CURRICULUM VITAE Sue Becker - Faculty of Science - McMaster ...

CURRICULUM VITAE
Sue Becker
Department of Psychology, McMaster University
1280 Main Street West, Hamilton Ont. L8S 4K1
email: becker@mcmaster.ca
EDUCATIONAL BACKGROUND
B.A. (Honours) Queen’s University (Psychology) 1982
M.Sc. Queen’s University (Computing & Information Science) 1985
Ph.D. University of Toronto (Computer Science) 1992
AFFILIATIONS
Professor, Department of Psychology, McMaster University 2004-
Associate member, Department of Computing and Software, McMaster 1994-
Adjunct Member, Centre for Vision Research, York University 2001-
PROFESSIONAL ORGANIZATIONS
Cognitive Science Society 1993-
The Psychonomic Society, Inc. (Associate Member) 1995-2002
The Psychonomic Society, Inc. (Member) 2002-
International Neural Network Society 1993-1996
The Canadian Society for Brain, Behavior and Cognitive Science 1995-
European Neural Network Society 2001 -
Society for Neuroscience (Member) 2004-
EMPLOYMENT HISTORY
1992 Visiting Academic, Department of Psychology, University of Queensland,
Brisbane, Australia (June-July)
1992-93 Post-Doctoral Fellow, Rotman Research Institute, Baycrest Hospital
1993-98 Assistant Professor, Department of Psychology, McMaster University,
Hamilton , Ontario
1994- Associate Member, Department of Computing & Software,
McMaster University, Hamilton Ontario
1998-04 Associate Professor, Department of Psychology, McMaster University,
Hamilton , Ontario
1999-00 Honorary Senior Research Fellow,
Institute of Cognitive Neuroscience,
University College London

AREAS OF INTEREST
Computational neuroscience, cognitive modelling, cortical reorganization during perceptual development
and after neuronal injury including hearing loss and tinnitus, visual processing, unsupervised
learning, human memory, amnesia, priming, hippocampally-dependent memory, frontal control of
memory, spatial memory, spatial navigation, computational neuroscience, signal processing applications
of neural networks including image and speech compression, hearing compensation algorithms.
HONOURS
NSERC Women’s Faculty Award, covering 80% of salary 1993-1998
SUPERVISORSHIPS
Post-doctoral Fellows:
Fellow Year Project Current position
Steve Joordens 1996 Long-term semantic priming Assist.Prof.,U of T
Andrew Smith 2002- Models of dopamine and PDF
schizophrenia
Melissa Dominguez 2003- Models of cortical Research
reorganization in tinnitus Associate
Graduate Theses:
Student Year Program Thesis/Project Title
Ken Seergobin 1993- MSc Unsupervised learning: the impact of
1996 (Psych) temporal and spatial coherence on the
formation of visual representations
Chris Lesner 1996- MSc Multi-resolution mixtures of
1999 (C&IS) principal components
Arnold Liwanag 1996- MSc Learning with multiple
(Psych) co-operating agents
Jean Lim 1997- MSc Learning and consolidation
-2000 (Psych) in the hippocampus
Gaurav Patel 1997- MEng Modelling nonlinear dynamics with extended
2000 (ECE) Kalman filter trained recurrent multilayer
perceptrons (with Haykin & Racine)
Steve Howell 1998-04 PhD Developmental models of
(Psych) language learning
Damian Jancowicz 1998- PhD Modeling Semantic Category-Specific
(Psych) Deficits Using Topographic,
Corpus-Derived Representations
Chris Gilbert 2001- PhD Models and empirical studies of
(Psych) strategic memory use
Patrick Byrne 2002- PhD Models and empirical studies of
(Psych) Spatial cognition
Jeff Bondy 2003- PhD Intelligent hearing aids (with Haykin)
(ECE)
Undergraduate Theses:

