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School of Industrial Engineering Department of Computer Science Purdue University Modeling the impact of Auditory Training Research Advisor: Presented By: Prof. Aditya Mathur Alok Bakshi March 10, 2006 Auditory Neuroscience Lab Northwestern University, Evanston 1 Research Objective To construct and validate a model to understand the effect of treatment on children with learning disabilities and/or auditory disorders 2 Objective of This Meeting To present our understanding of the auditory pathway and progress made towards the goal of obtaining a validated computational model of the auditory pathway. To discuss possible approaches to the construction and validation of a model of the auditory pathway. 3 Background Children with learning problems are unable to discriminate rapid acoustic changes in speech It was observed that “auditory training” improves the ability to discriminate and identify an unfamiliar sound [Bradlow et al. 1999] Can a computational model reproduce this observation? 4 Methodology Study physiology of Auditory System Simulate the auditory pathway making new models/using existing models of individual components Validate it against experimental results pertaining to auditory systems 5 Methodology – Cont’d Mimic experimental results of auditory processing tasks on children with disabilities to gain insight about the causes of malfunction Experiment with the validated model to asses the effect of treatments on children with auditory/learning disabilities 6 Auditory System From Ear to Auditory Cortex Transforms sound waves into distinct patterns of neural activity Integrated with information from other sensory systems to guide behavior and intra-species communication 7 Auditory Pathways Ascending Auditory Pathway Information from both the ears is carried to higher centers (focus on it in this presentation) Descending Auditory Pathway Brain influences the processing of information 8 Human Ear http://www.owlnet.rice.edu/~psyc351/Images/Ear.jpg 9 Ascending Auditory Pathway http://emsah.uq.edu.au/linguistics/ic310/Gif/audpath.gif 10 Brainstem Evoked Auditory Potential What does the potential represent? Ensemble behavior? At what points in the pathway? http://www.iurc.montp.inserm.fr/cric/audition/english/audiometry/ex_ptw/voies_potentiel.jpg http://www.iurc.montp.inserm.fr/cric/audition/english/audiometry/ex_ptw/e_pea2_ok.gif 11 Auditory Qualities Hearing Involves perception of Loudness Pitch Timbre Sound Localization 12 Place Coding Different regions of the basilar membrane vibrate differentially at different frequencies Thus place of maximum displacement gives topographical mapping of frequency (Tonotopy) Conserved throughout the auditory system 13 Phase Locking Hair Cells follow waveform of low frequency sounds Resultant phase locking provide temporal information in the form of inter-aural time differences 14 Auditory Neuron Cell bodies in Spiral Ganglion Send axons to Cochlear Nucleus Two Types Type I: Innervate Inner Hair Cell Type II: Innervate Outer Hair Cell 15 Tuning Curve Intensity Threshold Characteristic Frequency Frequency 16 Cochlear Nucleus Auditory Nerves connects almost exclusively to Ipsilateral Cochlear Nucleus Three divisions Anteroventral Cochlear Nucleus (AVCN) Posteroventral Cochlear Nucleus (PVCN) Dorsal Cochlear Nucleus (DCN) 17 Cochlear Nucleus Contains neurons of different response types Breaks up sound into pieces of qualitatively different aspects Encode these aspects and send them to higher centers for higher processing 18 Superior Olivary Complex (SOV) Receives bilateral ascending input from Ventral Cochlear Nucleus Essential for Sound Localization Four Divisions Medial Superior Olivary Complex Lateral Superior Olivary Complex Medial Nucleus of the Trapezoid Body Periolivary Nuclei 19 Medial Superior Olive Uses inter-aural time difference as a cue for sound localization Receives excitatory inputs from both anteroventral cochlear nucleus Cells work as Coincidence Detectors responding when both inputs arrive at the same time 20 Lateral Superior Olive Uses inter-aural intensity difference as a cue for sound localization It receives Excitatory input from Ipsilateral Cochlear nucleus Inhibitory input from Contralateral Cochlear Nucleus 21 Inferior Colliculus Thought to be have Auditory-Space Map Neurons in auditory-space map responds best to sound originating from a specific region of space 22 Modeling Perspectives Stochastic versus Deterministic Phenomenological versus Noumenal Level of abstraction Computationally tractable Resemble the actual system 23 Modeling Option - I Modeling of Individual Neuron [Hodgkin-Huxley model etc.] Identification of anatomically different units/sub-units in auditory pathway Separate modeling of units by simulating many neurons with appropriate parameters Auditory pathway simulation by simulating these units 24 Option – I Cont’d Advantages Nearer to reality Easy to validate against experimental data Disadvantage Computationally intensive 25 Option – I Cont’d Soma Dendrites Axons Interneuron Neuron Model Auditory Pathway Unit 26 Option –I Cont’d Input Unit 1 Unit 2 Feedback ??? Unit 4 Unit 3 Output 27 Modeling Option - II Identify functionally