1

Segment I Past work: impact

Segment II Modeling the Auditory Pathway

Segment III future.cs@purdue: a personal view

Segment IV Q&A

Aditya P. MathurCS Department Colloquium

March 26, 2007

2

Research: Impact Coverage principle and the saturation effect

[Horgan.Mathur96] Microsoft quality gate criteria. Pioneered by Praerit Garg [MS’95] Guidant test quality assessment for medical devices

[recommendation accepted; yet to be implemented]

Software reliability estimation [Chen.Mathur.Rego 95; Krishnamurthy.Mathur 97]

Led to new approaches to software reliability modeling. [Gokhale.Trivedi 98; Singpurwalla.Wilson 99; Goševa-Popstojanova.Trivedi 01; Yacoub et al. 99; Cortellessa et al. 02; Mao.Deng 04]

3

Research firsts with ~No impact (so far!)

Testing on SIMD, Vector, MIMD architectures [joint with Choi, Galiano, Krauser, Rego. 88--92]

Feedback control of software test processes [joint with Cangussu, DeCarlo, Miller. 00--06]

LSL: A language for the specification of program auralization [Boardman.Mathur 94, 94-04]

4

Education: Impact Introduction to Microprocessors [80, 85, 89]

Drove curricula in almost every engineering college in India (including all the IITs).

Continues to be recommended mostly as a reference text in many Indian universities.

Over 100,000 students benefited from this book.

Foundations of Software Testing, Vol 1 [07], Vol 2 [08]

First comprehensive (text) book to present software testing and reliability as an integrated discipline with algorithms for test generation, assessment, and enhancement. Is driving testing curricula in CS/ECE departments.

5

Service: Impact Educational Information Processing System [BITS, Pilani 85]

Led a team of four faculty to design, develop, and deploy from scratch. In use even now(‘06) (code changed from Fortran IV to C!)

Purdue University Research Expertise (PURE) database [06]

Original idea: Dean Vitter. My contribution: Requirements analysis, design, testing, and management; interaction with all 10 colleges.

Over 85% of Purdue (WL) faculty in PURE. Expansion planned to other state universities; enhancement of feature set [with Luo Si]

Software Engineering Research Center (SERC) [94-00]

Started by Conte/Demillo ‘86-87. Led SERC recovery from six industrial members to 13 and

from two university members to four. Over $1.5 Million in research funds awarded to faculty.

6

Segment I Past work: impact

Segment II Modeling the Auditory Pathway

Segment III future.cs@purdue: a personal view

Segment IV Q&A

Aditya P. MathurCS Department Colloquium

March 26, 2007

7

Modeling the Auditory Pathway

Principle Investigator

Aditya Mathur

Graduate Student

Alok Bakshi, Industrial Engineering

Sponsor: National Science Foundation

Collaborators:

Nina Kraus: Hugh Knowles Professor

Sumit Dhar: Assistant Professor,

Department of Neurobiology and Physiology, Northwestern

Michael Heinz: Assistant Professor,

Speech, Language, and Hearing Sciences and Biomedical Engineering, Purdue

8

Objective

To construct and validate a model of the auditory

pathway that enables us to understand the impact of defects and auditory plasticity along the pathway in children with learning disabilities.

9

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

What is Brainstem Auditory Evoked Potential (BAEP)?

BAEP and children with learning disabilities

Existing modeling approaches versus our approach

Progress so far and the future

What is auditory pathway?

Trail

10http://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

Pitch discrimination (VCN)

Transport frequency, intensityInformation; rate encoding/temporal encoding

Azimuth, integration from both ears;ITD and ILD computation

Range,timing, intervals

Spatial map?, Spectral analysis

Sensory integration(e.g. head movement)

Comparison across sounds

What is (ascending) auditory pathway?

Medial geniculate body

Input for sound localization

Onset neurons

Gateway for AC

11

What is Brainstem Auditory Evoked Potential (BAEP)?

ABR [1.5-15ms]: Brainstem

MLR [25-50ms]: Upper brainstem and/or Auditory Cortex

ABR: Auditory Brainstem ResponseMLR: Middle Latency Response

Source: http://www.audiospeech.ubc.ca/haplab/aep.htm

Q: What is the effect of learning disability on ABR?

12

BAEP for normal and language impaired children

Normal children

Language impaired children

Source: Wible, Nicol, Kraus; Brain 2005.

6.2ms 7.2ms

V: lateral lemniscal input to inferior colliculus

Vn: dendritic processing in the inferior colliculus

Observation: Duration of V-Vn found to be more prolonged for children with learning problems than for normal children. Notice also the difference in the slope of V-Vn.

