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Segment I Past work: Highlights
Segment II Modeling the Auditory Pathway
Segment III future.cs@purdue: a personal view
Segment IV Q&A
Aditya P. MathurResearch, Education, Service, and Vision
CS Department Colloquium
March 26, 2007
2
Research: Empirical Studies
Saturation effect [Horgan.Mathur96]
FUNCTIONAL, DECISION, DATAFLOWAND MUTATION TESTING PROVIDETEST ADEQUACY CRITERIA.
Reliability
Testing EffortTrue reliability (R)Estimated reliability (R’)Saturation region
Mutation
Dataflow
Decision
Functional
RmRdfRdRf
R’f R’d R’df R’m
tfs tfe tds tde tdfs tdfe tms tfe
3
Research: Empirical Studies and Reliability
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]
4
Research: High Performance Testing
Testing on SIMD, Vector, MIMD architectures [joint with Choi,
Galiano, Krauser, Rego. 88--92]
5
Research: Feedback Control
Feedback control of software test processes [joint with Cangussu, DeCarlo, Miller. 00--06]
6
Education
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.
7
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--HP1000-- to C (PC)!)
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.
8
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
9
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
10
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.
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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
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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 is (ascending) auditory pathway?
Medial geniculate body
42,000
8,800
392,000
100,000,000
570,000
Pitch discrimination (VCN)
Transport frequency, intensityInformation; rate encoding/temporal encoding
Azimuth, integration from both ears;ITD and ILD computation
Range,timing, intervals
Sensory integration(e.g. head movement)
Comparison across sounds
Input for sound localization
Onset neurons
Gateway for AC
Spatial map?, Spectral analysis
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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?
Slow AC response
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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/
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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.
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FFR for Musicians and Non-musicians
Source: Wong, Skoe, Russo, Dees, Kraus; Nature Neuroscience, 2007.
Stimulus: /mi1/, /mi2/, /mi3/ [Mandarin]F0: Stimulus fundamental frequency
Observation: Musicians showed more faithful representation of the F0 contour than non-musicians.
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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.
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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.
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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?
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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
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Our approach
P-model P-model P-model…….
Equations
Anatomy
Assumptions
Simulation
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Progress
Medial Superior Olive
Lateral Superior Olive
Medial Nucleus of the
Trapezoid Body
INFERIOR COLLICULUS Not Implemented
Not Implemented
SUPERIOR OLIVARY COMPLEX
COCHLEAR NUCLEUS
Pyramidal Cell
Stellate Cell
Inter-Neurons
Bushy Cell
Octopus Cell
Fusiform Cell
Not Implemented
Implemented
AN Fibres [Zhang et al.] HRTF [Lookup table/person]
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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.
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Bushy Cell Model [Rothman ‘93]
Slow low threshold potassium conductance
Some constants associated with Bushy cell:
Fast high threshold potassium conductance
Passive leakage conductance
Inhibitory synaptic conductance
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Bushy Cell Model
• The cell potential (V) is given by:
Where
Reverse potentials for corresponding ions
Leakage conductance
Membrane capacitance
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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:
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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:
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Bushy Cell Model
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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 N=1 and A= 9.1, while the output obtained by Rothman et. al. is shown in D for same parameter.
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Next Step
• Implement the IID 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
Zhang et al.
Rothman et al.
Spirou et al.
Constant delay
H&H
Carney et al.
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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
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Next Step
• Implement the dorsal cochlear nucleus neurons and
find out the correlation between vertical angle and
neural output in DCN region
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Model Validation
• Interconnected P-models
• Functional– Sound localization; in collaboration with Professor Sumit Dhar,
Northwestern
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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=
( ) ( ) ( ) ( )tIVVgVVgVVgdt
dVC extLLNaNaKK +−+−+−=
m, n and h depend on V
Hodgkin Huxley Model
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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
36
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. …..
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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.
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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.
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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!
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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.
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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.
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Other programs/staff
• Outreach programs
• All staff
• Facilities
• Corporate Partners Program
• Development
Goal: Maintain excellence where it exists.