Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components

Chen, Jen-Yung; Marachlian, Emiliano; Assisi, Collins; Huerta, Ramón; Smith, Brian H.; Locatelli, Fernando; Bazhenov, Maxim

doi:10.1523/jneurosci.2345-14.2015

Cited by 37 publications

(64 citation statements)

References 67 publications

(54 reference statements)

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“…Given the influence of inhibitory connections in shaping responses of the AL network, changing the weights of inhibitory connections could change activity after conditioning in ways that we and others have described (Chen et al, 2015;Linster and Smith, 1997). In the honey bee AL, an octopamine-based sucrose reinforcement pathway (Hammer, 1993) specifically targets a group of local inhibitory (GABAergic) interneurons (Sinakevitch et al, 2011(Sinakevitch et al, , 2013.…”

Section: Discussionmentioning

confidence: 96%

“…In doing it in this way, we minimized learning and memory disruptions caused by stress associated with preparation for imaging. Interestingly, the fact that changes affecting mixture representation in the AL are evident 1 day after the experience (Chen et al, 2015;Fernandez et al, 2009;Sandoz et al, 2003) suggests that the changes may constitute a component of a consolidated, protein synthesis-dependent olfactory memory. It remains to be determined if changes we describe in the AL emerge immediately after conditioning or whether they only emerge in the interval from training to imaging that we used.…”

Section: Discussionmentioning

confidence: 99%

“…1C. The fact that the reduction in glomerulus 17 was not consistent across bees rules out the possibility that suppression is a consequence of physical interactions between the components in the mixture and suggests inter-individual differences with regard to how odors are encoded (Chen et al, 2015).…”

Section: Calcium Imagingmentioning

confidence: 99%

See 2 more Smart Citations

Learning about natural variation of odor mixtures enhances categorization in early olfactory processing

Locatelli

Fernández

Smith

2016

Journal of Experimental Biology

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Natural odors are typically mixtures of several chemical components.Mixtures vary in composition among odor objects that have the same meaning. Therefore a central 'categorization' problem for an animal as it makes decisions about odors in natural contexts is to correctly identify odor variants that have the same meaning and avoid variants that have a different meaning. We propose that identified mechanisms of associative and non-associative plasticity in early sensory processing in the insect antennal lobe and mammalian olfactory bulb are central to solving this problem. Accordingly, this plasticity should work to improve categorization of odors that have the opposite meanings in relation to important events. Using synthetic mixtures designed to mimic natural odor variation among flowers, we studied how honey bees learn about and generalize among floral odors associated with food. We behaviorally conditioned honey bees on a difficult odor discrimination problem using synthetic mixtures that mimic natural variation among snapdragon flowers. We then used calcium imaging to measure responses of projection neurons of the antennal lobe, which is the first synaptic relay of olfactory sensory information in the brain, to study how ensembles of projection neurons change as a result of behavioral conditioning. We show how these ensembles become 'tuned' through plasticity to improve categorization of odors that have the different meanings. We argue that this tuning allows more efficient use of the immense coding space of the antennal lobe and olfactory bulb to solve the categorization problem. Our data point to the need for a better understanding of the 'statistics' of the odor space.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

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Learning about natural variation of odor mixtures enhances categorization in early olfactory processing

Locatelli

Fernández

Smith

2016

Journal of Experimental Biology

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“…Similarly, several studies have begun to describe the geometry and connectivity of neural networks in honeybees (Grünewald, 1999;Mobbs, 1984). Sociobiology would greatly benefit from an increased understanding of neural networks that co-vary among behaviorally different life stages, castes, or species with degree of sociality (Chen et al, 2015;Hourcade, Perisse, Devaud, & Sandoz, 2009;Rybak & Menzel, 1993), including simple comparisons between social and nonsocial models (Camiletti, Percival-Smith et al, 2016).…”

Section: Climbing the Network Ladder To Neuronal Networkmentioning

confidence: 99%

From gene list to gene network: Recognizing functional connections that regulate behavioral traits

et al. 2018

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The study of social breeding systems is often gene focused, and the field of insect sociobiology has been successful at assimilating tools and techniques from molecular biology. One common output from sociogenomic studies is a gene list. Gene lists are readily generated from microarray, RNA sequencing, or other molecular screens that typically aim to prioritize genes based on the differences in their expression. Gene lists, however, are often unsatisfying because the information they provide is simply tabular and does not explain how genes interact with each other, or how genetic interactions change in real time under social or environmental circumstances. Here, we promote a view that is relatively common to molecular systems biology, where gene lists are converted into gene networks that better describe the functional connections that regulate behavioral traits. We present a narrative related to honeybee worker sterility to show how network analysis can be used to reprioritize candidate genes based on connectivity rather than their freestanding expression values. Networks can also reveal multigene modules, motifs, clusters or other system‐wide properties that might not be apparent from an ab initio list. We argue that because network analyses are not restricted to “genes” as nodes, their implementation can potentially connect multiple levels of biological organization into a single, progressively complex study system.

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“…The fact that several functional aspects of the input, output and local elements of the AL have been described, makes the insect antennal lobe an interesting system in which behavior, physiology and computational sciences converge to disentangle the neural mechanisms involved in sensory processing (Chan et al, 2018;Chen et al, 2015;Schmuker, Yamagata, Nawrot, & Menzel, 2011;Serrano, Nowotny, Levi, Smith, & Huerta, 2013). In this context, mathematical modeling of the cell interactions that take place in the antennal lobe allows fast evaluation of putative network configurations and also inspires solutions with technological applications in object and pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Gain modulation and odor concentration invariance in early olfactory networks

Marachlian

Huerta²,

Locatelli

2019

Preprint

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A conserved principle of the olfactory system, in most, if not all animals, is that each olfactory receptor interacts with different odorant molecules and each odorant molecule interacts with different olfactory receptors. This broad receptive field of the receptors constitutes the basis of a combinatorial code that allows animals to discriminate many more odorants than the actual number of receptor types that they express. A drawback is that high odorant concentrations recruit lower affinity receptors, which can give rise to the perception of qualitatively different odors. Here we addressed the contribution that early signal-processing in the honey bee antennal lobe does to keep odor representation stable across concentrations. We describe the contribution that GABA-A and GABA-B receptors-dependent-inhibition plays in terms of the amplitude and temporal profiles of the signals that convey odor information from the antennal lobes to the mushroom bodies. GABA reduces the amplitude of odor elicited signals and the number of glomeruli that are recruited in a concentration-dependent way. Blocking GABA-A and GABA-B receptors decreases the correlation among glomerular activity 1 patterns elicited by different concentrations of the same odor. Based on the results we built a realistic computational model of the antennal lobe that could be further used to evaluate the signal processing properties of the AL network under conditions that cannot be achieved in physiology experiments. Interestingly, even though based on rather simplistic topology and interactions among cells solely mediated by GABA-A and GABA-B interactions, the AL model reproduced the key features of the AL stable response in relation to different concentrations.

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Learning Modifies Odor Mixture Processing to Improve Detection of Relevant Components

Cited by 37 publications

References 67 publications

Learning about natural variation of odor mixtures enhances categorization in early olfactory processing

Learning about natural variation of odor mixtures enhances categorization in early olfactory processing

From gene list to gene network: Recognizing functional connections that regulate behavioral traits

Gain modulation and odor concentration invariance in early olfactory networks

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