Student Year Program Thesis/Project Title
Stephen Holik 1995-96 BSc (Honours A neural network model of
Psychology) episodic memory
Rick Taylor 1995-96 BSc (Honours Filtering and tuning by
Psychology) neocortex-thalamus complex
Ken Jobity 1995-96 BSc (Honours Neural networks for
Psychology) medical image compression
Sara Page 1996-97 BSc (Honours The impact of temporal coherence
Psychology) on the formation of visual
representations
Keisha Edwards, 1996-97 BSc (Computing The Web Test Generator
Savraj Malhotra & & Info Sciences) (4MP6/4ZP6 group project,
Rajesh Sookhoo jointly supervised by R. Racine)
Ted Meeds 1997-98 BSc Local cost functions in a cascading
(Neur. auto-encoder: A neural network model
Comp.) of the hippocampal memory system
Adeline Chin 1998-99 BSc (Honours Fast learning in the hippocampus
Neur.Comp.)
Marcia Cushing 1998-99 BSc (Honours An associative model of arithmetic
Psychology)
Pat O’Banion 2001- BSc (Honours Algorithms for intelligent hearing aids
Neur.Comp.
Andrew Chan 2002-03 BSc (Honours Models of dopaminergic modulation
Neur.Comp.
Psychology)
Jordan Hoath 2002-03 BSc (Honours Investigations of strategic memory
Psychology)
Cindy Narinesingh 2003- BSc (Honours Models of human memory
Neur.Comp.
Shilpy Gupta 2003-04 BSc (Honours Models of human memory
Neur.Comp.
Rohini Kalia 2004-05 BSc (Honours Models of contextual condidtioning
Psychology)
Lorie Giles 2004-05 BSc (Honours VR studies of spatial memory
Psychology)
Kianosh Keyvani 2004-05 BSc (Honours Models of spatial memeory
Psychology)
Josee Yang 2004-05 BSc (Honours Models of ECT, kindling, neurogenesis
Psychology)
HaeMin Han 2004-05 BSc (Honours Models of hippocampal-PFC gating
Psychology)

RESEARCH FUNDING
Note: MPCN = McDonnell-Pew Program in Cognitive Neuroscience.
1992-93 MPCN A Connectionist Account of the Implicit-Explicit Memory $127,169
Distinction (post-doctoral training grant to S. Becker)
1993-95 MPCN A Connectionist Account of the Implicit-Explicit Memory $39,600/yr
Distinction (research grant to S. Becker (P.I.),
M. Moscovitch & M. Behrmann)
1993 NSERC High-Speed Workstations (equipment grant to S. Becker) $25,643
1993-96 NSERC Neural Network Models of Human Learning $22,000/yr
& Memory (research grant to S. Becker)
1996-2000 NSERC Neural Network Models of Human Learning $25,000/yr
& Memory (research grant to S. Becker)
1996-99 NSERC The effects of altered connectivity on interneuronal
communication (Collaborative grant to R. Racine (PI), $86,000/yr
S. Becker, S. Haykin, & G. Gerstein)
2000-03 NSERC Intelligent hearing aid systems $165,400/yr
(Collaborative research opportunities grant to S. Haykin
(PI),S. Becker, L. Trainor, J.Platt & R.Racine)
2000-04 NSERC Neural network models of learning & memory $25,000/yr
(research grant to S. Becker)
2002-07 CIHR Understanding, treating and preventing tinnitus $229,200/yr
(New Emerging Teams grant to L. Roberts (PI),
S. Becker, J. Eggermont, C. Pantev, I. Bruce, L. Ward)
2002-07 CIHR The development of an integrated computational $242,000/yr
neuroscience to understand human mental function
(New Emerging Teams grant to A.R. McIntosh (PI),
S. Becker, S. Kapur, M. Henkelman, S. Graham,
P. Fletcher, R. Zemel, R. Dembo)
2002-05 OMHF Decoding Schizophrenia: putting the pieces together: $320,000/yr
Linking Genes, Neurochemistry, Cognition, Affect and
Neural Networks (Special initiatives grant to S. Kapur
(PI) S. Becker, P.Fletcher, P.Seeman and T.Tallerico
2004-07 CIHR Schizophrenia, reward learning and reward prediction
errors - A study using computational models and
event-related fMRI (Operating grant to S. Kapur (PI),
S. Becker, D. Mikulis, R. Zipursky) $108,619/yr

