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  Boosting olfactory cocktail-party performance by semi-supervised learning in mice

Mathis, A., Wei, A., Ding, A., Bethge, M., & Murthy, V. (2017). Boosting olfactory cocktail-party performance by semi-supervised learning in mice. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2017), Salt Lake City, UT, USA.

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Mathis, A, Author
Wei, A, Author
Ding, A, Author
Bethge, M1, Author              
Murthy, VN, Author
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1Universität Tübingen, ou_persistent22              

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 Abstract: Mice are excellent at detecting single odor components in complex mixtures. Yet, when they are trained on single odors alone, they fail to reliably detect target odors in mixtures of multiple odorants. This inability was predicted by a linear readout that was trained using samples from an empirically estimated, nonlinear odor encoding model at the level of receptors. These results from mouse behavior and the modeling suggested that mice learn the ‘cocktail-party task’ discriminatively (Mathis et al. 2016, Neuron). Another possibility for their inability to generalize much beyond simple mixtures, is that lab mice are not exposed to mixtures and thus, have not formed a reliable generative model’ of mixtures. To test this idea, we performed a novel variant of the previous task. As before, mice were trained on single odor-reward associations with two target odors and fourteen distractor odors until they reached performance levels above 90. They were divided in two groups. Outside of the operant-conditioning task, mice were exposed to odor stimuli in an ‘unsupervised way’. One group was presented with mixtures stimuli (UM group) and the other group with single odors (US group). Once an animal reached 90 performance, they were tested on mixture stimuli with 1, 4, 8 and 12 odorants. On the first day, the UM group significantly outperformed the US group, even for single odors, despite similar performance on the last day of training. Over multiple days, the UM group then also improved their performance faster than the US group. Thus, passive exposure to mixtures can aid the detection of single odors in mixtures. We will discuss the implications of this result for recent models of the olfactory cocktail party task.

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 Dates: 2017-02
 Publication Status: Published in print
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 Identifiers: BibTex Citekey: MathisWDBM2017
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Title: Computational and Systems Neuroscience Meeting (COSYNE 2017)
Place of Event: Salt Lake City, UT, USA
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Title: Computational and Systems Neuroscience Meeting (COSYNE 2017)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: II-84 Start / End Page: 157 - 157 Identifier: -