Application of a Computationally Efficient Coincidence Detector Model to Simulate Auditory Brainstem Neurons
* Presenting author
Modeling approaches play an important role in the investigation of acoustic information processing along the auditory system. Recent studies found that a relatively simple neuronal model, which produces action potentials depending on the count and timing of modeled synaptic inputs, can simulate a number of response features of auditory brainstem neurons, such as interaural time and intensity difference coding in the lateral superior olive. In this presentation, we review this simple coincidence detector model and discuss its possible applications to several types of auditory neurons. Our applications include principal neurons in the medial superior olive that change their number of action potentials according to interaural time differences and globular bushy cells (GBCs) in the anteroventral cochlear nucleus that send output to the superior olivary complex. By additionally employing an adapting threshold, the model can not only simulate the enhancement of phase-locking relative to auditory nerves but also primary-like-with-notch peristimulus time histograms that are regarded as one of the most important characteristics of GBCs found in vivo. Because of its computational efficiency, the model is also suitable for large-scale simulations that typically include thousands of neurons to study, for example, across-frequency integration and the resulting perception of binaural sounds.