Modeling the Auditory Pathway

1
Modeling the Auditory Pathway
School of Industrial Engineering Department of
Computer Science Purdue University
Humans Simulator Project December 3,
2007 Sponsor National Science Foundation
Research Advisor Aditya Mathur
Graduate Student Alok Bakshi
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Objective

• To construct and validate a model of the
  auditory pathway to understand the effect of
  various treatments on children with auditory
  disorders.

3
Background and Problem

• Children with some forms of auditory disorders
  are unable to discriminate rapid acoustic changes
  in speech.
• It has been observed that auditory training
  improves the ability to discriminate and identify
  an unfamiliar sound.
• Computational model desired to reproduce this
  observation.
• A validated model would assist in assessing the
  impact of disorders in the auditory pathway on
  brainstem potential. This would be useful for
  diagnosis. This appears related to fault
  diagnosis and tolerance in software systems. It
  might have an impact on the design of redundant
  software systems.

4
Methodology

• Study physiology of the auditory system.
• Simulate the auditory pathway by constructing new
  models, or using existing models, of individual
  components along the auditory pathway.
• Validate the model against experimental results
  pertaining to the auditory system.
• Mimic experimental results of auditory processing
  tasks in children with disabilities and gain
  insight into the causes of malfunction.
• Experiment with the validated model to assess the
  effects of treatments on children with
  auditory/learning disabilities.

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Characteristics of our approach

• Systems, holistic, approach.
• Detailed versus aggregate models.
• Explicit modeling of inherent anatomical and
  physiological parallelism.

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Progress

• Synaptic model is implemented for connection
  between two neurons
• Following (existing) models incorporated for the
  simulation of the Auditory pathway
• Phenomenological model for the response of
  Auditory nerve fibers
• Computational model of the Cochlear Nucleus
  Octopus Cell

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Brainstem Evoked Auditory Potential
Normal children
Language impaired children
http//www.iurc.montp.inserm.fr/cric/audition/engl
ish/audiometry/ex_ptw/voies_potentiel.jpg
http//www.iurc.montp.inserm.fr/cric/audition/engl
ish/audiometry/ex_ptw/e_pea2_ok.gif
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Auditory Pathway Modeling
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Auditory Neuron Model
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Cochlear Nucleus

• Consists of (at least) 13 types of cells
• Single cell responses differ based on
• of excitatory/inhibitory inputs
• Input waveform pattern

Input tone
Onset response
Buildup response
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Octopus Cell
Octopus Cell
Receives excitatory input from 60-120 AN fibers
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Schematic of a typical Octopus Cell

• Representative Cell
• Has four dendrites
• Receives 60 AN fibers with 1.4 - 4 kHz CF
• Majority of input from high SA fibers, medium SA
  fibers denoted by superscript m

http//www.ship.edu/cgboeree/neuron.gif
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Octopus Cell Model Simplifications

• Four dendrites replaced by a single cylinder
• Active axon lumped into soma
• Synaptic transmission delay taken as constant 0.5
  ms
• Compartmental model employed with
• 15 equal length dendritic compartments
• 2 equal length somatic compartments

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Octopus Cell Model
2 somatic compartments and 15 dendritic
compartments modeled by the same circuit with
different parameters Different number of
dendritic compartments depending on number of
synapses with AN fibers
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Octopus Cell - Output

• The output of the model implemented by Levy et.
  al. is compared against our model on the right
  side of the figure for a tone given at CF in
  figure A
• Same comparison is made in figure B but with a
  tone of different intensity

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Bushy Cell
AN spikes
Bushy Cell
Time
Bushy Cell spikes
Receives excitatory input from 1-20 AN fibers
Time
Latent period
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Bushy Cell Model

• Representative Cell
• Has no dendrites and axon
• The soma is equipotential
• Receives 1-20 AN fibers with different
  characteristic frequency
• Inhibitory inputs ignored in the model

