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James W. Pennebaker, Chair The University of Texas at Austin, SEA 4.212, Austin, TX 78712 • (512) 475-7596

Jonathan William Pillow

Assistant Professor Ph.D., New York University

Jonathan William Pillow

Contact

Biography

Jonathan Pillow received a Ph.D. in Neural Science from New York University, and was a postdoctoral fellow at the Gatsby Computational Neuroscience Unit, UCL, before coming to the University of Texas. Jonathan's research interests lie at the intersection of computational neuroscience, machine learning, and human visual perception. His lab employs a variety of theoretical tools, in conjunction with psychophysical experiments, to study how neural populations represent and process information. He collaborates closely with labs devoted to neurophysiology and fMRI, applying Bayesian statistical methods to model the responses of neural populations in the visual pathway.

Current research topics include: neural decoding methods, population coding, human motion perception, theoretical models of adaptation, natural scene statistics, and unsupervised learning with spike trains.

Selected Publications (See lab site for full list of publications and PDF downloads)

Park I & Pillow JW (2011). Bayesian spike-triggered covarianceAdvances in Neural Information Processing Systems (NIPS) 24, eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F. & Weinberger, K., 1692-1700

Park M & Pillow JW (2011). Receptive field inference with localized priorsPLoS Computational Biology 7(10), 1-16.

Pillow JW, Ahmadian Y, & Paninski L (2011). Model-based decoding, information estimation, and change-point detection techniques for multi-neuron spike trainsNeural Computation 23:1-45.

Pillow, JW, Shlens, J, Paninski, L, Sher, A, Litke, AM, Chichilnisky, EJ, Simoncelli, EP. (2008) Spatio-temporal correlations and visual signaling in a complete neuronal populationNature 454: 995-999.

Pillow JW and Simoncelli EP. (2006). Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysisJournal of Vision, 6(4):414-428.

Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ. (2005). Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking ModelJournal of Neuroscience 25:11003-11013.

Pillow JW & Rubin N. (2002). Perceptual Completion across the Vertical Meridian and the Role of Early Visual Cortex.Neuron 33(5):805-13.

Interests

Neural coding, vision, mathematical modeling, and Bayesian statistics

PSY 323 • Perception

43660 • Fall 2014
Meets TTH 930am-1100am NOA 1.126
show description

Theory and research in the ways we extract information from the environment. Three lecture hourse a week for one semester. Prerequisite: For psychology majors, upper-division standing and Psychology 301 and 418 with a grade of at least C in each; for nonmajors, upper-division standing, Psychology 301 with a grade of at least C, and one of the following with a grade of at least C: BIO 318M, Civil Engineering 311S, Economics 329, Educational Psychology 371, Electrical Engineering 351K, Government 350K, Mathematics 316, 362K, Mechanical Engineering 335, Psychology 317, Sociology 317L, Social Work 318, Statistics 309, Statistics and Scientific Computation 302, 303, 304, 305, 306, 318.

PSY 394U • Seminar In Computatnl Neurosci

44330 • Spring 2014
Meets TTH 1100am-1230pm SEA 3.250
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Seminars in Cognitive or Perceptual Systems. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing and consent of instructor.

PSY 323 • Perception

43710 • Fall 2013
Meets TTH 930am-1100am NOA 1.126
show description

Theory and research in the ways we extract information from the environment. Three lecture hourse a week for one semester. Prerequisite: For psychology majors, upper-division standing and Psychology 301 and 418 with a grade of at least C in each; for nonmajors, upper-division standing, Psychology 301 with a grade of at least C, and one of the following with a grade of at least C: BIO 318M, Civil Engineering 311S, Economics 329, Educational Psychology 371, Electrical Engineering 351K, Government 350K, Mathematics 316, 362K, Mechanical Engineering 335, Psychology 317, Sociology 317L, Social Work 318, Statistics 309, Statistics and Scientific Computation 302, 303, 304, 305, 306, 318.

PSY 394U • Seminar In Computatnl Neurosci

43670 • Spring 2013
Meets W 900am-1200pm SEA 5.106
show description

Seminars in Cognitive or Perceptual Systems. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing and consent of instructor.

