Modeling the impact of Auditory Training

1. School of Industrial Engineering Department of Computer Science Purdue University Modeling the impact ofAuditory Training Research Advisor: Prof. Aditya Mathur Presented By: Alok Bakshi March 10, 2006 Auditory Neuroscience Lab Northwestern University, Evanston

2. Research Objective To construct and validate a model to understand the effect of treatment on children with learning disabilities and/or auditory disorders

3. 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.

4. 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?

5. 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

6. 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

7. 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

8. 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

9. Human Ear http://www.owlnet.rice.edu/~psyc351/Images/Ear.jpg

10. Ascending Auditory Pathway http://emsah.uq.edu.au/linguistics/ic310/Gif/audpath.gif

11. 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

12. Auditory Qualities Hearing Involves perception of Loudness Pitch Timbre Sound Localization

13. 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

14. Phase Locking Hair Cells follow waveform of low frequency sounds Resultant phase locking provide temporal information in the form of inter-aural time differences

15. 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

16. Tuning Curve Intensity Threshold Characteristic Frequency Frequency

17. 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)

18. 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

19. 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

20. 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

21. 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

22. 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

23. Modeling Perspectives • Stochastic versus Deterministic • Phenomenological versus Noumenal • Level of abstraction • Computationally tractable • Resemble the actual system

24. 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

25. Option – I Cont’d • Advantages • Nearer to reality • Easy to validate against experimental data • Disadvantage • Computationally intensive

26. Option – I Cont’d Soma Axons Dendrites Interneuron Neuron Model Auditory Pathway Unit

27. Unit 1 Unit 2 Unit 4 Unit 3 Option –I Cont’d Input Feedback ??? Output

28. 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

29. 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

30. Option – II Cont’d Encode Intensity Encode Frequency Sound Interpretation of Sound Encode Timbre

31. 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]

32. 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

33. action potential Molecular basis -70mV Na+ K+ Ca2+ Ions/proteins Gerstner W. and Kistler W., Spiking Neuron Models’02

34. Voltage Spike Time Hyper-Polarization Action Potential

35. 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

36. Channel Kinetics www.sis.ipm.ac.ir/seminars/weekly%20seminars/course/Neural%20modeling/babadi04.ppt

37. 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

38. 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

39. 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 Sub-Threshold voltage • If voltage becomes greater than threshold • A spike is generated • Membrane potential is reset to a value for refractory period

40. Hodgkin-Huxley Model

41. a b and i i Hodgkin-Huxley Model –Cont’d • Hodgkin-Huxley model successfully describes the mechanism of Action Potential • The model is completely deterministic Functions of voltage V

42. 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

43. Ion Channel Kinetics for Na Mino H. et al. Comparison of Algorithms for the Simulation of Action Potentials with Stochastic Sodium Channels’ 02

44. Approximation Algorithm • Langevin description of cellular automaton model • Channel density variable instead of modeling individual ion channels • Computationally less intensive but poor performance

45. 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

46. Validation • Validate against what? • Auditory Evoked Responses • Data from other animals ???

47. Progress so far… • Studied anatomical structure of the auditory pathway • Surveyed various models of neuron and neural networks

48. 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. 578-587

49. 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.