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 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 azimuth

Binaural Coincidence Detector Neurons High Frequencies Low Frequencies Smith & Rubel, 1979 Axons from left ear Axons from right ear

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 (Hodgkin- Huxley, 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 DendriteRight Dendrite Hillock Axon Synaptic Inputs from Left Ear Synaptic Inputs from Right Ear

Dendritic 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). Space Constant

Point vs. compartmental neuron models GdGd GdGd GmGm CmCm GrGr EsEs EsEs GlGl GmGm CmCm GrGr EsEs EsEs GlGl Point neuron3-compartment model Synaptic potential depends only on sum G l +G r for point-neuron model, but also depends on product G l G r for 3-compartment model. Point neuron does not distinguish between monaural and binaural coincidences.

Better coincidence detection for 3- compartment model Binaural: G l =G r =G b Monaural: G l =0, G r =2G b Fixed Parameters: E s =100mV, G m =100, G d =20

Extra slides

Binaural coincidence mechanism for coding interaural time differences (ITD) SOUND COCHLEAR FILTER INTERNAL DELAY X NEURAL RESPONSE COCHLEAR FILTER COINCIDENCE DETECTOR CONTRA EAR IPSI EAR ITD

User Interface

Result: ITD tuning improves as synaptic inputs get farther from soma along dendrites Interaural Phase Difference (degree) Normalized Discharge Rate Distance from Soma 10% 30% 90%

Result: There is an optimal frequency for every dendritic length Interaural Phase Difference (degree) Discharge Rate (spikes/sec) 500 Frequency (Hz)

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