Computational principles of neural adaptation for binaural signal integration.
Adaptation to statistics of sensory inputs is an essential ability of neural systems and extends their effective operational range. Having a broad operational range facilitates to react to sensory inputs of different granularities, thus is a crucial factor for survival. The computation of auditory c...
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Public Library of Science (PLoS)
2020-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008020&type=printable |
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| author | Timo Oess Marc O Ernst Heiko Neumann |
| author_facet | Timo Oess Marc O Ernst Heiko Neumann |
| author_sort | Timo Oess |
| collection | DOAJ |
| description | Adaptation to statistics of sensory inputs is an essential ability of neural systems and extends their effective operational range. Having a broad operational range facilitates to react to sensory inputs of different granularities, thus is a crucial factor for survival. The computation of auditory cues for spatial localization of sound sources, particularly the interaural level difference (ILD), has long been considered as a static process. Novel findings suggest that this process of ipsi- and contra-lateral signal integration is highly adaptive and depends strongly on recent stimulus statistics. Here, adaptation aids the encoding of auditory perceptual space of various granularities. To investigate the mechanism of auditory adaptation in binaural signal integration in detail, we developed a neural model architecture for simulating functions of lateral superior olive (LSO) and medial nucleus of the trapezoid body (MNTB) composed of single compartment conductance-based neurons. Neurons in the MNTB serve as an intermediate relay population. Their signal is integrated by the LSO population on a circuit level to represent excitatory and inhibitory interactions of input signals. The circuit incorporates an adaptation mechanism operating at the synaptic level based on local inhibitory feedback signals. The model's predictive power is demonstrated in various simulations replicating physiological data. Incorporating the innovative adaptation mechanism facilitates a shift in neural responses towards the most effective stimulus range based on recent stimulus history. The model demonstrates that a single LSO neuron quickly adapts to these stimulus statistics and, thus, can encode an extended range of ILDs in the ipsilateral hemisphere. Most significantly, we provide a unique measurement of the adaptation efficacy of LSO neurons. Prerequisite of normal function is an accurate interaction of inhibitory and excitatory signals, a precise encoding of time and a well-tuned local feedback circuit. We suggest that the mechanisms of temporal competitive-cooperative interaction and the local feedback mechanism jointly sensitize the circuit to enable a response shift towards contra-lateral and ipsi-lateral stimuli, respectively. |
| format | Article |
| id | doaj-art-e6316c8d421e49e981a227a0248616ba |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2020-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-e6316c8d421e49e981a227a0248616ba2025-08-20T02:00:42ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-07-01167e100802010.1371/journal.pcbi.1008020Computational principles of neural adaptation for binaural signal integration.Timo OessMarc O ErnstHeiko NeumannAdaptation to statistics of sensory inputs is an essential ability of neural systems and extends their effective operational range. Having a broad operational range facilitates to react to sensory inputs of different granularities, thus is a crucial factor for survival. The computation of auditory cues for spatial localization of sound sources, particularly the interaural level difference (ILD), has long been considered as a static process. Novel findings suggest that this process of ipsi- and contra-lateral signal integration is highly adaptive and depends strongly on recent stimulus statistics. Here, adaptation aids the encoding of auditory perceptual space of various granularities. To investigate the mechanism of auditory adaptation in binaural signal integration in detail, we developed a neural model architecture for simulating functions of lateral superior olive (LSO) and medial nucleus of the trapezoid body (MNTB) composed of single compartment conductance-based neurons. Neurons in the MNTB serve as an intermediate relay population. Their signal is integrated by the LSO population on a circuit level to represent excitatory and inhibitory interactions of input signals. The circuit incorporates an adaptation mechanism operating at the synaptic level based on local inhibitory feedback signals. The model's predictive power is demonstrated in various simulations replicating physiological data. Incorporating the innovative adaptation mechanism facilitates a shift in neural responses towards the most effective stimulus range based on recent stimulus history. The model demonstrates that a single LSO neuron quickly adapts to these stimulus statistics and, thus, can encode an extended range of ILDs in the ipsilateral hemisphere. Most significantly, we provide a unique measurement of the adaptation efficacy of LSO neurons. Prerequisite of normal function is an accurate interaction of inhibitory and excitatory signals, a precise encoding of time and a well-tuned local feedback circuit. We suggest that the mechanisms of temporal competitive-cooperative interaction and the local feedback mechanism jointly sensitize the circuit to enable a response shift towards contra-lateral and ipsi-lateral stimuli, respectively.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008020&type=printable |
| spellingShingle | Timo Oess Marc O Ernst Heiko Neumann Computational principles of neural adaptation for binaural signal integration. PLoS Computational Biology |
| title | Computational principles of neural adaptation for binaural signal integration. |
| title_full | Computational principles of neural adaptation for binaural signal integration. |
| title_fullStr | Computational principles of neural adaptation for binaural signal integration. |
| title_full_unstemmed | Computational principles of neural adaptation for binaural signal integration. |
| title_short | Computational principles of neural adaptation for binaural signal integration. |
| title_sort | computational principles of neural adaptation for binaural signal integration |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1008020&type=printable |
| work_keys_str_mv | AT timooess computationalprinciplesofneuraladaptationforbinauralsignalintegration AT marcoernst computationalprinciplesofneuraladaptationforbinauralsignalintegration AT heikoneumann computationalprinciplesofneuraladaptationforbinauralsignalintegration |