PUBLICATIONS
(i) Peer Reviewed Articles:
Under review:
1. Becker, S. (submitted) “A principle for hippocampal learning”. Submitted to Hippocampus.
2. Howell, S. R., Jankowicz, D., and Becker, S. (submitted), A Model of Grounded Language Acquisition:
Do Sensorimotor Features Improve Grammar Learning? Submitted to The Journal of Memory
and Language.
3. Howell, S. R. and Becker, S. (submitted), SRNEngine: A Windows-based neural network simulation
tool for the non-programmer. Submitted to Behavior Research Methods, Instruments, and Computers.
4. Howell, S. R., and Becker, S. (Submitted). Grammar from the lexicon: Evidence from neural network
simulations of language acquisition, submitted to Cognitive Science.
5. Jankowicz, D. and Becker, S. (Submitted), “Representation of conceptual knowledge: Modeling
category specific deficits using topographic, corpus-derived codes”.
In Press/Published:
1. Smith, A., Becker, S. and Kapur, S. (in press), A computational model of the selective role of the
striatal D2-receptor in the expression of previously acquired behaviours. Neural Computation.
2. Byrne, P. and Becker, S. (in press), Modelling mental navigation in scenes with multiple objects,
Neural Computation.
3. Trainor, L., Sonnadara, R., Wiklund, K., Bondy, J., Gupta, S., Becker, S., Bruce, I. and Haykin,
S. (2004). Development of a flexible, realistic hearing in noise test environment (R-HINT-E). Signal
Processing 84:299-309.
4. Smith, A., Li, M., Becker, S. and Kapur, S. (2004), A model of antipsychotic action in conditioned
avoidance: a computational approach. Neuropsychopharmacology 29(6):1040-9.
5. Haykin, S., Chen, Z. and Becker, S. (to appear), Correlation: Novel Basis for Statistical Learning
Algorithms. IEEE Transactions on Signal Processing.
6. Bondy, J., Becker, S., Bruce, I., Trainor, L. and Haykin. S. (2004), A novel signal processing strategy
for hearing aid design: NeuroCompensation. Signal Processing Volume: 84, Issue: 7, July, 2004, pp.
1239-1253
7. Bondy, J., Bruce, I., Becker, S. and Haykin, S. (2004). Predicting Speech Intelligibility from a
Population of Neurons. Advances in Neural Processing Systems 16, MIT Press.
8. Chen, Z., Haykin, S. and Becker, S. (to appear), Theory of Monte Carlo Sampling-Based Alopex
Algorithms For Neural Networks, Proceedings of the IEEE International Conference on Acoustics,
Speech and Signal Processing, 2004.

9. Becker, S. and Lim, J. (2003), “A computational model of prefrontal control in free recall: strategic
memory use in the California Verbal Learning Task”. Journal of Cognitive Neuroscience 15(6):1-12.
10. Becker, S. and Zemel, R.(2003), “Unsupervised learning with global objective functions, in The
Handbook of Brain Theory and Neural Networks, Second Edition, M. Arbib (ed), La Jolla, CA: MIT
Press.
11. Smith, A., Becker, S. and Kapur, S. (2003). From dopamine to psychosis: A computational approach.
Proceedings of the 7th International Conference on Knowledge-Based Intelligent Information & Engineering
Systems, St Anne’s College, University of Oxford, U.K.
12. Stoianov, I., Zorzi, M., Becker, S. and Umilta, C. (2002), “Associative arithmetic with Boltzmann
Machines: The role of number representations”. Proceedings of the International Conference on
Artificial Neural Networks.
13. Becker, S., Chan, A., Fletcher, P. and Kapur, S. (2002), A computational network model of the
neural circuits subserving motivated behaviours, Biological Psychiatry, 51:130S.
14. Chen, H., Yao, D., Becker, S., Zhuo, Y., Zeng, M. and Chen, L. (2002), A new method for fMRI
data processing: Neighborhood independent component correlation algorithm and its preliminary
application, Science in China, Series F, Volume 45, Number 5.
15. Burgess, N., Becker, S., King, J. and O’Keefe (2001), Memory for events and their spatial context:
models and experiments, Philosophical Transactions of the Royal Society of London B, 356:1493-1503.
Also appeared in A. Baddeley, M. Conway and J. Aggleton (editors), Episodic Memory: New Directions
in research, Oxford University Press, 2002.
16. Becker, S. and Burgess, N. (2001), Modelling spatial recall, mental imagery and neglect, Advances
in Neural Processing Systems 13:96-102, Todd Leen, Tom Dietterich, Volker Tresp (eds), MIT Press.
17. Howell, S. and Becker, S. (2001), “Modelling language acquisition: Grammar from the lexicon?”,
Proceedings of the cognitive science society, 2001.
18. Howell, S., Becker, S. and Jankowicz, D. (2001), “Modelling language acquisition: Lexical grounding
through perceptual features”, Proceedings of the 2001 Workshop on Developmental Embodied
Cognition (DECO-2001).
19. Yao, D., Chen, H., Becker, S., Zhou, T., Zhuo, Y. and Chen, L. (2001), “A fMRI data analysis
method using a fast infomax-based ICA algorithm”, IEEE Canadian Conference on Electrical and
Computer Engineering ( IEEE CCECE) 2001.
20. Stevens, C., Becker, S. and Trainor, L. (2000), “A Pitch in Time: An Artificial Neural Network Model
of Melodic Expectancy”, Cognitive Science in Australia, 2000: Proceedings of the Fifth Biennial
Conference of the Australasian Cognitive Science Society.
21. Christianson, G.B. and Becker, S. (1999), “A Model for Associative Multiplication”, Advances in
Neural Information Processing Systems 12.
22. Becker, S. (1999), “Implicit learning in 3D object recognition: The importance of temporal context”,
Neural Computation, 11(2):347-374.