PSY 394U • Tpcs Statistics/Neural Coding

43680 • Spring 2013
Meets TTH 930am-1100am SEA 5.106
show description

Seminars in Cognitive or Perceptual Systems. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing and consent of instructor.

PSY 323 • Perception

43255 • Fall 2012
Meets TTH 1230pm-200pm NOA 1.126
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This course will provide an introduction to the scientific study of perception. We tend to think of perception as something that happens automatically whenever we open our eyes, ears, nose, etc., but perception is in fact an active process that relies an exquisitely sensitive receptor neurons working in concert with powerful computational machinery housed in the brain, spinal cord, and peripheral nervous system. Our perceptual faculties have been honed by evolution over many millions of years. A central focus of this course will be to examine how these sensory systems work and why. Though we tend not to realize it, we are all perceptual virtuosos; the average 5-year-old can easily out-perform the world's most powerful supercomputers at recognizing objects or comprehending speech.

This course will focus on the insights into sensory perception provided by a wide variety of disciplines (philosophy, physics, chemistry, biology, mathematics, computer science, neuroscience, and psychology), beginning with a study of the physical basis for perceptual information (e.g., light, sound waves, odors), and proceeding to the biological and psychological processes by which such information is converted to percepts in the brain.

PSY 323 • Perception

43145 • Fall 2011
Meets TTH 1230pm-200pm NOA 1.126
show description

This course will provide an introduction to the study of sensation and perception. Perception is the active process by which organisms extract information from their surroundings. Casually, we tend to think of this as an "automatic" process, e.g.: "You just look at something and you can see what it is, where it is, how it's moving, etc." However, what seems "easy" to us is in fact the result of an exquisitely sensitive sensory system working in concert with powerful computational machinery housed in the brain, spinal cord, and peripheral nervous system. Our perceptual faculties have been honed by evolution over many millions of years. A central focus of this course will be to examine how these sensory systems work and why. Though we tend not to realize it, we are all perceptual virtuosos; the average 5-year-old can easily out-perform the world's most powerful supercomputers at recognizing objects or comprehending speech.

This course will focus on the insights into sensory perception provided by a wide variety of disciplines (philosophy, physics, chemistry, biology, mathematics, computer science, statistics, neuroscience, and psychology), beginning with a study of the physical substrates for perceptual information (e.g., light, sound waves, temporature, odors), and proceeding to the biological and psychological processes by which such information is converted to "percepts" in the brain.

PSY 394U • Meths In Computatnal Neurosci

44030 • Spring 2011
Meets F 900am-1200pm SEA 2.224
show description

Seminars in Cognitive and Perceptual Systems. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing and consent of instructor.

PSY 323 • Perception

43100 • Fall 2010
Meets TTH 1230pm-200pm NOA 1.126
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Course Description

Perception is the active process by which organisms extract information from their environments. Informally, the study of perception represents our best scientific efforts to address the question: “Why does the world look the way it does”? Perception depends critically on the design of our sensory organs and the neural pathways that process sensory information. This course will provide an introduction to the scientific study of perception, focusing on the sensory modalities (sight, hearing, touch, smell, taste), the neurobiological underpinnings of these senses, and the computational strategies used by the brain to interpret the sensory world.

Texts

Wolfe, Kluender & Levi, Sensation & Perception (2nd Ed.) Sinauer Associates. (ISBN-13: 978-0878939534)

PSY 394U • Meths In Computatnal Neurosci

44190 • Spring 2010
Meets TTH 200pm-330pm SEA 2.224
(also listed as NEU 394P )
show description

Seminars in Cognitive and Perceptual Systems. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing and consent of instructor.