different units of auditory pathway Define and model input/output relationship for these units Simulate the auditory pathway by simulating these units together 28 Option – II Cont’d Advantages Computationally tractable Model gives more insight about the system Disadvantage Doesn’t represent biological reality completely Don’t have complete understanding 29 Option – II Cont’d Encode Intensity Sound Encode Frequency Interpretation of Sound Encode Timbre 30 Neuron Models Binary Neuron [Olshausen B. A. 2004 Sparse coding of sensory inputs] On/off depending on the input Firing Rate Neuron [Tanaka S. 2001 Computational approaches to the architecture and operations of the prefrontal cortical circuit for working memory. ] Firing rate instead of individual spikes are modeled Integrate and Fire model [Izak, R. 1999 Sound source localization with an integrate-and-fire neural system ] Hodgkin-Huxley model [Hodgkin A. et. al. 1952 Measurement of current-voltage relations in the membrane of the giant axon of Loligo] 31 Neuron Models – Cont’d Hodgkin-Huxley model Chaotic but completely deterministic Approximation Algorithm Fox R. F. 1997 Stochastic Versions of the Hodgkin[ Huxley Equations] White noise term in HH model Channel State Tracking Algorithm [Rubinstein 1995 Threshold Fluctuations in an N Sodium Channel Model of the Node of Ranvier ] Simple but computationally intensive Channel Number Tracking Algorithm Gillespie D. T. 1977 Exact [ Stochastic Simulation of Coupled Chemical Reactions] Computationally efficient 32 Molecular basis -70mV Na+ action potential K+ Ca2+ Ions/proteins Gerstner W. and Kistler W., Spiking Neuron Models’0233 Action Potential Voltage Spike Time Hyper-Polarization 34 Ion Channels Each channel opens with rate ai and closes with rate bi Potassium ion channel Has four similar sub-units Each subunit is open or closed independently Open iff all four sub-units are open Sodium Ion channel Three similar sub-units and one slow sub-unit The channel id open iff all four sub-units are open 35 Channel Kinetics www.sis.ipm.ac.ir/seminars/weekly%20seminars/course/Neural%20modeling/babadi04.ppt 36 Binary Neuron Each neuron has two states On (1) Off (0) Each input to the neuron has a particular weight-age If the combined input exceeds threshold then neuron comes into on (1) state 37 Firing Rate Neuron The firing rate is a function of voltage Firing rate rather than individual spikes are modeled Hence encodes information related with firing rate and ignores spikes 38 Integrate and Fire Neuron Time of occurrence of Action Potential is modeled rather than its shape Dynamics of Neuron Sub-Threshold Supra-Threshold Conductance due to Na and K channels ignored in SubThreshold voltage If voltage becomes greater than threshold A spike is generated Membrane potential is reset to a value for refractory period 39 Hodgkin-Huxley Model m m t m m 40 Hodgkin-Huxley Model –Cont’d ai and bi Functions of voltage V Hodgkin-Huxley model successfully describes the mechanism of Action Potential The model is completely deterministic 41 Stochastic Phenomena Kinetics of ion channels as continuous time discrete state Markov jumping process Channel noise affects Stability of resting potential Temporal representation of sound 42 Ion Channel Kinetics for Na Mino H. et al. Comparison of Algorithms for the Simulation of Action Potentials with Stochastic Sodium Channels’ 02 43 Approximation Algorithm Langevin description of cellular automaton model Channel density variable instead of modeling individual ion channels Computationally less intensive but poor performance 44 Exact Algorithms Channel State Tracking Algorithm Tracks state of each individual channel Simple but more computation requirement Channel Number Tracking Algorithm Tracks number of channel in each state Assumes multiple channels are memory-less Computationally quite efficient 45 Validation Validate against what? Auditory Evoked Responses Data from other animals ??? 46 Progress so far… Studied anatomical structure of the auditory pathway Surveyed various models of neuron and neural networks 47 References • • • • • Drawing/image/animation from "Promenade around the cochlea" <www.cochlea.org> EDU website by R. Pujol et al., INSERM and University Montpellier Fox F. R. 1997, Stochastic versions of the Hodgkin-Huxley Equations. Biophysical Journal, Volume 72, 2068-2074 Gunter E. and Raymond R. , The central Auditory System’ 1997 Kraus N. et. al, 1996 Auditory Neurophysiologic Responses and Discrimination Deficits in Children with Learning Problems. Science Vol. 273. no. 5277, pp. 971 – 973 Mino H. et al. 2002, Comparison of Algorithms for the Simulation of Action Potentials with Stochastic Sodium Channels. Annals of Biomedical Engineering, Vol. 30, pp. 578587 48 References – Cont’d Purves et al, Neuroscience 3rd edition •P. O. James, An introduction to physiology of hearing 2nd edition •Ruggero M. A. and Rich N. C. 1991, Furosemide alters Organ of Corti mechanics: Evidence for feedback of Outer Hair Cells upon the Basilar Membrane. The Journal of Neuroscience, 11(4): 1057-1067 •Tremblay K., 1997 Central auditory system plasticity: generalization to novel stimuli following listening training. J Acoust Soc Am. 102(6):3762-73. • 49