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Computational principles of neural adaptation for binaural signal integration
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 16, Iss 7, p e1008020 (2020)
- Publication Year :
- 2019
- Publisher :
- Cold Spring Harbor Laboratory, 2019.
-
Abstract
- 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.<br />publishedVersion
- Subjects :
- Auditory Pathways
Computer science
Physiology
Normal Distribution
Social Sciences
Action Potentials
Sensory perception
Biochemistry
Sound localization
DDC 570 / Life sciences
Cell Signaling
Animal Cells
Medicine and Health Sciences
Psychology
Biology (General)
gamma-Aminobutyric Acid
media_common
Neurons
education.field_of_study
Coding Mechanisms
Neural adaptation
Olive
Neurochemistry
Superior Olivary Complex
Neurotransmitters
Adaptation, Physiological
Electrophysiology
medicine.anatomical_structure
Sound
Coding mechanisms
Neuronal plasticity
Excitatory postsynaptic potential
Sensory Perception
Cellular Types
Anatomy
Cues
Algorithms
Research Article
Signal Transduction
Signal Inhibition
QH301-705.5
media_common.quotation_subject
Auditory perception
Population
Auditorisches System
Models, Neurological
Auditory pathways
Sensory system
Stimulus (physiology)
Olivary Nucleus
Inhibitory postsynaptic potential
Membrane Potential
Receptors, GABA
Encoding (memory)
Perception
ddc:570
medicine
Trapezoid body
Animals
Humans
Computer Simulation
Sound Localization
education
Trapezoid Body
Membrane potential
Olive
Neuronale Plastizität
Computational Neuroscience
Behavior
Neural inhibition
Cognitive Psychology
Biology and Life Sciences
Computational Biology
Reproducibility of Results
Auditory Threshold
Cell Biology
Gamma-Aminobutyric Acid
Acoustic Stimulation
Ears
Cellular Neuroscience
Synapses
Cognitive Science
Neuron
Lateral superior olive
Gerbillinae
Neuroscience
Binaural recording
Nucleus
Head
Short-term plasticity
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology, PLoS Computational Biology, Vol 16, Iss 7, p e1008020 (2020)
- Accession number :
- edsair.doi.dedup.....bdae5a35bc15ccede40a17758796fa83
- Full Text :
- https://doi.org/10.1101/863258