P185 Fitting the data and describing neural computation with interaural time differences in the human medial superior olive
Petr Marsalek *1, Pavel Sanda 2, Zbynek Bures 3,
1 Institute of Pathological Physiology, First Medical Faculty, Charles University in Prague, Czech Republic 2 Institute of Computer Science, Czech Academy of Sciences, Prague, Czech Republic 3 College of Polytechnics, Tolsteho 16/1556, 586 01, Jihlava, Czech Republic
*Email: petr.marsalek@lf1.cuni.cz
Introduction
In the auditory nerve and the following auditory pathway, incoming sound is encoded into spike trains - series of neural action potentials. At the third neuron of the auditory pathway, spike trains of the left and right sides converge and are processed to yield sound localization information. Two different localization encoding mechanisms are employed for low and high sound frequencies in two dedicated nuclei in the brainstem: the medial and lateral superior olivary nuclei.
Methods The model neural circuit is based on connected phenomenological neurons. Spikes in these neurons are point events, only spike times matter. The model employs concepts of the just noticeable difference read out by the neural circuit and an ideal observer with access to all the information.
Results Building upon our previous computational model of medial superior olive (MSO), we bring analytical estimates of parameters needed to describe auditory coding in the MSO circuit. We arrive to best estimates for neuronal signaling with the use of just noticeable difference and the ideal observer concepts. We describe spike timing jitter and its role in the spike train processing. We study the dependence of sound localization precision on the sound frequency. All parameters are accompanied with detailed estimates of their values and variability. Discussion Intervals bounding all the parameters from lower and higher values are discussed. Most of the results are obtained by a Monte Carlo simulation of the noisy and random inputs to the model neurons. Where it is possible, analytical calculations of probabilities and curves fitting are used.
Acknowledgements This project was in part funded by Charles University graduate students research program, acronym SVV, No. 260 519/ 2022-2024, to Petr Marsalek div.standard { margin-bottom: 2ex; }
References Bures, Z. (2012). Biol. Cybern., 106(2): 111-122.
Bures, Z. and Marsalek, P. (2013). Brain Res., 1536:16-26.
Sanda, P., Marsalek, P. (2012). Brain Res., 1434: 257-265.
Marsalek, P., Sanda, P., Bures, Z. (2020). An arXiv pre-print. https://arxiv.org/abs/2007.00524
