MIT HST 723 - Study References (10 pages)
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Study References
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- Pages:
- 10
- School:
- Massachusetts Institute of Technology
- Course:
- Hst 723 -
Documents
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14 pages
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Suppression in auditory-nerve fibers of cats
20 pages
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Two- tone unmasking and suppression in a forward-masking situation
13 pages
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Relationship between perception of spectral ripple and speech recognition
18 pages
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Binaural and cochlear disparities
7 pages
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Localization cues with bilateral cochlear implants
12 pages
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Fundamentals of Perceptual Audio Encoding
7 pages
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SYNAPTIC MECHANISMS FOR CODING TIMING IN AUDITORY NEURONS
20 pages
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5 pages
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54 pages
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Localization cues with bilateral cochlear implants
13 pages
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10 pages
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36 pages
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11 pages
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10 pages
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Two-tone suppression in cochlear mechanics
12 pages
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Anatomical Organization of the Auditory Cortex
7 pages
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Model for Interaural Time Difference Sensitivity
13 pages
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Estimates of Human Cochlear Tuning at Low Levels
14 pages
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23 pages
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Speech recognition in noise as a function
17 pages
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Tonotopic organization of human auditory cortex
10 pages
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Lab 4- Compartmental Model of Binaural Coincidence Detector Neurons
4 pages
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Mammalian Ventral Cochlear Nucleus
16 pages
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Fundamentals of Perceptual Audio Coding
30 pages
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Neuromagnetic Correlates of Streaming in Human Auditory Cortex
7 pages
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Threshold for intracellular current injections
9 pages
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22 pages
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69 pages
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 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
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Study Guide: Midterm 2
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