Neuronal adaptation translates stimulus gaps into a population code

Neurons in sensory pathways exhibit a vast multitude of adaptation behaviors, which are assumed to aid the encoding of temporal stimulus features and provide the basis for a population code in higher brain areas. Here we study the transition to a population code for auditory gap stimuli both in neur...

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Bibliographic Details
Published in:PloS one 2014-04, Vol.9 (4), p.e95705-e95705
Main Authors: Yuan, Chun-Wei, Khouri, Leila, Grothe, Benedikt, Leibold, Christian
Format: Article
Language:English
Subjects:
Adaptation
Analysis
Auditory discrimination
Biology and Life Sciences
Brain
Chiroptera
Colliculus
Computation
Computational neuroscience
Computer and Information Sciences
Computer simulation
Eptesicus fuscus
Firing rate
Humans
Independent component analysis
Inferior colliculus
Models, Neurological
Neural circuitry
Neurons
Neurons - physiology
Neurosciences
Owls
Population
Research and Analysis Methods
Sensory neurons
Stimuli (Psychology)
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Summary:Neurons in sensory pathways exhibit a vast multitude of adaptation behaviors, which are assumed to aid the encoding of temporal stimulus features and provide the basis for a population code in higher brain areas. Here we study the transition to a population code for auditory gap stimuli both in neurophysiological recordings and in a computational network model. Independent component analysis (ICA) of experimental data from the inferior colliculus of Mongolian gerbils reveals that the network encodes different gap sizes primarily with its population firing rate within 30 ms after the presentation of the gap, where longer gap size evokes higher network activity. We then developed a computational model to investigate possible mechanisms of how to generate the population code for gaps. Phenomenological (ICA) and functional (discrimination performance) analyses of our simulated networks show that the experimentally observed patterns may result from heterogeneous adaptation, where adaptation provides gap detection at the single neuron level and neuronal heterogeneity ensures discriminable population codes for the whole range of gap sizes in the input. Furthermore, our work suggests that network recurrence additionally enhances the network's ability to provide discriminable population patterns.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0095705