Download Talk3.26.07V02 - Purdue University

Aditya P. Mathur
Research, Education, Service, and Vision
CS Department Colloquium
March 26, 2007
Segment I
Past work: Highlights
Segment II
Modeling the Auditory Pathway
Segment III
future.cs@purdue: a personal view
Segment IV
Q&A
1
Research: Empirical Studies
 Saturation effect [Horgan.Mathur96]
R’d
R’f
Reliability
Rm
Rdf
Rd
Rf
R’m
R’df
Mutation
Dataflow
Decision
Functional
t fs
t fe
tds tde
tdfs tdfe
tms tfe
Testing Effort
True reliability (R)
Estimated reliability (R’)
Saturation region
FUNCTIONAL, DECISION, DATAFLOW
AND MUTATION TESTING PROVIDE
TEST ADEQUACY CRITERIA.
2
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]
3
Research: High Performance Testing
 Testing on SIMD, Vector, MIMD architectures [joint with
Choi, Galiano, Krauser, Rego. 88--92]
4
Research: Feedback Control

Feedback control of software test processes [joint with
Cangussu, DeCarlo, Miller. 00--06]
5
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.
6
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)!)

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.
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]
7
Aditya P. Mathur
CS Department Colloquium
March 26, 2007
Segment I
Past work: impact
Segment II
Modeling the Auditory Pathway
Segment III
future.cs@purdue: a personal view
Segment IV
Q&A
8
Modeling the Auditory Pathway
Sponsor: National Science Foundation
Principle Investigator
Graduate Student
Aditya Mathur
Alok Bakshi, Industrial Engineering
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
9
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.
10
Trail
Progress so far and the future
Quic kTime™ and a
TIFF (Unc ompres sed) decompress or
are needed to
see this picture.
Existing
modeling
approaches versus our approach
BAEP and children with learning disabilities
What is Brainstem Auditory Evoked Potential (BAEP)?
What is auditory pathway?
11
What is (ascending) auditory pathway?
100,000,000
Comparison across sounds
570,000
Medial geniculate body
Gateway for AC
Sensory integration 392,000
(e.g. head movement)
Pitch discrimination (VCN)
Input for sound localization
Onset neurons
42,000
Range,timing, intervals
Spatial map?, Spectral analysis
8,800
Azimuth, integration from both ears;
ITD and ILD computation
Transport frequency, intensity
Information; rate encoding/temporal
12
http://www.iurc.montp.inserm.fr/cric/audition/english/audiometry/ex_ptw/voies_potentiel.jpg
encoding
http://www.iurc.montp.inserm.fr/cric/audition/english/audiometry/ex_ptw/e_pea2_ok.gif
What is Brainstem Auditory Evoked Potential (BAEP)?
Q: What is the effect of learning disability on ABR?
ABR [1.5-15ms]: Brainstem
Slow AC response
MLR [25-50ms]: Upper brainstem and/or Auditory Cortex
ABR: Auditory Brainstem Response
MLR: Middle Latency Response
Source: http://www.audiospeech.ubc.ca/haplab/aep.htm
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BAEP for normal and language impaired children
6.2ms
7.2ms
Stimulus: Synthesized /da/
V: lateral lemniscal input to
inferior colliculus
Vn: dendritic processing in the
inferior colliculus
Normal children
Language impaired children
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.
Source: Wible, Nicol, Kraus; Brain 2005.
14
BAEP for normal and language impaired children
Onset and formant structure of speech sounds in children
Stimulus: Train of /da/
FFR: Frequency Following Response
FFR
Normal children
Language impaired children
Observation: Mean V-Vn slope was smaller
for children with language-based learning
problems.
Source: Wible, Nicol, Kraus; Biological Psychology, 2004.
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FFR for Musicians and Non-musicians
Stimulus: /mi1/, /mi2/, /mi3/ [Mandarin]
F0: Stimulus fundamental frequency
Observation: Musicians showed more faithful representation of the
F0 contour than non-musicians.
Source: Wong, Skoe, Russo, Dees, Kraus; Nature Neuroscience, 2007.
16
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.
17
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.
18
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?
19
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
20
Our approach
Simulation
P-model P-model
…….
P-model
Anatomy
Equations
Assumptions
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Progress
Not
Implemented
INFERIOR COLLICULUS
SUPERIOR OLIVARY COMPLEX
Lateral Superior
Olive
Medial
Superior Olive
Medial Nucleus
of the
Trapezoid Body
Not
Implemented
COCHLEAR NUCLEUS
Pyramidal
Cell
Stellate
Cell
Not
Implemented
Octopus
Cell
Implemented
Inter-Neurons
Bushy
Cell
AN Fibres [Zhang et al.]
Fusiform
Cell
HRTF [Lookup table/person]
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Bushy Cell (in Anteroventral Cochlear Nucleus)
Preserves timing information
for the computation of ITD.
AN spikes
Bushy Cell
Time
Bushy Cell
spikes
Receives excitatory input from 120 AN fibers in the same
frequency range
Latent period
Time
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Bushy Cell Model [Rothman ‘93]
Some constants associated with Bushy cell:
Slow low threshold potassium conductance
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
Membrane capacitance
Leakage conductance
25
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:
26
Bushy Cell Model
Here
Here
themselves depend on voltage of soma V
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:
27
Bushy Cell Model
28
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.
29
Next Step
• Implement the IID circuit and find out the correlation between
neural output and sound source (azimuth angle)
Carney et al.
H&H
LSO
Cochlear Nucleus
Rothman et al.
SBC
GBC
LSO
Constant delay
MNTB
MNTB
Cochlear Nucleus
GBC
SBC
Spirou et al.
Zhang et al.
Cochlea
Cochlea
30
Next Step
• Implement the ITD circuit and find out the correlation between
neural output and sound source (azimuth angle)
MSO
MSO
LNTB
LNTB
Cochlear Nucleus
SBC
GBC
Cochlea
Cochlear Nucleus
MNTB
MNTB
GBC
SBC
Cochlea
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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
32
Model Validation
• Interconnected P-models
• Functional
– Sound localization; in collaboration with Professor Sumit Dhar,
Northwestern
33
Hodgkin Huxley Model
Outside
Iext
IK
gK
VK
INa
IL
gNa
gL
VNa
C
VL
K+ ion channel
Inside
( At potential V )
C
dV
 g K VK  V   g Na VNa  V   g L VL  V   I ext t 
dt
g K  g K n4
g Na  g Na m 3 h
m, n and h depend on V
34
http://personal.tmlp.com/Jimr57/textbook/chapter3/images/pro5.gif
Aditya P. Mathur
CS Department Colloquium
March 26, 2007
Segment I
Past work: impact
Segment II
Modeling the Auditory Pathway
Segment III
future.cs@purdue: a personal view
Segment IV
Q&A
35
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. …..
36
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.
37
Goals
1. Offer a broader set of options to our undergraduate
students.
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.
38
Aditya P. Mathur
CS Department Colloquium
March 26, 2007
Segment I
Past work: impact
Segment II
Modeling the Auditory Pathway
Segment III
future.cs@purdue: a personal view
Segment IV
Q&A
Thanks!
39
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.
40
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.
41
Other programs/staff
• Outreach programs
• All staff
• Facilities
• Corporate Partners Program
• Development
Goal: Maintain excellence where it exists.
42