23. Becker, S. and Hinton, G. E. (1999), “Learning Mixture Models of Spatial Coherence”, in Unsupervised
Learning, Foundations of Neural Computation, MIT Press.
24. Becker, S., Moscovitch, M., Behrmann, M. and Joordens, S. (1997), “Long-term semantic priming:
A computational account and empirical evidence”, Journal of Experimental Psychology: Learning,
Memory and Cognition, Vol. 24, No. 5, pp. 1059-1082.
25. Joordens, S. and Becker, S. (1997) “The long and short of semantic priming effects in lexical decision”,
Journal of Experimental Psychology: Learning, Memory and Cognition, Vol. 24, No. 5, pp. 1083-
1105.
26. Becker, S. (1997), “Learning temporally persistent hierarchical representations”. in Advances in
Neural Information Processing Systems 9, M. Mozer, M. Jordan and T. Petsche (eds), MIT Press,
pages 824-830.
27. Liwanag, A. and Becker, S. (1997), “Improving Associative Memory Capacity: One-Shot Learning
in Multilayer Hopfield Networks”. Proceedings the 19th Annual Conference of the Cognitive Science
Society, pages 442-447.
28. Becker, S. and Plumbley, M. (1996), “Unsupervised neural network learning procedures for feature
extraction and classification, ” in Applied Intelligence, special issue on neural networks, Vol. 6, No.
3, pp. 185-205.
29. Becker, S. (1996), “Mutual Information Maximization: Models of Cortical Self-Organization”. Network:
Computation in Neural Systems, 7:7-31.
30. Becker, S. (1995), “Unsupervised learning with global objective functions, in The Handbook of Brain
Theory and Neural Networks, M. Arbib (ed), La Jolla, CA: MIT Press.
31. Becker, S. (1995), “JPMAX: Learning to recognize moving objects as a model-fitting problem”, in
Advances in Neural Information Processing Systems 7, Tesauro, G., Touretzky, D. and Leen, T. (eds),
MIT Press, pages 933-940.
32. Chapman, C.A. and Becker, S. (1994), “Model synapses with frequency potentiation characteristics
can cooperatively enhance Hebbian learning,” Proceedings of the 3rd Annual Computation in Neurons
and Neural Systems Meeting, Boston, MA: Kluwer Academic Publishers.
33. Becker, S., Behrmann, M. and Moscovitch, M. (1993), “Word priming in attractor networks”, Proceedings
of the Fifteenth Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Lawrence
Erlbaum Associates, pp. 231-236.
34. Becker, S. (1993), “Learning to categorize objects using temporal coherence”, in Giles, C.L., Hanson,
S.J. and Cowan, J.D. (eds.), Advances in Neural Information Processing Systems 5, pp. 361-368, San
Mateo, CA: Morgan Kaufmann Publishers. (full paper)
35. Becker, S. and Hinton, G. E. (1993), “Learning mixture models of spatial coherence,” Neural Computation,
Vol. 5, No. 2, pp. 267-277.
36. Becker, S. and Hinton, G. E. (1992), “A self-organizing neural network that discovers surfaces in
random-dot stereograms,” Nature, Vol. 355, pp. 161-163.

37. Becker, S. and Hinton, G. E. (1992), “Learning to make coherent predictions in domains with discontinuities,”
in Advances in Neural Information Processing Systems 4, pp. 372-379, Morgan Kaufmann
Publishers.
38. Becker, S. (1991), “Unsupervised learning procedures for neural networks,” International Journal Of
Neural Systems, Vol. 2, No. 1&2, pp. 17-33.
39. Becker, S. and le Cun, Y. (1988), “Improving the convergence of back-propagation learning with
second-order methods,” Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann
Publishers, D. S. Touretzky, G. E. Hinton and T. J. Sejnowski (eds). Also appeared as University
of Toronto Connectionist Research Group Technical Report CRG-TR-88-5.
40. Crawford, R.G. and Becker, H.S. (1985), “Toward the development of interfaces for untrained users,”
Proceedings of the American Society for Information Science, Las Vegas, Nevada, Vol 22, pp. 236-23.
(ii) Peer Reviewed Abstracts:
1. Becker, S. and Wojkowicz, J.M. (to appear), A role for hippocampal neurogenesis in retention of
long-term memories: evidence from computational modelling. To appear, Proceedings of the 2004
Society for Neuroscience Meeting.
2. Dominguez, M., Becker, S. and Bruce, I. (2004), “Modelling Cortical Reorganization in Tinnitus”,
Proceedings of the 2004 Computational and Systems Neuroscience meeting.
3. Becker, S., Chan, A., Fletcher, P., Smith, A. and Kapur, S. (2003), “A computational model of the
role of dopamine and psychotropic drugs in modulating motivated action”. Schizophrenia Research
60(1) Supplement: Abstracts of th IXth International Congress on Schizophrenia Research, page 164.
4. Howell, S., Becker, S. and Jankowicz, D. (2003), “A Model of Sensorimotor Grounding in Lexical
Learning”. Proceedings of the Brain, Behaviour and Cognitive Science conference.
5. Jankowicz, D., Becker, S. and Howell, S. (2003), “Modeling Semantic Category-Specific Deficits Using
Topographic, Corpus-Derived Representations”. Proceedings of the Brain, Behaviour and Cognitive
Science conference (abstract). Also to appear in the Proceedings of the 44th Annual Meeting of the
Psychonomics Society.
6. Chun, S., Campos, J., Chan, G., Becker, S., Burgess, N. and Sun, H.-J. (2003), “Viewpoint Dependency
in the Perception of Multi-Object Layout”, Proceedings of the Brain, Behaviour and Cognitive
Science conference.
7. Gilbert, C. and Becker, S. (2003), “Semantic Strategies in Free Recall”, Proceedings of the Brain,
Behaviour and Cognitive Science conference.
8. Becker, S., Chan, A., Fletcher, P. and Kapur, S. (2002), “A computational network model of the
neural circuits subserving motivated behaviours”, Society for Biological Psychiatry, Philadelphia,
May, 2002.
9. Becker, S. (2002), “Strategic Learning and the Control of Memory”, Proceedings of the Psychonomics
Society Meeting.

