Aditya P Mathur CS Department Colloquium March 26

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 1

Research: Impact n Coverage principle and the saturation effect [Horgan. Mathur 96] n n n 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] n 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] 2

Research firsts with ~No impact (so far!) n n n Testing on SIMD, Vector, MIMD architectures [joint with Choi, Galiano, Krauser, Rego. 88 --92] LSL: A language for the specification of program auralization [Boardman. Mathur 94, 94 -04] Feedback control of software test processes [joint with Cangussu, De. Carlo, Miller. 00 --06] 3

Education: Impact n Introduction to Microprocessors [80, 85, 89] n n 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] n 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. 4

Service: Impact n Educational Information Processing System [BITS, Pilani 85] n n Software Engineering Research Center (SERC) [94 -00] n n n 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!) 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] n n 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] 5

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 6

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 7

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. 8

Trail Progress so far and the future Existing modeling approaches versus our approach BAEP and children with learning disabilities What is Brainstem Auditory Evoked Potential (BAEP)? What is auditory pathway? 9

What is (ascending) auditory pathway? Comparison across sounds Medial geniculate body Gateway for AC Sensory integration (e. g. head movement) Pitch discrimination (VCN) Input for sound localization Onset neurons Transport frequency, intensity Information; rate encoding/temporal encoding Range, timing, intervals Spatial map? , Spectral analysis Azimuth, integration from both ears; ITD and ILD computation http: //www. iurc. montp. inserm. fr/cric/audition/english/audiometry/ex_ptw/voies_potentiel. jpg http: //www. iurc. montp. inserm. fr/cric/audition/english/audiometry/ex_ptw/e_pea 2_ok. gif 10

What is Brainstem Auditory Evoked Potential (BAEP)? ABR [1. 5 -15 ms]: Brainstem Q: What is the effect of learning disability on ABR? MLR [25 -50 ms]: Upper brainstem and/or Auditory Cortex ABR: Auditory Brainstem Response MLR: Middle Latency Response Source: http: //www. audiospeech. ubc. ca/haplab/aep. htm 11

BAEP for normal and language impaired children 6. 2 ms 7. 2 ms 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. 12

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. 13

FFR for Musicians and Non-musicians Stimulus: /mi 1/, /mi 2/, /mi 3/ F 0: Stimulus fundamental frequency Observation: Musicians showed more faithful representation of the f 0 contour than non-musicians. Source: Wong, Skoe, Russo, Dees, Kraus; Nature Neuroscience, 2007. 14

Importance of the BAEP n n 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. 15

Why model the auditory pathway? n n n 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. n This might lead to better diagnosis. n Several other advantages too. 16

Research questions n n 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? 17

Existing approaches n n n Connectionist models: n Surface and deep dyslexia: Hinton. Shallice’ 91, Plaut. Shallice’ 93 n Spatial firing patterns: Nomoto’ 79 Phenomenological models [P-models]: n Sound localization: Neti. Young. Schneider’ 93 n Response to amplitude modulated tones: Nelson. Carney’ 04 n Cochlear model: Kates’ 93 n Speech recognition: Lee. Kim. Wong. Park’ 03 Simulation models: n External ear to cochlear nucleus: Guérin. Bès. Jeannès. ’ 03 18

Our approach n Simulation, system of systems, holistic, approach. n Detailed, cellular. n Explicit modeling of inherent anatomical and physiological parallelism. n Functionality used primarily for validation of the simulation 19

Our approach Simulation P-model ……. P-model Anatomy Equations Assumptions 20

Progress • Octopus Cell model by Levy et. al. • Models of other cells being implemented Auditory Nerve fiber model by Zhang et. al. 21

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 22

Bushy Cell Model [Rothman’ 93, Spirou’ 05] • Has no dendrites and axon • The soma is equipotential • Receives 1 -20 AN fibers with different characteristic frequency Soma 23

Hodgkin Huxley Model Outside Iext IK g. K VK INa IL g. Na g. L VNa C VL K+ ion channel Inside ( At potential V ) m, n and h depend on V http: //personal. tmlp. com/Jimr 57/textbook/chapter 3/images/pro 5. gif 24

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 25

Vision as in the Strategic Plan [2003] n 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. …. . 26