Stimulus: Synthesized /da/

13

BAEP for normal and language impaired children

Onset and formant structure of speech sounds in children

Normal children

Language impaired children

Source: Wible, Nicol, Kraus; Biological Psychology, 2004.

Stimulus: Train of /da/

FFR

FFR: Frequency Following Response

Observation: Mean V-Vn slope was smaller for children with language-based learning problems.

14

FFR for Musicians and Non-musicians

Source: Wong, Skoe, Russo, Dees, Kraus; Nature Neuroscience, 2007.

Stimulus: /mi1/, /mi2/, /mi3/F0: Stimulus fundamental frequency

Observation: Musicians showed more faithful representation of the f0 contour than non-musicians.

15

Importance of the BAEP

Neural activity in the auditory pathway, measured via the BAEP, seems to be a strong indicator of learning disabilities in children.

Auditory pathway is “tuned” by tonal experience.

16

Why model the auditory pathway?

BAEP is an external measurement (black box) of an internal activity.

Direct observation of internal activity is almost impossible in humans.

A validated model will allow direct observation of (simulated) internal activity and offer insights into the relationship between such activity and the BAEP.

This might lead to better diagnosis. Several other advantages too.

17

Research questions

How can neuro-computational models be used to encode, and mimic, the auditory neural behavior exhibited by children with learning disabilities?

How can such models be used to accurately predict the impact of treatments for learning impairments?

18

Existing approaches

Connectionist models: Surface and deep dyslexia: Hinton.Shallice’91, Plaut.Shallice’93 Spatial firing patterns: Nomoto’79

Phenomenological models [P-models]: Sound localization: Neti.Young.Schneider’93 Response to amplitude modulated tones: Nelson.Carney’04 Cochlear model: Kates’93 Speech recognition: Lee.Kim.Wong.Park’03

Simulation models: External ear to cochlear nucleus: Guérin.Bès.Jeannès.’03

19

Our approach

Simulation, system of systems, holistic, approach. Detailed, cellular. Explicit modeling of inherent anatomical and physiological

parallelism. Functionality used primarily for validation of the simulation

20

Our approach

P-model P-model P-model…….

Equations

Anatomy

Assumptions

Simulation

21

Progress

Auditory Nerve fiber model by Zhang et. al.

•Octopus Cell model by Levy et. al.

•Models of other cells being implemented

22

Bushy Cell (in Anteroventral Cochlear Nucleus)

Bushy Cell

Receives excitatory input from 1-20 AN fibers in the same frequency range

AN spikes

Time

Bushy Cell spikes

TimeLatent period

Preserves timing information for the computation of ITD.

23

Bushy Cell Model

Model [Rothman’93, Spirou’05]

• Has no dendrites and axon

• The soma is equipotential

• Receives 1-20 AN fibers with different characteristic frequency

Soma

24

K+ ion channel

http://personal.tmlp.com/Jimr57/textbook/chapter3/images/pro5.gif

Outside

Iext

IK INa IL

gK gNa gL

VK VNa VL

C

Inside

( At potential V )4ngg KK

hmgg NaNa3 tIVVgVVgVVg

dt

dVC extLLNaNaKK

m, n and h depend on V

Hodgkin Huxley Model

25

Segment I Past work: impact

Segment II Modeling the Auditory Pathway

Segment III future.cs@purdue: a personal view

Segment IV Q&A

Aditya P. MathurCS Department Colloquium

March 26, 2007

26

Vision as in the Strategic Plan [2003]

The faculty will be preeminent in creating and disseminating new knowledge on computing and communication. The department will prepare students to be leaders in computer science and its applications. Multidisciplinary activities that strengthen the impact of computation in other disciplines will play an essential role. …..

27

Vision as in the Strategic Plan [2003]

The department will be known for: Faculty who are recognized worldwide as leaders. They will set and

implement the national agenda for discovery and education in computer science.

A superior and diverse student body learning the values, vision, knowledge, and skills of computer science.

Graduates who go on to be faculty at highly ranked departments, researchers at internationally recognized labs, and leaders and innovators in industry and government.

Involvement and leadership in university institutes and centers that foster multidisciplinary research.

Collaboration with public and private enterprises in Indiana, the nation, and

the world.

28

Goals

2. Strengthen interdisciplinary research and educational programs.

3. Improve upon the existing research environment for faculty and students, in particular for tenure-track assistant professors.

4. Meet our implicit obligations to the state and the nation, in particular to our customers.

5. Maintain excellence where it already exists.

1. Offer a broader set of options to our undergraduate students.

29

Undergraduate Education

Tackle the declining enrollment problem: Revisit the undergraduate curriculum: should we change the

core? Should we offer alternate cores for different specializations? Create specializations: such as SE, Visualization, Security. Offer scoping into the MS program.