PSY 323 • Perception

44065 • Fall 2009
Meets TTH 200pm-330pm NOA 1.124
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Perception  (PSY 323)
Fall 2009
Instructor:  Jonathan Pillow, Assistant Professor Time: T, TH 2-3:30 pm
Office:  SEA 4.104 Location: NOA 1.124
Office hours: Tue 3:30-5p, Fri 1:30-3p (and by appointment)
e-mail: pillow@mail.utexas.edu
Teaching Assistant: Brian Sullivan
Office: SEA 4.128G
Office hours: Tues 12:30-2, Wed 1-2:30p (and by appointment)
email: brians@mail.utexas.edu
course website: http://homepage.psy.utexas.edu/homepage/faculty/pillow/courses/perception09/
Course Description
This course will provide students with an introduction to the study of sensation and perception.
Perception is the active process by which organisms extract information from their surroundings.  
Casually, we tend to think of perception as an "automatic" process, e.g.: "You just look at
something and you can see what it is, where it is, how it's moving, etc."  However, what seems
"easy" to us is in fact the result of an exquisitely sensitive sensory system working in concert
with powerful computational machinery housed in the brain, spinal cord, and peripheral nervous
system.  Our perceptual faculties have been honed by evolution over many millions of years.  A
central focus of this course will be to examine how these sensory systems work and why they are
so impressive.  Though we tend not to realize it, we are all perceptual virtuosos; the average 5-
year-old can easily out-perform the world's most powerful supercomputers at recognizing faces
or comprehending speech.
The study of perception has a long history, spanning the disciplines of philosophy, physics,
chemistry, biology, neuroscience, mathematics, computer science, statistics, and psychology.  We
will conduct a survey of results and insights provided by a wide variety of these disciplines.  
Students will be expected to have a solid grounding in the basic sciences and good intuition for
elementary mathematical ideas.  We will study the physical media (e.g., light, sound waves) that
underlie sensation, and the biological and psychological processes by which the information
provided by these media are converted into "percepts".  We will study vision, audition,
somatosensation (touch), olfaction (smell), taste, and a smattering of other sensory systems.  
(Whoever said there were only five senses!).  At least half of the course focuses on vision,
reflecting the fact that it is the longest-studied and (arguably) best-understood sensory modality.
Perceptual science is still very much in its infancy; there are vast tracts of unexplored territory.  
For many perceptual phenomena, we have only rudimentary sketches of the underlying
mechanisms, but we will explore open problems and active areas of research.
Textbook
Sensation & Perception, 2nd ed.  Wolfe, J.M., Kluender, K.R., Levi, D.M., Bartoshuk, L.M.,
Herz, R.S., Klatzky, R.L., Lederman, S.J., and Merfeld, D. M..  Sinauer Associates, 2009.
Course requirements and grades
Regular Exams
(A) Three exams and one comprehensive final will be given. Final grades will be based on the
highest three of the four grades. Thus, if you are happy with your grade after the first three
exams, there is no need to take the final. Early, late or make-up exams will not be given without
an extremely compelling excuse that is fully documented in writing. In most cases, we will end
up agreeing that the (otherwise optional) final should serve as the make-up exam.
Material from lectures and from the assigned readings will be covered in the exams and final.
Exams are multiple choice.
(B) This course satisfies a Science requirement for many students. If you want to do well on
these exams you will have to work hard in this course. It will require significant investments of
time and effort. You will need to know all of the material presented in the textbook (the assigned
readings), and all of the material presented in the lectures. A cursory review of this material a
few days before the exam will most likely not suffice for a passing grade. This is a Science
course and the material is difficult. Please do not expect easy exams.
(C)You will not be permitted to wear hats with a brim during the examinations. Be sure to bring
your UT Identification Card with you to the exams and be prepared to show this card. Bring
your own pencil; we will bring the scantrons.
Optional-Final Exam
The final exam is optional, not required. If you take all three regular exams and are content with
