Abstract
The neurons in the mammalian (gerbil, cat) dorsal cochlear nucleus (DCN) have responses to tones and noise that have been used to classify them into unit types. These types (I–V) are based on excitatory and inhibitory responses to tones organized into plots called response maps (RMs). Type I units show purely excitatory responses, while type V units are primarily inhibited. A computational model of the neural circuitry of the mammalian DCN, based on the MacGregor neuromime, was used to investigate RMs of the principal cells (P-cells) that represent the fusiform and giant cells. In gerbils, fusiform cells have been shown to have primarily type III unit response properties; however, fusiform cells in the cat DCN are thought to have type IV unit response properties. The DCN model is based on a previous computational model of the cat (Hancock and Voigt Ann Biomed Eng 27: 73–87, 1999) and gerbil (Zheng and Voigt Ann Biomed Eng 34: 697–708, 2006) DCN. The basic model for both species is architecturally the same, and to get either type III unit RMs or type IV unit RMs, connection parameters were adjusted. Interestingly, regardless of the RM type, these units in gerbils and cats show spectral notch sensitivity and are thought to play a role in sound localization in the median plane. In this study, further parameter adjustments were made to systematically explore their effect on P-cell RMs. Significantly, type I, type III, type III-i, type IV, type IV-T and type V unit RMs can be created for the modeled P-cells. Thus major RMs observed in the cat and gerbil DCN are recreated by the model. These results suggest that RMs of individual DCN projection neurons are the result of specific assortment of excitatory and inhibitory inputs to that neuron and that subtle differences in the complement of inputs can result in different RM types. Modulation of the efficacy of certain synapses suggests that RM type may change dynamically.
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Zheng, X., Voigt, H.F. Computational model of response maps in the dorsal cochlear nucleus. Biol Cybern 95, 233–242 (2006). https://doi.org/10.1007/s00422-006-0081-9
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DOI: https://doi.org/10.1007/s00422-006-0081-9