Compartmental Model for Binaural Coincidence Detector NeuronsBertrand DelgutteZachary Smith and Leonardo Cedolin, SHBTJonathan Simon, University of MarylandMotivation• Provide understanding of how neurons work, and how their structure defines their information-processing capabilities.• Traditional teaching formats such as lectures and discussion of literature papers do not give sufficient intuition.Specific Goals• Provide hands-on experience with modern compartmental model of a neuron.• Experiment with model parameters and learn their role in neural signal processing.Model System• Binaural coincidence detector neurons in the auditory brainstem.Interaural time difference is a cue to sound source azimuthBinaural Coincidence Detector NeuronsHigh FrequenciesLow FrequenciesSmith & Rubel, 1979Axons from left earAxons from right earThe Model• Developed by Jonathan Simon at University of Maryland• Based on coincidence detector neurons in the chick• Compartmental model: Neuron geometry is explicitly represented• Includes known membrane channels (Hodgkin-Huxley, synaptic, low-threshold K+, etc…)• All model parameters easily manipulated with GUI• Implemented in NEURON, a general, high-level language for neural modelingBuilding a compartmental modelC. Circuit model for small length of passive cable-> Also need active membrane channelsCompartmental Model of Coincidence Detector NeuronSomaLeft Dendrite Right DendriteHillockAxonSynaptic Inputs from Left EarSynaptic Inputs from Right EarDendritic filtering and attenuation• Transient response of linear cable to impulse of current at different distances from the current source.• Both latency and temporal spread increase with distance (lowpass filtering). Peak amplitude decreases (attenuation).2cimaGλρ=Space ConstantPoint vs. compartmental neuron modelsGdGdGmCmGrEsEsGlGmCmGrEsEsGlPoint neuron 3-compartment modellrsmlrGGVEGGG+=++2()(1 )(2 )lr lrdsmlr md lr mddGG GGGVEGGG GGGG GGG++=++ + + +• Synaptic potential depends only on sum Gl+Grfor point-neuron model, but also depends on product GlGrfor 3-compartment model. • Point neuron does not distinguish between monaural and binaural coincidences.Better coincidence detection for 3-compartment model• Binaural: Gl=Gr=Gb• Monaural: Gl=0, Gr=2Gb• Fixed Parameters: Es=100mV, Gm=100,
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