10. Howell, S. and Becker, S. (2000), “Modelling language acquisition at multiple temporal scales”,
Proceedings of the Cognitive Science Socitey meeting.
11. Lim, J.C. and Becker, S. (2000), “A Reinforcement Learning Application to Free Recall Clustering”,Proceedings
of the Cognitive Science Socitey meeting.
(iii) Patents
1. Haykin, S., Becker, S., Bruce, I., Bondy, J., Trainor, L. and Racine, R.J. (Submitted), Binaural
Adaptive Hearing System. UNITED STATES PROVISIONAL PATENT APPLICATION.
(iv) Not Peer Reviewed:
1. Bondy, J., Bruce, I., Becker, S. and Haykin, S. (2003), Applications for modeling of intelligibility
of sensorineural hearing loss. 37th Asilomar Conference on Signals, Systems, and Computers, 2003.
Invited paper.
2. Bruce, I., Bondy, J., Haykin, S. and Becker, S. (2002), “A Physiologically Based Predictor of Speech
Intelligibility”, International Conference on Hearing Aid Research (abstract).
3. Bondy, J., Lopez-Risueqo, G., Haykin, S., Bruce, I. and Becker, S. (2002), “Automatic Identification
and Individualized Phonemic Processing”, International Conference on Hearing Aid Research
(abstract).
4. Becker, S. and Bruce, I. (2002), “Neural coding in the auditory periphery: Insights from physiology
and modelling lead to a novel hearing compensation algorithm”, Workshop on Neural Information
Coding, Les Houches France, March, 2002 (abstract).
5. Patel, G.S., Becker, S. and Racine, R. (2001), “2D Image Modelling as a Time Series Prediction
Problem”, in Kalman filtering applied to neural networks, S. Haykin (editor), J. Wiley and Sons.
6. Becker, S. (2000) “The hippocampus as a multi-layer learning circuit: Seeing beyond the Hebb rule”,
NICE 2000 - Learning and Neural Plasticity: Theoretical and Experimental Approaches workshop,
Grindelwald, Switzerland (abstract).
7. Cheung, A., Becker, S. and Burgess, N. (2000), “A model of hippocampal-parietal interaction in
spatial navigation, imagery and episodic recall”, workshop on The nature of hippocampal-cortical
interaction: Theoretical and experimental perspectives (abstract), Dublin, Ireland.
8. Becker, S., Chin, S. and Meeds, E. (1999), “Modelling episodic memory: A global cost function
that leads to fast, local, high-capacity learning”, Proceedings of the Learning workshop (abstract),
Snowbird, Utah, April, 1999.
9. Becker, S. and Liwanag, A. (1997),“One-shot learning in Hopfield networks with hidden units”,
Proceedings of the Machines That Learn workshop (abstract), Snowbird, Utah.
10. Becker, S. (1996), “An unsupervised classifier modulated by temporal history outperforms recurrent
back-propagation in face recognition”, Proceedings of the Machines That Learn workshop (abstract),
Snowbird, Utah.