Vision as in the Strategic Plan [2003] n The department will be known for: n n n 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. 27

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. 28

Undergraduate Education n Tackle the declining enrollment problem: n n Revisit the undergraduate curriculum: should we change the core? Should we offer alternate cores for different specializations? n Create specializations: such as SE, Visualization, Security. n 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. 29

Graduate Education n Enrollment n Admissions n MS and Ph. D programs. n Interdisciplinary programs Goal: Meet our implicit obligations to the state and the nation. 30

Faculty: Hiring n n 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. 31

Faculty: Tenure n n 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. n Identify and communicate ways of measuring impact/potential impact. n Create a “Tenure card” that aids in (accurate) self assessment. n Strengthen the third year review process. Goal: Improve upon the existing research environment for faculty and students, in particular for tenure-track assistant professors. 32

Other programs/staff n Outreach programs n All staff n Facilities n Corporate Partners Program n Development Goal: Maintain excellence where it exists. 33

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! 34

Auditory Neuron Model (Zhang et al. , 2001) (Heinz et al. , 2001) (Bruce et al. , 2003) 35

Cochlear Nucleus n n Consist of 13 types of cells Single cell responses differ based on n n # of excitatory/inhibitory inputs Input waveform pattern Input tone Onset response Buildup response 36

Octopus Cell AN discharge rate Octopus Cell Time Octopus Cell discharge Receives excitatory input from 60 -120 AN fibers rate Latent period Time 37

Schematic of a typical Octopus Cell Representative Cell • Has four dendrites • Receives 60 AN fibers with 1. 4 - 4 k. Hz CF • Majority of input from high SA fibers, medium SA fibers denoted by superscript ‘m’ http: //www. ship. edu/~cgboeree/neuron. gif 38

Octopus Cell Model Simplifications n Four dendrites replaced by a single cylinder n Active axon lumped into soma n Synaptic transmission delay taken as constant 0. 5 ms n Compartmental model employed with n 15 equal length dendritic compartments n 2 equal length somatic compartments 39

Octopus Cell Model Soma Dendrite 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 40

Octopus Cell - Output n n 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 41

Fusiform Cell AN discharge rate Fusiform Cell Receives different inhibitory inputs from DCN Time Fusiform Cell discharge rate Latent period Time 42

Fusiform Cell Model n n 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 43

Fusiform Cell Model Characteristics n n 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 44

References n n n 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. 45

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 3 rd edition P. O. James, An introduction to physiology of hearing 2 nd edition Tremblay K. , 1997 Central auditory system plasticity: generalization to novel stimuli following listening training. J Acoust Soc Am. 102(6): 3762 -73 46

Bushy Cell Model Characteristics n As the number and conductance of inputs is varied, the full range of response seen in VCN Bushy cell are reproduced n For inputs with low frequency(< 1 k. Hz), the model shows stronger phase locking than AN fibers, thus preserving the precise temporal information about the acoustic stimuli n The model simulates the spherical bushy cell, but doesn’t reproduce all characteristics of globular bushy cell 47

Progress AN Fibers Cochlear Nucleus Unknown Connection Nucleus Boundary Cochlea Known Connection 48

Progress INFERIOR COLLICULUS Not Implemented SUPERIOR OLIVARY COMPLEX Lateral 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 Fusiform Cell 49

Cochlear Nucleus 50

Bushy Cell n Some constants associated with Bushy cell: Slow threshold potassium conductance Fast high threshold potassium conductance Passive leakage conductance Inhibitory synaptic conductance 51

Bushy Cell n The cell potential (V) is given by: Where Reverse potential for corresponding ions Membrane potential Leakage potential 52

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: 53

Bushy Cell Model 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: 54

Bushy Cell Model 55

Bushy Cell Model - Output n n 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. 56

Next Step n Implement the ILD circuit and find out the correlation between neural output and source (azimuth angle) LSO Cochlear Nucleus SBC GBC Cochlea MNTB GBC SBC Cochlea 57

Next Step n Implement the ITD circuit and find out the correlation between neural output and source (azimuth angle) MSO LNTB Cochlear Nucleus SBC GBC Cochlear Nucleus MNTB GBC SBC Cochlea 58

Next Step n Implement the dorsal cochlear nucleus neurons and find out the correlation between vertical angle and neural output in DCN region 59