Pharmacological Analysis of Ionotropic Glutamate Receptor Function in Neuronal Circuits of the Zebrafish Olfactory Bulb

Tabor, Rico; Friedrich, Rainer W.

doi:10.1371/journal.pone.0001416

Cited by 28 publications

(19 citation statements)

References 61 publications

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“…In Drosophila, gain control is achieved by a nonlinear transfer function at the first synapse in the olfactory pathway [10]. In the insect antennal lobe and in the olfactory bulb, the average output firing rate remains relatively stable even if the input intensity varies over a wide range, presumably as a consequence of distributed inhibitory feedback onto the output neurons [18][19][20]. Hence, gain control in the first olfactory processing center may equalize the output activity and thereby contribute to the orthogonalization of activity patterns.…”

Section: Discussionmentioning

confidence: 99%

“…We found that the approximate connectivity W (f f ′ ) a , which does not attempt to equalize the outputs, achieves considerably better orthogonalization than W (f f ) a . As is apparent from (20), its coupling strengths depend not only on the correlations between the channels but also on the input activity levels of the respective neurons. It would be interesting to see whether the performance of the network studied in [27] could be improved by implementing connectivity…”

Section: Discussionmentioning

confidence: 99%

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Pattern orthogonalization via channel decorrelation by adaptive networks

et al. 2009

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The early processing of sensory information by neuronal circuits often includes a reshaping of activity patterns that may facilitate the further processing of stimulus representations in the brain. Motivated by recent studies in the olfactory system we study simple adaptive networks that aim to orthogonalize activity patterns representing similar stimuli. Biologically it is plausible that the adaptation is driven by simultaneous correlations between the input channels rather than by the similarity of input patterns that the animal experiences at different times. We demonstrate that networks can achieve effective pattern orthogonalization through channel decorrelation if they also equalize their output levels. In feedforward networks adaptation fails for even moderately similar input patterns. Recurrent networks do not have that limitation. When optimized for linear neural dynamics, they can orthogonalize the representations of highly similar input patterns quite well, even if a threshold-linear nonlinearity is retained in the neural dynamics. For complex stimulus ensembles the rectifying neural dynamics can degrade the network performance significantly. Our results provide insights into fundamental features of simplified inhibitory networks that may be relevant for pattern orthogonalization by neuronal circuits.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Pattern orthogonalization via channel decorrelation by adaptive networks

et al. 2009

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“…Primary sensory neurons excite mitral cells through AMPA and kainic ionotropic glutamate receptors (Tabor and Friedrich, 2008); therefore, treatment with kainic acid is expected to induce neural activity in the olfactory bulb. Adult fish were anesthetized with 4% tricaine (ethyl 3-aminobenzoate methanesulfonate solution; Sigma-Aldrich; Westerfield, 2000) and placed on a damp sponge.…”

Section: Methodsmentioning

confidence: 99%

Aversive cues fail to activate fos expression in the asymmetric olfactory-habenula pathway of zebrafish

deCarvalho¹,

Akitake²,

Thisse³

et al. 2013

Front. Neural Circuits

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The dorsal habenular nuclei of the zebrafish epithalamus have become a valuable model for studying the development of left-right (L-R) asymmetry and its function in the vertebrate brain. The bilaterally paired dorsal habenulae exhibit striking differences in size, neuroanatomical organization, and molecular properties. They also display differences in their efferent connections with the interpeduncular nucleus (IPN) and in their afferent input, with a subset of mitral cells distributed on both sides of the olfactory bulb innervating only the right habenula. Previous studies have implicated the dorsal habenulae in modulating fear/anxiety responses in juvenile and adult zebrafish. It has been suggested that the asymmetric olfactory-habenula pathway (OB-Ha), revealed by selective labeling from an lhx2a:YFP transgene, mediates fear behaviors elicited by alarm pheromone. Here we show that expression of the fam84b gene demarcates a unique region of the right habenula that is the site of innervation by lhx2a:YFP-labeled olfactory axons. Upon ablation of the parapineal, which normally promotes left habenular identity; the fam84b domain is present in both dorsal habenulae and lhx2a:YFP-labeled olfactory bulb neurons form synapses on the left and the right side. To explore the relevance of the asymmetric olfactory projection and how it might influence habenular function, we tested activation of this pathway using odorants known to evoke behaviors. We find that alarm substance or other aversive odors, and attractive cues, activate fos expression in subsets of cells in the olfactory bulb but not in the lhx2a:YFP expressing population. Moreover, neither alarm pheromone nor chondroitin sulfate elicited fos activation in the dorsal habenulae. The results indicate that L-R asymmetry of the epithalamus sets the directionality of olfactory innervation, however, the lhx2a:YFP OB-Ha pathway does not appear to mediate fear responses to aversive odorants.

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“…Schematic illustration of selected cell types and synaptic connections in the OB. Modified from [91].…”

Section: Computational Functions Of Neuronal Circuits and The Olfactomentioning

confidence: 99%

Neuronal circuits and computations: Pattern decorrelation in the olfactory bulb

Friedrich

Wiechert

2014

FEBS Letters

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a b s t r a c tNeuronal circuits in the olfactory bulb transform odor-evoked activity patterns across the input channels, the olfactory glomeruli, into distributed activity patterns across the output neurons, the mitral cells. One computation associated with this transformation is a decorrelation of activity patterns representing similar odors. Such a decorrelation has various benefits for the classification and storage of information by associative networks in higher brain areas. Experimental results from adult zebrafish show that pattern decorrelation involves a redistribution of activity across the population of mitral cells. These observations imply that pattern decorrelation cannot be explained by a global scaling mechanism but that it depends on interactions between distinct subsets of neurons in the network. This article reviews insights into the network mechanism underlying pattern decorrelation and discusses recent results that link pattern decorrelation in the olfactory bulb to odor discrimination behavior. Ó 2014 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved. Computational functions of neuronal circuits and the olfactory systemHigher brain functions are not directly determined by the biophysical properties of individual neurons but emerge from interactions between many neurons in synaptically connected networks. Deciphering such networks is central to understanding the principles of biological computation, the relationship between brains and computers, brain dysfunction in mental disorders, and the very nature of humans and other animals. Neurons are organized in structured networks, or circuits, that are typically defined as circumscribed populations of interconnected neurons. Small circuits such as repetitive columnar elements of the optic lobes in Drosophila may be comprised of <100 The challenge to understand a neuronal computation obviously depends on the complexity of the computation and the underlying circuit. Some computations can be described based on first-order statistical properties of neuronal connectivity (average connection strength) and based on univariate properties of neuronal activity or simply mean firing rate. These quantities can often be measured using well-established methods and the computations can often be described by tractable mathematical models. One example of such a computation is ''normalization'', an important elementary operation that scales responses of individual neurons as a function of the mean population activity [4,5]. Other computations, however, depend on higher-order properties of connectivity and on multivariate properties of activity patterns. These diverse and potentially complex computations have not yet been explored exhaustively. Some of these computations are likely to depend on the activity of specific subsets of neurons and on specific connectivity. For example, receptive field properties of neurons in primary visual cortex are thought to be shaped by specific http://dx

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Pharmacological Analysis of Ionotropic Glutamate Receptor Function in Neuronal Circuits of the Zebrafish Olfactory Bulb

Cited by 28 publications

References 61 publications

Pattern orthogonalization via channel decorrelation by adaptive networks

Pattern orthogonalization via channel decorrelation by adaptive networks

Aversive cues fail to activate fos expression in the asymmetric olfactory-habenula pathway of zebrafish

Neuronal circuits and computations: Pattern decorrelation in the olfactory bulb

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