CPC sponsored undergraduate research projects. Some may lead to MS thesis.

Consider formalizing advisory role for the CPC in undergraduate curriculum design.

Strengthen the CS study abroad program.

Goal: Offer a broader set of options to our undergraduate students. Meet our implicit obligations to our customers.

30

Graduate Education

Enrollment Admissions MS and PhD programs. Interdisciplinary programs

Goal: Meet our implicit obligations to the state and the nation.

31

Faculty: Hiring

Look to the future of CS. Continue support for research in core areas but aim to

establish collaborative groups that are radically different in their perspective and aspirations.

Consider CS as a discipline essential to finding solutions to problems of key significance to humans: cancer and other diseases, large scale information processing, finance, health care, etc.

Aim at creating strengths in new and challenging areas while retaining current strength in core areas.

Goal: Strengthen interdisciplinary research and educational programs.

32

Faculty: Tenure

Reduce the uncertainty for an Assistant Professor. Focus (primarily) on scholarship; identify quantitative and

qualitative indicators of scholarship. Consider “quality” as a multi-dimensional attribute.

Identify and communicate ways of measuring impact/potential impact.

Create a “Tenure card” that aids in (accurate) self assessment.

Strengthen the third year review process.

Goal: Improve upon the existing research environment for faculty and students, in particular for tenure-track assistant professors.

33

Other programs/staff

Outreach programs All staff Facilities Corporate Partners Program Development

Goal: Maintain excellence where it exists.

34

Segment I Past work: impact

Segment II Modeling the Auditory Pathway

Segment III future.cs@purdue: a personal view

Segment IV Q&A

Aditya P. MathurCS Department Colloquium

March 26, 2007

Thanks!

35

Auditory Neuron Model

(Zhang et al., 2001)(Heinz et al., 2001)(Bruce et al., 2003)

36

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

37

Octopus Cell

Octopus Cell

Receives excitatory input from 60-120 AN

fibers

AN discharge

rate

Time

Octopus Cell

discharge

rate

TimeLatent period

38

Schematic of a typical Octopus Cell

http://www.ship.edu/~cgboeree/neuron.gif

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’

39

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

40

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

Soma Dendrite

41

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

42

Fusiform Cell

Fusiform Cell

Receives different inhibitory inputs from

DCN

AN discharge

rate

Time

Fusiform Cell

discharge

rate

TimeLatent period

43

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 inputElectrical model of fusiform

cell

44

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

45

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, 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, 96:1501–1514, 1994.

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, 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, 70:2562–2583, 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, 109:648–670, 2001.

46

References

• Drawing/image/animation from "Promenade around the cochlea" <www.cochlea.org> 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

47

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

48

Progress

Cochlea

UnknownConnection

KnownConnection

Nucleus Boundary

AN

Fib

ers

Cochlear Nucleus

49

Progress

Medial Superior Olive

Lateral Superior Olive

Medial Nucleus of the

Trapezoid Body

INFERIOR COLLICULUSNot

Implemented

Not Implemented

SUPERIOR OLIVARY COMPLEX

COCHLEAR NUCLEUS

Pyramidal Cell

Stellate Cell

Inter-Neurons

Bushy Cell

Octopus Cell

Fusiform Cell

Not Implemented

Implemented

50

Cochlear Nucleus

51

Bushy Cell

Slow low threshold potassium conductance

Some constants associated with Bushy cell:

Fast high threshold potassium conductance

Passive leakage conductance

Inhibitory synaptic conductance

52

Bushy Cell

The cell potential (V) is given by:

Where

Reverse potential for corresponding ions

Leakage potential

Membrane potential

53

Bushy Cell Model

Factor to scale rate constants to body temperature

General expression for scaling rate constants to temperature T

The three conductance mentioned earlier are given as:

54

Bushy Cell Model

Here themselves depend on voltage of soma V

Here denotes the arrival time for spike and synaptic

Conductance reaches its peak value of at time

Variation is given as:

Here and are given as:

55

Bushy Cell Model

56

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.

57

Next Step

Implement the ILD circuit and find out the correlation between neural output and sound source (azimuth angle)

Cochlea

Cochlear Nucleus

SBC GBC MNTB MNTB

LSO

Cochlea

Cochlear Nucleus

GBC SBC

LSO

58

Next Step

Implement the ITD circuit and find out the correlation between neural output and sound source (azimuth angle)

Cochlea

Cochlear Nucleus

SBC GBC MNTB MNTB

MSO

Cochlea

Cochlear Nucleus

GBC SBC

MSO

LNTBLNTB

59

Next Step

Implement the dorsal cochlear nucleus neurons and find out the correlation between vertical angle and neural output in DCN region