MIT HST 723 - Study References (10 pages)

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Study References



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Study References

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Pages:
10
School:
Massachusetts Institute of Technology
Course:
Hst 723 -

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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 V Gl Gr 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



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