1 modeling the impact of auditory training research advisor: prof. aditya mathur school of...
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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.