A Computational Model of Olivocochlear Neurons to Determine Effects of Synaptic Circuitry on Action Potential Generation
Friday, September 16, 2016 — Poster Session IV
- JA Drew
- CJ Weisz
Computational methods are frequently implemented in neuroscience in order to theoretically examine and predict the functionality of individual neurons. Our research works to understand the synaptic circuitry of the olivocochlear (OC) efferent neurons, the final component of the auditory efferent pathway, which sends signals to the cochlea where the initial encoding of auditory stimuli occurs. The medial OC (MOC) neurons are implicated in gain control (amplification or dampening of sound response), as well as protection against hearing loss from over-stimulation, by innervating and affecting the electromotility of the outer hair cells (OHCs). We have constructed a compartmental model of a MOC neuron using the NEURON modeling software that allows us to customize the topology, geometry, and biophysical properties of the cell. Synaptic inputs, based on electrophysiological experiments, as well as values from the literature, were added to the neurons’ dendritic arbors. The model MOC neuron has a specified input resistance, an A-type potassium conductance, as well as voltage-gated potassium (Kv2.2) and sodium channels (Nav) in order to replicate experimental data and literature values. Potassium and sodium channel conductances, as well as current densities, were sequentially modulated to replicate experimental measures of MOC action potential threshold and waveform. This will be used to determine the effect of dendritic structural complexity on neuronal electrical properties. We will also use the model to simulate synaptic inputs using post-synaptic potential waveform data collected from electrophysiological experiments. This will predict the effect of heterogeneous synaptic input on action potential generation.
Category: Computational Biology