11. Becker, S. (1995), “Unsupervised learning of population codes as a joint density fitting problem”,
Proceedings of the Neural Networks for Computing workshop (abstract), Snowbird, Utah.
12. Becker, S. and Hinton, G. E. (1995), “Spatial coherence as an internal teacher for a neural network,”
in Backpropagation: Theory, Architectures, and Applications, Y. Chauvin and D. Rumelhart (eds),
part of the series Developments in Connectionist Theory, Lawrence Erlbaum: Hillsdale, NJ. Also
appeared as University of Toronto Connectionist Research Group Technical Report CRG-TR-89-7.
13. Roberts, L.E., Racine, R.J., Durlach, P. and Becker, S. (1994), “Tuning and filtering in associative
learning”, in Oscillatory event-related brain dynamics, C. Pantev, T. Elbert and B. Lutkenhoner
(eds), Plenum Press.
14. Hinton, G. E. and Becker, S. (1992), “Using coherence assumptions to discover the underlying causes
of the sensory input,” In S. Davis (Ed.) Connectionism: Theory and practice, New York: Oxford
University Press, pages 3-29.
15. Hinton, G. E. and Becker, S. (1990), “An unsupervised learning procedure that discovers surfaces
in random-dot stereograms,” Proceedings of the International Joint Conference on Neural Networks,
Vol. 1, pp. 218-222, Lawrence Erlbaum Associates, Hillsdale, NJ.
PRESENTATIONS AT MEETINGS: ( ∗∗ : also listed under Proceedings of Meetings)
i) Invited:
1. Workshop on New Directions for Signal Processing in the 21st Century, Lake Louise, Alberta, October
5-10, 2003.
2. Computational Mechanisms in the Generation and Treatment of Tinnitus, Workshop on Understanding
and Treating Tinnitus, Peter Wall Institute, Vancouver B.C. November, 2003.
3. Modelling Cortical Reorganization in Tinnitus, Workshop on Tinnitus Modelling, Niagara-on-thelake,
April, 2003.
4. “Computational models of neural coding in the hippocampus”, Workshop on Computational Neuroscience:
Levels of Modelling, Fields Institute, Toronto, October 21, 2002.
5. “Connectionist models of controlled memory search: Successes, challenging data, and future directions”,
Symposium on Models of Context, Orlando, Florida, November, 2002.
6. “Computational models of neural coding explain robust, rapid memory formation and neurogenesis in
the hippocampus”, Symposium on Interdisciplinary Approaches to Neuroscience: The Hippocampus,
Kalamazoo College, Michigan, April 2002.
7. “Neural coding in the auditory periphery: Insights from physiology and modelling lead to a novel
hearing compensation algorithm”, Workshop on Neural Information Coding, Les Houches France,
March, 2002. ∗∗
8. “A Model of Strategic Memory Access: Prefrontal Involvement in the California Verbal Learning
Task”, Workshop on Connectionist Neuropsychology, Whistler, B.C., December, 2001.

9. “The hippocampus as a multi-layer learning circuit: Seeing beyond the Hebb rule”, NICE2000 workshop
on Learning and Neural Plasticity: Theoretical and Experimental Approaches, Grindelwald,
Switzerland. March, 2000. ∗∗
10. “Modelling episodic memory: A global cost function that leads to fast, local, high-capacity learning”
Workshop on Learning, Snowbird, Utah. April, 1999. ∗∗
11. “Modelling expectancy and translation-invariance in musical sequence processing”, Workshop on
Statistical Theories of Cortical Function, Breckenridge, Colorado, December, 1998.
12. “Modelling intelligence: In search of the brain’s cost function”, Workshop on computational and
biological models of intelligence, Seattle WA and Friday Harbour, WA, August 1998.
13. “Implicit and Explicit Learning: What computational models can tell us”, Lake Ontario Visionary
Establishment conference, February 1998.
14. “Computations in the hippocampus: From global to local cost functions”, Workshop on Computational
Neuroscience and Generative Models, University of Toronto, February 1998.
15. “Context, coherence and cortical computations”, MRC research group workshop on brain mapping
and cortical plasticity, Vineland, Ontario, June, 1997.
16. “One-shot learning in Hopfield networks with hidden units”, Machines That Learn workshop, Snowbird,
Utah, April 1997. ∗∗
17. “An unsupervised classifier modulated by temporal history outperforms recurrent back-propagation
in face recognition”, Machines That Learn workshop, Snowbird, Utah, April 1996. ∗∗
18. “Unsupervised learning and vision I & II”, Spring Workshop on Artificial Neural Networks, University
of Montreal, two invited lectures, April, 1996.
19. “Computational and empirical evidence for temporal persistence as a cue in perceptual learning”,
McDonnell-Pew Program in Cognitive Neuroscience, annual meeting, Wellesley, Maryland, June,
1996.
20. “On the Computational Utility of Contextually Modulated Plasticity: A Model, some Empirical Results
and Speculations on Cortical Function”, Workshop on Neural Modulation, Snowmass, Colorado,
December, 1996.
21. “Unsupervised learning of population codes as a joint density fitting problem”, Neural Networks for
Computing workshop, Snowbird, Utah, April 1995. ∗∗
22. “Long-term semantic priming in lexical decisions”. Symposium on Computational Approaches to
Modelling Semantic Effects in Word Recognition, BBCS Conference, Halifax, Nova Scotia, June
1995.
23. “Information maximization vs. density estimation: What’s the best cost function for unsupervised
learning?”. Presented at the Workshop on Information Theory and Neural Networks at the 1995
meeting of the Neural Information Processing Society in Colorado, December, 1995.
24. “Learning to cluster visual scenes with contextual modulation”. Workshop on Unsupervised Learning
and Vision, at the 6th annual meeting of the Neural Information Processing Society, Vale, Colorado,
December, 1994.

