Sensory Representation and Learning-Related Plasticity in Mushroom Body Extrinsic Feedback Neurons of the Protocerebral Tract

Haehnel, Melanie; Menzel, Randolf

doi:10.3389/fnsys.2010.00161

Cited by 56 publications

(50 citation statements)

References 64 publications

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“…3). These results argue in favor of a crucial role for GABAergic feedback to the MB calyces, and thus for the circuitry provided by A3v neurons, which precisely provide such feedback (39)(40)(41)(42)(43)51). In Drosophila, GABAergic input to the MBs is provided by anterior paired lateral neurons, which seem to be presynaptic both to the calyces and lobes (65) so that they do not provide an inhibitory feedback signal to the MBs, contrary to A3v neurons of the bee.…”

Section: Discussionmentioning

confidence: 83%

“…We thus blocked GABAergic signaling in the MBs by locally injecting picrotoxin (PTX), an efficient GABA antagonist (49), into the calyces or into the lobes. Convergence between the olfactory and the reward pathways exists at the level of the calyces but not of the lobes (50); we thus hypothesized that feedback inhibition from the lobes to the calyces (39,(40)(41)(42)(43)51) might be crucial for nonelemental learning (48). Fig.…”

Section: Blocking Gabaergic Signaling In the Mushroom Body Calyces Immentioning

confidence: 99%

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Neural substrate for higher-order learning in an insect: Mushroom bodies are necessary for configural discriminations

Devaud

Papouin

Carcaud

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

120

116

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Learning theories distinguish elemental from configural learning based on their different complexity. Although the former relies on simple and unambiguous links between the learned events, the latter deals with ambiguous discriminations in which conjunctive representations of events are learned as being different from their elements. In mammals, configural learning is mediated by brain areas that are either dispensable or partially involved in elemental learning. We studied whether the insect brain follows the same principles and addressed this question in the honey bee, the only insect in which configural learning has been demonstrated. We used a combination of conditioning protocols, disruption of neural activity, and optophysiological recording of olfactory circuits in the bee brain to determine whether mushroom bodies (MBs), brain structures that are essential for memory storage and retrieval, are equally necessary for configural and elemental olfactory learning. We show that bees with anesthetized MBs distinguish odors and learn elemental olfactory discriminations but not configural ones, such as positive and negative patterning. Inhibition of GABAergic signaling in the MB calyces, but not in the lobes, impairs patterning discrimination, thus suggesting a requirement of GABAergic feedback neurons from the lobes to the calyces for nonelemental learning. These results uncover a previously unidentified role for MBs besides memory storage and retrieval: namely, their implication in the acquisition of ambiguous discrimination problems. Thus, in insects as in mammals, specific brain regions are recruited when the ambiguity of learning tasks increases, a fact that reveals similarities in the neural processes underlying the elucidation of ambiguous tasks across species.learning | configural learning | mushroom bodies | honey bee | Apis mellifera

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Section: Discussionmentioning

confidence: 83%

Section: Blocking Gabaergic Signaling In the Mushroom Body Calyces Immentioning

confidence: 99%

Neural substrate for higher-order learning in an insect: Mushroom bodies are necessary for configural discriminations

Devaud

Papouin

Carcaud

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

120

116

View full text Add to dashboard Cite

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“…The recording of Fifty years of PER conditioning in honeybees www.learnmem.org electromyograms of the muscles involved in proboscis extension, for instance, muscle M17 (Rehder 1987;Smith and Menzel 1989) or other muscles (Gauthier and Richard 1992), allowed a more graded measure of the animal's response. This technique can be efficiently used to measure a neural correlate of behavioral responses when movements of the proboscis should be prohibited, such as when coupled with other electrophysiological (Hammer 1993;Okada et al 2007;Denker et al 2010;Strube-Bloss et al 2011) or imaging recordings (Faber et al 1999;Hähnel and Menzel 2010). A few attempts have also been made using video recordings and subsequent analysis of PER parameters (Hosler and Smith 2000).…”

Section: Cs Variationsmentioning

confidence: 99%

Invertebrate learning and memory: Fifty years of olfactory conditioning of the proboscis extension response in honeybees

Giurfa

Sandoz²

2012

Learn. Mem.