Soma
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Bushy Cell Model Characteristics

• As the number and conductance of inputs is
  varied, the full range of response seen in VCN
  Bushy cell are reproduced
• For inputs with low frequency(lt 1 kHz), the model
  shows stronger phase locking than AN fibers, thus
  preserving the precise temporal information about
  the acoustic stimuli
• The model simulates the spherical bushy cell, but
  doesnt reproduce all characteristics of globular
  bushy cell

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Bushy Cell Model - Output

• Response of Bushy cell for different number of
  input AN fibers (N), and synaptic conductance (A)
• Fig. A shows the response of our implemented
  model for N1 and A 9.1, while the output
  obtained by Rothman et. al. is shown in D for
  same parameter.

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Bushy Cell Model - Output

• Similarly for N5 and A9.1, our implemented
  models response is shown in B, while response of
  model by Rothman et. al. is shown in E
• Finally, the fig. C shows response of our model
  for N1, A18.2 and the corresponding response of
  model by Rothman et. al. is shown in fig. F

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Fusiform Cell
AN discharge rate
Fusiform Cell
Time
Fusiform Cell discharge rate
Receives different inhibitory inputs from DCN
Time
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Fusiform Cell Model

• Exhibit buildup and pauser response and nonlinear
  voltage/current relationship
• The model simulates the soma of fusiform cell
  with three K and two Na voltage dependent ion
  channels
• The model doesnt take into account the Calcium
  conductance
• Doesnt model the synaptic input

Electrical model of fusiform cell
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Fusiform Cell Model Characteristics

• Predicts the electrophysiological properties of
  the fusiform cell by using basic Hodgkin-Huxley
  equations
• Simulates the pauser and buildup response by
  virtue of intrinsic membrane properties
• Synaptic organization of cells in DCN is not
  understood presently, so this model doesnt model
  synapse and take direct current as the input
  instead
• Doesnt rule out the possibility of inhibitory
  inputs as the reason for pauser and buildup
  response

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Next Steps

• Modify the models if they ignore few inputs for
  the sake of simplification, to account for such
  inputs.
• Determine the response of the cochlear nucleus as
  a whole with different input waveforms.
• Add models of additional stages (Superior Olive,
  Lateral Lemniscus, and Inferior Colliculus)
• Validate a partial model of the auditory pathway
  using sound localization.

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References

• Hiroyuki M. Jay T.R. John A.W. Comparison of
  algorithms for the simulation of action
  potentials with stochastic sodium channels.
  Annals of Biomedical Engineering, 30578587,
  2002.
• Kim D.O. Ghoshal S. Khant S.L. Parham K. A
  computational model with ionic conductances for
  the fusiform cell of the dorsal cochlear nucleus.
  The Journal of the Acoustical Society of America,
  9615011514, 1994.
• Levy K.L. Kipke D.R. A computational model of
  the cochlear nucleus octopus cell. The Journal of
  the Acoustical Society of America, 102391402,
  1997.
• Rothman J.S. Young E.D. Manis P.B. Convergence
  of auditory nerve fibers onto bushy cells in the
  ventral cochlear nucleus Implications of a
  computational model. The Journal of
  Neurophysiology, 7025622583, 1993.
• Zhang X.Heinz M.G.Bruce I.C. Carney L.H. A
  phenomenological model for the responses of
  auditory-nerve fibers 1. nonlinear tuning with
  compression and suppression. The Journal of the
  Acoustical Society of America, 109648670, 2001.

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References

• Drawing/image/animation from "Promenade around
  the cochlea" ltwww.cochlea.orggt EDU website by R.
  Pujol et al., INSERM and University Montpellier
• 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
• Purves et al, Neuroscience 3rd edition
• P. O. James, An introduction to physiology of
  hearing 2nd edition
• Tremblay K., 1997 Central auditory system
  plasticity generalization to novel stimuli
  following listening training. J Acoust Soc Am.
  102(6)3762-73