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Compartmental Model for Binaural Coincidence Detector Neurons Bertrand Delgutte Zachary Smith and Leonardo Cedolin, SHBT Jonathan Simon, University of Maryland Motivation • Provide understanding of how neurons work, and how their structure defines their informationprocessing 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 azimuth Binaural Coincidence Detector Neurons High Frequencies Low Frequencies Axons from left ear Axons from right ear Smith & Rubel, 1979 The 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 (HodgkinHuxley, synaptic, low-threshold K+, etc…) • All model parameters easily manipulated with GUI • Implemented in NEURON, a general, high-level language for neural modeling Building a compartmental model C. Circuit model for small length of passive cable -> Also need active membrane channels Compartmental Model of Coincidence Detector Neuron Soma Left Dendrite Synaptic Inputs from Left Ear Right Dendrite Hillock Axon Synaptic Inputs from Right Ear Dendritic filtering and attenuation Space Constant c a 2 iGm • 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). Point vs. compartmental neuron models Point neuron 3-compartment model Gd Gl Gm Cm Es Gr Gm Es Gl Gr V Es Gm Gl Gr Gl V Gd Cm Es Gr Es Gl Gr 2Gl Gr Gd Es Gm (Gl Gr )(1 Gm Gd ) Gl Gr (2 Gm Gd ) Gd • Synaptic potential depends only on sum Gl+Gr for point-neuron model, but also depends on product GlGr for 3-compartment model. • Point neuron does not distinguish between monaural and binaural coincidences. Better coincidence detection for 3compartment model • Binaural: Gl=Gr=Gb • Monaural: Gl=0, Gr=2Gb • Fixed Parameters: Es=100mV, Gm=100, Gd=20 Extra slides Binaural coincidence mechanism for coding interaural time differences (ITD) IPSI EAR ITD COCHLEAR FILTER SOUND COINCIDENCE DETECTOR CONTRA EAR COCHLEAR FILTER INTERNAL DELAY X NEURAL RESPONSE User Interface Result: ITD tuning improves as synaptic inputs get farther from soma along dendrites 1 Distance from Soma 10% Normalized Discharge Rate 0.9 0.8 30% 0.7 90% 0.6 0.5 0.4 0.3 0.2 0.1 0 -180 -135 -90 -45 0 45 90 Interaural Phase Difference (degree) 135 180 Result: There is an optimal frequency for every dendritic length Discharge Rate (spikes/sec) 400 Frequency (Hz) 300 500 800 1250 300 200 100 0 -180 -90 0 90 Interaural Phase Difference (degree) 180 Student Feedback Pros • • • • • The lab provides the basic understanding of a compartmental model I am happy to work with a full-blown model and not a baby version We had the opportunity to be creative and try different parameters It was very user friendly The simulations really drove home the reasons for using a compartmental model in the first place Cons • • • This lab was a little too complicated… I prefer something more straightforward. All we did was load the configuration file and press `Init & Run’. I must admit that the lab was pretty "dry". General • The labs provide the best available introduction to the field. What next? • Improve existing laboratory exercise: – Make the lab less “cookbook” – Make user interface less daunting • Connect neuron model to model of signal processing by normal and pathological ears • Develop more challenging simulations for advanced classes (e.g. requiring programming in NEURON) Interaural time difference is a cue to sound source azimuth Electrical circuit for small segment of nerve fiber Synapse position Farunge Basic circuit elements