25. Workshop on “Unsupervised Learning”, 1993 Connectionist Models Summer School, Boulder, Colorado.
26. “Unsupervised connectionist learning procedures that discover Spatially coherent features of the
visual world”, Neural Information Processing Society workshop on Unsupervised Learning and Selforganization
in Vision, Vale, Colorado, December, 1991.
ii) Contributed:
Peer Reviewed:
1. A computational network model of the neural circuits subserving motivated behaviours”, Contributed
poster presented (refereed), Society for Biological Psychiatry. May 2002.
2. “Strategic Learning and the Control of Memory”, Psychonomics Society Meeting, Kansas City, MO,
November, 2002
3. “A computational network model of the neural circuits subserving motivated behaviours”, Society
for Biological Psychiatry, Philadelphia, May, 2002. Contributed. ∗∗
4. “A model of hippocampal-parietal interaction in spatial navigation, imagery and episodic recall”,
workshop on The nature of hippocampal-cortical interaction: Theoretical and experimental perspectives
Dublin, Ireland. March, 2000. ∗∗
5. “Improving Associative Memory Capacity: One-Shot Learning in Multilayer Hopfield Networks”. Selected
for oral presentation at the 19th Annual Conference of the Cognitive Science Society, Stanford,
CA, August, 1997. ∗∗
6. “Learning temporally persistent hierarchical representations”, Neural Information Processing Systems
9, Denver Colorado, 1996. ∗∗
7. “JPMAX: Learning to recognize moving objects as a model-fitting problem”, Neural Information
Processing Systems 7, Denver, Colorado, 1994. ∗∗
8. “Model synapses with frequency potentiation characteristics can cooperatively enhance Hebbian
learning,” 3rd Annual Computation in Neurons and Neural Systems Meeting, Boston, Mass., 1994. ∗∗
9. “Word priming in attractor networks”, Fifteenth Annual Conference of the Cognitive Science Society,
Pittsburgh, PA, 1993. ∗∗
10. “Learning to categorize objects using temporal coherence”, Neural Information Processing Systems
5, Denver, Colorado, 1992. ∗∗
11. “Learning to make coherent predictions in domains with discontinuities,” Neural Information Processing
Systems 4, Denver, Colorado, 1991. ∗∗
Not Peer Reviewed:
1. “Computations in the hippocampus: From global to local cost functions”, Workshop on Computational
models of episodic memory and hippocampal function, Breckenridge, Colorado, December
1997.

