1 Modeling the Auditory Pathway Research Advisor: Aditya Mathur School of Industrial Engineering Department of Computer Science Purdue University Graduate Student: Alok Bakshi SERC Showcase, Ball Sate University, November 15-16, 2006 Sponsor: National Science Foundation

2 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.

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

6 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

7 Brainstem Evoked Auditory Potential Normal children Language impaired children

8 Auditory Pathway Modeling Auditory Nerve fiber model by Zhang et. al. Octopus Cell model by Levy et. al. Models of other cells being implemented

9 K + ion channel Outside I ext IKIK I Na ILIL gKgK g Na gLgL VKVK V Na VLVL C Inside ( At potential V ) m, n and h depend on V Hodgkin Huxley Model

10 Hodgkin Huxley Model (contd.)

11 Auditory Neuron Model (Zhang et al., 2001) (Heinz et al., 2001) (Bruce et al., 2003)

12 Cochlear Nucleus Consist of 13 types of cells Single cell responses differ based on # of excitatory/inhibitory inputs Input waveform pattern Onset response Buildup response Input tone

13 Octopus Cell Receives excitatory input from AN fibers AN discharge rate Time Octopus Cell discharge rate Time Latent period

14 Schematic of a typical Octopus Cell Representative Cell Has four dendrites Receives 60 AN fibers with kHz CF Majority of input from high SA fibers, medium SA fibers denoted by superscript ‘m’

15 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

16 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 SomaDendrite

17 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

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

19 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

20 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(< 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 doesn’t reproduce all characteristics of globular bushy cell

21 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 N=1 and A= 9.1, while the output obtained by Rothman et. al. is shown in D for same parameter.

22 Bushy Cell Model - Output Similarly for N=5 and A=9.1, our implemented model’s 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 N=1, A=18.2 and the corresponding response of model by Rothman et. al. is shown in fig. F

23 Fusiform Cell Receives different inhibitory inputs from DCN AN discharge rate Time Fusiform Cell discharge rate Time Latent period

24 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 doesn’t take into account the Calcium conductance Doesn’t model the synaptic input Electrical model of fusiform cell

25 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 doesn’t model synapse and take direct current as the input instead Doesn’t rule out the possibility of inhibitory inputs as the reason for pauser and buildup response

26 Next Steps Verify the models of Pyramidal and Stellate cell in the cochlear nucleus. Identify structural connections of different types of cells in the cochlear nucleus. 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.

27 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, 30:578–587, 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, 96:1501– 1514, Levy K.L.; Kipke D.R. A computational model of the cochlear nucleus octopus cell. The Journal of the Acoustical Society of America, 102:391–402, 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, 70:2562–2583, 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, 109:648–670, 2001.

28 References Drawing/image/animation from "Promenade around the cochlea" 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 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):