Monk, Travis and Savin, Cristina and Lücke, Jörg (2018) Optimal neural inference of stimulus intensities. Scientific reports, 8 (1). p. 10038. ISSN 2045-2322

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In natural data, the class and intensity of stimuli are correlated. Current machine learning algorithms ignore this ubiquitous statistical property of stimuli, usually by requiring normalized inputs. From a biological perspective, it remains unclear how neural circuits may account for these dependencies in inference and learning. Here, we use a probabilistic framework to model class-specific intensity variations, and we derive approximate inference and online learning rules which reflect common hallmarks of neural computation. Concretely, we show that a neural circuit equipped with specific forms of synaptic and intrinsic plasticity (IP) can learn the class-specific features and intensities of stimuli simultaneously. Our model provides a normative interpretation of IP as a critical part of sensory learning and predicts that neurons can represent nontrivial input statistics in their excitabilities. Computationally, our approach yields improved statistical representations for realistic datasets in the visual and auditory domains. In particular, we demonstrate the utility of the model in estimating the contrastive stress of speech.

Item Type: Article
Additional Information: Publiziert mit Hilfe des DFG-geförderten Open Access-Publikationsfonds der Carl von Ossietzky Universität Oldenburg.
Subjects: Science and mathematics > Physics
Technology, medicine, applied sciences > Medicine and health
Divisions: Faculty of Medicine and Health Sciences > Department of Medical Physics and Acoustics
Date Deposited: 12 Sep 2019 11:08
Last Modified: 24 Mar 2020 11:55
URN: urn:nbn:de:gbv:715-oops-42436
DOI: 10.1038/s41598-018-28184-5

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