odor X but not in the presence of odor Y (Train: X+ // Y), whereas another group is trained reciprocally (Train: X // Y+). After training, differences in odor preference between these reciprocally trained groups in a choice test (Test: X - Y) reflect associative learning. The current study, after showing which odor pairs can ...[more]

PMID: 21227902

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Project description:Human learners acquire complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on two factors: the architecture (or informational structure) of the knowledge network itself and the architecture of the computational unit-the brain-that encodes and processes the information. That is, learning is reliant on integrated network architectures at two levels: the epistemic and the computational, or the conceptual and the neural. Motivated by a wish to understand conventional human knowledge, here, we discuss emerging work assessing network constraints on the learnability of relational knowledge, and theories from statistical physics that instantiate the principles of thermodynamics and information theory to offer an explanatory model for such constraints. We then highlight similarities between those constraints on the learnability of relational networks, at one level, and the physical constraints on the development of interconnected patterns in neural systems, at another level, both leading to hierarchically modular networks. To support our discussion of these similarities, we employ an operational distinction between the modeller (e.g. the human brain), the model (e.g. a single human's knowledge) and the modelled (e.g. the information present in our experiences). We then turn to a philosophical discussion of whether and how we can extend our observations to a claim regarding explanation and mechanism for knowledge acquisition. What relation between hierarchical networks, at the conceptual and neural levels, best facilitate learning? Are the architectures of optimally learnable networks a topological reflection of the architectures of comparably developed neural networks? Finally, we contribute to a unified approach to hierarchies and levels in biological networks by proposing several epistemological norms for analysing the computational brain and social epistemes, and for developing pedagogical principles conducive to curious thought. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Dataset Information

Innate attractiveness and associative learnability of odors can be dissociated in larval Drosophila.

Publications

Innate attractiveness and associative learnability of odors can be dissociated in larval Drosophila.

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