2. “Long-term semantic priming in lexical decisions”, Psychonomics Society Meeting, 1995.
INVITED SEMINARS:
1. “A computational model of encoding and neurogenesis in the hippocampus, Hippocampal Neurogenesis
workshop, Computational Neurosciences conference, Baltimore, July 21-22, 2004 (invited)
2. Computational Models of Neural Coding in the Hippocampus, Understanding Complex Systems
Symposium, UIUC, Illinois, May 20, 2004 (invited).
3. “Is cognitive control an uncontrolled process? Insights and confusions from models and memory
experiments.” Cognition/Perception Seminar, Dept Psychology, McMaster, September, 2002.
4. “Computing visuo-spatial maps: From visual images to spatial memories and back again”, Centre
for Vision Research, York University, Toronto, Ontario, December, 2001.
5. “Models of dopaminergic actions in reinforcement learning, neuromodulation and schizophrenia”,
Schizophrenia Research Seminar, Centre of Addiction and Mental Health, Toronto, October, 2001.
6. “Intelligent hearing aid systems: adaptive signal processing and neural model-based approaches”,
July, 2001, BLISS/Intelligent Hearing Aid joint group meeting, Granada, Spain.
7. “A neural model of hippocampal-parietal interactions in spatial memory”, February 2, 2001, IGERT
program on Dynamics of Complex Systems in Science and Engineering, Interdisciplinary Seminar in
Nonlinear Science, Northwestern University, Chicago, Illinois.
8. “Hippocampal-parietal networks in imagery and scene recall”, February 28, 2001, Ebbinghaus Empire
Seminar, Department of Psychology, University of Toronto.
9. “Hippocampal Computations in Memory”, Swiss Federal Institute of Technolgy, Lausanne Switzerland,
January, 2000.
10. Implicit learning in 3D object recognition: The importance of temporal context, Colloquium for
Neuroinformatics, ETH, Zurich. January, 2000.
11. “A neural model of spatial cognition”, Informatics Jamboree, Edinburgh, Scotland, May 2000.
12. “Modelling the hippocampal system: From Hebbian learning to multilayer circuits and back again”,
October 22, 1999, Institute of Cognitive Neuroscience, University College London.
13. “What episodic memories are made of: Hebbian learning and beyond”, Ebbinghaus Empire seminar,
Department of Psychology, University of Toronto, March, 1999.
14. “The more you learn the less you know, and other paradoxes in connectionist models of human
memory”, Cognitive Science seminar, University of Arizona, Tucson, February 1997.
15. “Mechanisms of learning in the neocortex and hippocampus: What computational models can tell
us”, Rotman Research Institute, Baycrest Hospital, North York, Ont., December 1997.
16. “Contextually Modulated Unsupervised Learning”, Neural Networks Lab, Department of Computer
Science, University of Toronto, February, 1995.

17. “Converting hard learning problems to easy ones with unsupervised neural networks”, Seminar,
Department of Computer Science and Systems, McMaster, April, 1995.
18. “Unsupervised classification using temporal continuity”, Communications Research Lab, McMaster,
May, 1995.
19. “Connectionist modelling of hippocampal functions” Psychobiology Seminar, Department of Psychology,
McMaster University, April, 1994.
20. “Unsupervised connectionist learning procedures that discover spatio-temporal regularities in the
visual world”. Cognitive Science Seminar, Department of Psychology, University of Rochester, March,
1994.
21. “Unsupervised learning in neural networks”, Department of Computer Science, Queen’s University.
22. ”Learning invariant properties of sensory signals in neural networks”. Colloquium talk, Department
of Computer Science, University of Central Florida, Orlando, November, 1993.
23. ”A model of word priming in attractor networks that makes some novel predictions about human
memory”. Artificial Intelligence Lab, UCSD, February, 1993.
24. ”A model of word priming in attractor networks that makes some novel predictions about human
memory”. Connectionist Research Group, Stanford University, February, 1993.
25. “Learning Spatially Coherent Properties Of the Visual World in Connectionist Networks”, Department
of Computer Science, Queen’s University, 1991.
ADMINISTRATIVE RESPONSIBILITIES
i) Department
Curriculum Committee, Member, 1995-96
Computing Committee, Member, 1994-95
Hiring/Job Search Committee, Member, 1993-94, 2002-2003
Computing Committee, Chair, 1995-
Computer-aided Instruction Committee, Member, 1996-
Undergraduate Student Counselling Comittee, Member, 1998-2000
Undergraduate Curriculum Committee, 2002-
CFI budget/equipment purchase Committee, Member, 2003
ii) Faculty
Dean’s Advisory Committee on Computing, Member, 1993-94
Social Sciences Undergraduate Curriculum Committee, Member, 1995-96
Chair Selection Committee, Department of Mathematics and Statistics, 1995-96

Steering Committee, Honours Neural Computation Programme, 1995-
Programme Co-ordinator, Honours Neural Computation, 1995-
iii) University
Member, McMaster’s Research and High Performance Computing Support (RHPCS) Advisory Committee,
chaired by Hugh Couchman (as of May, 2002)
OTHER RESPONSIBILITIES
Faculty Member, Connectionist Models Summer School, Boulder CO, 1993
Topic area chair, International Congress of Psychology conference, 1995
Member, Organizing committee (Publicity chair), Neural Information Processing Society conference, 1996
Member, Program Committee, Neural Information Processing Society conference, 1997
Member, Organizing committee (Workshops co-chair), Neural Information Processing, 1998-99
Program Chair, Neural Information Processing Society conference, 2000-2001
General Chair, Neural Information Processing Society conference, 2001-2002
Member, Scientific Advisory Board, The Medipattern Corporation, Toronto, 2002-
Member, Provincial panel to rank students for OGS scholarships, 2003
Member, Advisory Board, Neural Information Processing Society conference, 2003-
Member, Five-year Review Committee, RIKEN Brain Sciences Institute, Creating the Brain section,
2004-2005