your grade (based upon these three exams), then there is no need to take the final exam; your
semester is over! If, however, you are not content with your grade and you would like to attempt
to raise it, you will be allowed the opportunity to take a comprehensive final examination
(covering all of the readings and the lectures). This exam is essentially a long version of the
regular exams, but is comprehensive. For many students, this is a golden opportunity to
demonstrate knowledge they have acquired. You will be given the full three hours to complete
the exam, if you feel you need this time. If you should elect to take this optional final
examination, then your final grade will be based upon the average of the highest three exam
scores. In other words, if you have taken all three regular exams and the final, then the low grade
will be dropped. The date, time, and place for this examination is determined by the University
and will be posted by the University. Note that this exam cannot be administered early, or late,
for any reason.
Final Grades
The final grades will be normalized (ie, "curved") to the highest student's total grade.    
Letter grade cutoffs are as follows: A=93-100, A-=90-92, B+=87-89, B=83-86, B-=80-82,          
C+=77-79, C=73-76, C-=70-72, D+=67-69, D=63-66, D-=60-62, F=below 60.
The total score will be rounded to the nearest decimal, e.g., 89.5% = 90%, 89.4% = 89%.   No
hand adjustment of these thresholds will be made once the curve is set by the highest score.
Exam review and grading disputes
The exams will be available for review in the TA's office during her or his office hours. If you
have a question about the grading or an exam item, please consult with the TA and then contact
Dr. Pillow if necessary.  Credit will be given for exam items that are clearly wrong or misleading,
but students bear the responsibility of understanding concepts and vocabulary as discussed in the
text and in class.
Other Information
Class Participation
Learning (like perception itself) is an active process. Students are strongly encouraged to attend
lectures and to ask questions.  (This will keep both students and instructor from falling asleep!)  
The goal of this course is not so much to convey a set of facts as to introduce a discipline and its
preferred methods of inquiry.  One of the goals of the lectures will be to interrogate the facts and
ideas presented in the textbook.  
Companion website
The textbook has a companion website with overviews, study aides, additional information, and
essays on select topics, as well as some nice demonstrations of perceptual illusions.  Please take
a look at this site; as it provides a nice supplement to some discussions contained in the book:  
http://www.sinauer.com/wolfe2e
Phones
Please silence your phone before class and then leave it in your pocket, backpack, or purse for
the duration of the class period.
Students with Disabilities
The University of Texas at Austin provides, upon request, appropriate academic accommodations
for qualified students with disabilities.  For more information, contact the Office of the Dean of
Students at 471-6259, 471-4641 TTY.
Pre-requisite information
Psychology Majors:
- PSYCHOLOGY 301 and 418  (with grade of C or better)
- plus upper-division standing
Non-Majors:
- PSYCHOLOGY 301 (with grade of C or better)
- At least one of the following (with grade of C or better):
 BIOLOGY 318M, CIVIL ENGINEERING 311S, ECONOMICS 329, EDUCATIONAL
 PSYCHOLOGY 371, GOVERNMENT 350K, MATHEMATICS 316, PSYCHOLOGY 317,
 SOCIOLOGY 317L, SOCIAL WORK 318, STATISTICS 309.  
Tentative Course schedule:
Week  Lecture Topic  Reading
1  Introduction / Overview Chap. 1
2  Intro: Philosophy & Basic Methods Chap. 1
  Light, Optics, & Early Vision Chap. 2
3   Retina & Receptive Fields Chap. 3
  Visual Cortex & Spatial Vision Chap. 3
4   Mid-level vision Chap. 4
   Object Recognition Chap. 4
5  Exam 1    (9/22)
 Color Vision I  Chap. 5
6 Color Vision II Chap. 5
 Space & Depth Perception Chap. 6
7 Bayesian Theories of Perception Chap. 6
 Motion Perception Chap. 7
8 Attention & Scene Perception I Chap. 8
 Attention & Scene Perception II Chap. 8
9 Exam 2.   (10/20)
 Intro to Sound & Hearing Chap. 9
10 Psychoacoustics Chap. 9
 Auditory System I Chap. 10
11 Auditory System II  Chap. 10
 Music  Chap. 11
12 Speech Perception Chap. 11
 Somatosensation I Chap. 12
13 Somatosensation II Chap. 12
 Olfaction   Chap. 13
14 Exam 3.   (11/26)
 --- Thanksgiving ---
15 Taste Chap 14
 Position Sense & the Vestibular System  Chap 15
TBA  Final Exam (cumulative)   

