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Modeling the impact ofAuditory Training

Research Advisor:

Prof. Aditya Mathur

School of Industrial Engineering

Department of Computer Science

Purdue University

Presented By:

Alok Bakshi

March 10, 2006Auditory Neuroscience LabNorthwestern University, Evanston

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Research Objective

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

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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.

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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?

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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

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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

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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

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Auditory Pathways

Ascending Auditory PathwayInformation from both the ears is carried to higher centers

(focus on it in this presentation)

Descending Auditory PathwayBrain influences the processing of information

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Human Ear

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

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Ascending Auditory Pathway

http://emsah.uq.edu.au/linguistics/ic310/Gif/audpath.gif

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Brainstem Evoked Auditory Potential

http://www.iurc.montp.inserm.fr/cric/audition/english/audiometry/ex_ptw/e_pea2_ok.gif

http://www.iurc.montp.inserm.fr/cric/audition/english/audiometry/ex_ptw/voies_potentiel.jpg

What does the potential represent? Ensemble behavior? At what points in the pathway?

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Auditory Qualities

Hearing Involves perception of

Loudness

Pitch

Timbre

Sound Localization

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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

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Phase Locking

Hair Cells follow waveform of low frequency sounds

Resultant phase locking provide temporal information in the form of inter-aural time differences

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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

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Tuning Curve

Frequency

Intensity

Threshold

Characteristic Frequency

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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)

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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

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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

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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

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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

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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

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Modeling Perspectives

Stochastic versus Deterministic

Phenomenological versus Noumenal

Level of abstraction Computationally tractable Resemble the actual system

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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

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Option – I Cont’d

Advantages Nearer to reality Easy to validate against experimental data

Disadvantage Computationally intensive

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Option – I Cont’d

Neuron Model

Auditory Pathway Unit

Interneuron

Dendrites

Soma

Axons

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Option –I Cont’d

Input

Output

Unit 1

Unit 2

Unit 3

Unit 4

Feedback ???

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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

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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

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Option – II Cont’d

Sound

Encode Intensity

Encode Frequency

Encode Timbre

Interpretation of Sound

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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]

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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

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action potential

Ca2+

Na+

K+

-70mV

Ions/proteins

Molecular basis

Gerstner W. and Kistler W., Spiking Neuron Models’02

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Action Potential

Voltage

Time Hyper-Polarization

Spike

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Ion Channels

Each channel opens with rate i and closes with rate i

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

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Channel Kinetics

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

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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

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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

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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

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Hodgkin-Huxley Model

tm âmât

= -m + m¥

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Hodgkin-Huxley Model –Cont’d

Functions of voltage V

Hodgkin-Huxley model successfully describes the mechanism of Action Potential

The model is completely deterministic

iand i

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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

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Ion Channel Kinetics for Na

Mino H. et al. Comparison of Algorithms for the Simulation of Action Potentials with Stochastic Sodium Channels’ 02

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Approximation Algorithm

Langevin description of cellular

automaton model

Channel density variable instead of

modeling individual ion channels

Computationally less intensive but poor

performance

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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

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Validation

Validate against what?

Auditory Evoked Responses Data from other animals ???

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Progress so far…

Studied anatomical structure of the auditory pathway

Surveyed various models of neuron and neural networks

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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

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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.