353

334

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The honeybee Apis mellifera has emerged as a robust and influential model for the study of classical conditioning, thanks to the existence of a powerful Pavlovian conditioning protocol, the olfactory conditioning of the proboscis extension response (PER). In 2011, the olfactory PER conditioning protocol celebrates 50 years since it was first introduced by Kimihisa Takeda in 1961. Here, we review its origins, developments, and perspectives in order to define future research avenues and necessary methodological and conceptual evolutions. We show that olfactory PER conditioning has become a versatile tool for the study of questions in extremely diverse fields in addition to the study of learning and memory and that it has allowed behavioral characterizations, not only of honeybees, but also of other insect species, for which the protocol was adapted. We celebrate, therefore, Takeda's original work and prompt colleagues to conceive and establish further robust behavioral tools for an accurate characterization of insect learning and memory at multiple levels of analysis.Classical conditioning (Pavlov 1927) is a form of conditioning in which a subject learns to associate a neutral stimulus (the "conditioned stimulus," or CS), which does not originally elicit a behavioral response, with a stimulus of biological significance (the "unconditioned stimulus," or US), which elicits an innate, often reflexive, response. Through this association, the originally neutral stimulus acquires the capacity to elicit a conditioned response.Decades of research on animal learning and memory have established some invertebrates (e.g., the sea hare, Aplysia californica, the fruit fly, Drosophila melanogaster, the honeybee, Apis mellifera) as standard models for the study of classical conditioning (Giurfa 2007b;Menzel et al. 2007). This success can be attributed to the fact that these animals can learn nonassociative as well as Pavlovian and operant associations and possess relatively simple nervous systems that allow retracing of these phenomena to the cellular and molecular levels in different kinds of laboratory preparations. Among these invertebrates, the honeybee, Apis mellifera, has emerged as a robust and influential model for the study of

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“…There is really no need to think our designs in that area must be based on human neurophysiology. Some good references here are (Farris and Sinakevitch 2003) (basic theoretical background), (Sjöholm et al 2005) (very good on mushroom body discussions) and (Haehnel and Menzel 2010). Also, birds have an analogue of prefrontal cortex which is useful to think about: see (Herrold et al 2011) and (Wang et al 2010).…”

Section: Discussionmentioning

confidence: 99%

Computation in networks

Peterson

2015

Comput Cogn Sci

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We present an introduction to the modeling of networks of nodes which parse the information presented to them into an output. One example is the nodes are excitable neurons which are collected into a nervous system for an animal whether invertebrate or vertebrate. We will focus on the development of the ideas and tools that might help us understand how to build a model of such a system being careful to explain the many approximations or model errors we make along the way. We start with a discussion of low level biophysical concepts such as the cable equation and the Hodgkin -Huxley model and end with graph based models of computation. We also include motivational arguments that show hardware and software issues in neural models are interwined. ReviewIn this article, we will discuss some of the principles behind models of biological information processing. As usual, we therefore use tools at the interface between science, mathematics and computer science. We want to find the right abstraction of the wealth of biological detail that is available; the right design to guide us in the development of our modeling environment. The details of how proteins interact, how genes interact and how neural modules interact to generate the high level outputs we find both interesting and useful are known to some degree but it is quite difficult to use this detailed information to build models of high level function. In all models of this type, we are asking high level questions and wondering how we might create a model that gives us some insight. All such models have built in assumptions and we must train ourselves to think abut these carefully. We must question the abstractions of the messiness of reality that led to the model and be prepared to adjust the modeling process if the world as experienced is different from what the model leads them to expect. There are three primary sources of error when we build models. First, there is the error we make when we abstract from reality; we make choices about which things we are measuring are important. We make further choices about how these things relate to one another. Perhaps we model this with mathematics, diagrams, words etc; whatever we choose to do, there is error we make. This is called Model error. Then, we make error when we use computational tools to solve our abstract models which is called Truncation error. Finally, since we can not store numbers exactly in any computer system, there is always a loss of accuracy because of this. This is called Round Off error. All three of these errors are always present and so the question is how do we know the solutions our models suggest relate to the real world? We must take the modeling results and go back to original data to make sure the model has relevance.With all this said, let's start looking at the fundamental building blocks of information transmission in a living neural system. The neurotransmitters and other signaling molecules to accomplish coordination of effort between individual cells were laid down probably in ...

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Sensory Representation and Learning-Related Plasticity in Mushroom Body Extrinsic Feedback Neurons of the Protocerebral Tract

Cited by 56 publications

References 64 publications

Neural substrate for higher-order learning in an insect: Mushroom bodies are necessary for configural discriminations

Neural substrate for higher-order learning in an insect: Mushroom bodies are necessary for configural discriminations

Invertebrate learning and memory: Fifty years of olfactory conditioning of the proboscis extension response in honeybees

Computation in networks

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