PSY 394U • Smnr In Cognition And Perceptn

43445 • Spring 2009
Meets W 1200-300pm SEA 5.128
show description

Seminars in Cognition and Perception. Three lecture hours a week for one semester. May be repeated for credit when the topics vary. Prerequisite: Graduate standing and consent of instructor.

Publications

Park, M. & Pillow, J.W. (2011). Receptive field inference with localized priors. PLoS Computational Biology (accepted) [abstract]

Histed MH & Pillow JW (2011). The 8th annual computational and systems neuroscience (Cosyne) meeting. Neural Systems & Circuits 1:8. (Invited meeting review)
download

Pillow, J.W., Ahmadian Y., & Paninski, L. (2011). Model-based decoding, information estimation, and change-point detection techniques for multi-neuron spike trains. Neural Computation 23:1-45. [abstract |
download

Ahmadian, Y., Pillow, J.W. & Paninski, L. (2011). Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Computation 23:46-96 [abstract]
download

Nirenberg, S., Bomash, I., Pillow, J.W. & Victor, J.D. (2010) Heterogeneous response dynamics in retinal ganglion cells: the interplay of predictive coding and adaptation. J Neurophysiol 103: 3184-3194. [abstract]
Link

Pillow, J.W. (2009). Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. Advances in Neural Information Processing Systems 22 eds. Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, A. Culotta. MIT Press. 1473-1481.  [abstract]
download

Berkes, P., Wood, F. & Pillow, J. (2009, September) Characterizing neural dependencies with copula models. Advances in Neural Information Processing Systems, 21, 129-136.
download

Pillow, J., Shlens, J., Paninski, L., Sher, A., Litke, A., Chichilnisky, E. & Simoncelli, E. (2008, September) Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature, 454, 995-999.
download

Pillow, J. & Latham, P. (2008, September) Neural characterization in partially observed populations of spiking neurons. Advances in Neural Information Processing Systems, 20.
download

Pillow, J. (2007) Likelihood-based modeling of neural responses. In K. Doya, S. Ishii, A. Pouget & R. Rao (Eds.), Bayesian Brain: Probabilistic Approaches to Neural Coding (pp.53-70). MIT Press.
download

Paninski, L., Pillow, J. & Lewi, J. (2007) Statistical models for neural encoding, decoding, and optimal stimulus design. In P. Cisek, T. Drew & J. Kalaska (Eds.), Computational Neuroscience: Theoretical Insights Into Brain Function. .
download

Pillow, J. & Simoncelli, E. (2006, September) Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis. Journal of Vision, 6(4), 414-428.
download

Schwartz, O., Pillow, J., Rust, N. & Simoncelli, E. (2006, September) Spike-triggered neural characterization. Journal of Vision, 6(4), 484-507.
download

Pillow, J., Paninski, L., Uzzell, V., Simoncelli, E. & Chichilnisky, E. (2005, September) Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model. J. Neurosci., 25, 11003-11013.

Simoncelli, E., Paninski, L., Pillow, J. & Schwartz, O. (2004) Characterization of neural responses with stochastic stimuli. In M. Gazzaniga (Ed.), The Cognitive Neurosciences, 3rd edition (pp.327-338). MIT Press.

Pillow, J., Paninski, L. & Simoncelli, E. (2004, September) Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural Computation, 16, 2533-2561.

Paninski, L., Pillow, J. & Simoncelli, E. (2004, September) Comparing integrate-and-fire-like models given intracellular and extracellular data. Neurocomputing, 65, 379-385.

Pillow, J., Paninski, L. & Simoncelli, E. (2004) Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. In S. Thrun, L. Saul & B. Scholkopf (Eds.), Advances in Neural Information Processing Systems. MIT Press.

Pillow, J.W. & Rubin N. (2002). Perceptual Completion across the Vertical Meridian and the Role of Early Visual Cortex. Neuron 33(5):805-13.
download

Courses

Semester        Course        Unique No.        Title

2014 Spr       NEU 394P       57790         Meths in Computational Neurosc

2014 Spr        Psy 394U       44330         Seminar In Computational
                                                                           Neurosc

2014 Spr       Psy 394U       44345          TPCS Statistics/Neural
                                                                          Coding 
2014 Spr       SSC 384        59765           TPCS Statistics/Neural
                                                                          Coding
2014 Spr       NEU 394P      57795          TPCS Statistics/Neural
                                                                          Coding

2013 Fall        Psy 323         43710           Perception 

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