Department of Psychology, Neuroscience & Behaviour

Suzanna Becker, Ph.D., Professor


Neurofeedback, brain-computer interfaces, neural network models

Dhindsa, K., Gauder, K. D., Marszalek, K., Terpou, B., & Becker, S. (In revision) Progressive Thresholding: Incorporating Shaping in Automated Neurofeedback Training.

Dhindsa, K. & Becker, S. (2017) Emotional Reaction Recognition from EEG. 7th International Workshop on Pattern Recognition in Neuroimaging 2017

Dhindsa, K., Carcone, D. and Becker, S. (2017), A brain-computer interface based on abstract visual and auditory imagery: Evidence for an effect of artistic training. Proceedings of the 19th International Conference on Human-computer Interaction. Vancouver, Canada. July 9-14 2017.

Dhindsa, K., Carcone, D. and Becker, S. (2017), Toward an open-ended BCI: A user-centered coadaptive design. Neural Computation 29(10).

[Preprint pdf] Becker, S. (2017), Neurogenesis and pattern separation: Time for a divorce. Wiley Interdisciplinary Reviews: Cognitive Science (WIREs Cogn Sci 2017, 8:e1427. doi: 10.1002/wcs.1427)

Finnegan, R., Shaw, M. and Becker, S. (2017), Restricted Boltzmann Machine Models of Hippocampal Coding and Neurogenesis. The Rewiring Brain: A Computational Approach to Structural Plasticity in the Adult Brain. Section VIII. Structural plasticity in large-scale models. Edited by Arjen van ooyen and Markus Butz-Ostendorf, Elsevier.

[pdf] Becker, S. (2017), Marr's simple memory theory of archicortex, then and now: four decades later, things are not quite as simple. Chapter 7, page 159-177 in Computational Theories and their Implementation in the Brain: The Legacy of David Marr. Edidted by L.M. Vaina and R.E. Passingham. Oxford University Press.

[pdf] Finnegan, R. and Becker, S. (2015), Neurogenesis paradoxically decreases both pattern separation and memory interference. Frontiers in Systems Neuroscience 9:136 doi: 10.3389/fnsys.2015.00136

Howell, S. R., and Becker, S. (2013). Grammar from the Lexicon: Evidence from Neural Network Simulations of Language Acquisition, in {\fontI Lexical Bootstrapping. The role of lexis and semantics in child language development}, D. Bittner and N. Ruhlig (Eds), de Gruyter Mouton.

Chrostowski, M., Yang, L., Wilson, H., Bruce, I. and Becker, S. (2011), Can Homeostatic Plasticity in Deafferented Primary Auditory Cortex Lead to Traveling Waves of Excitation? Journal of Computational Neuroscience 30(2):279-299. DOI 10.1007/s10827-010-0256-1

Aubie, B., Becker, S. and Faure, P. (2009), Computational models of millisecond level duration tuning in neural circuits. J. Neurosci. 29(29):9255-9270

Becker, S., MacQueen, G. and Wojtowicz, J.M. (2009), Computational modeling and empirical studies of hippocampal neurogenesis-dependent memory: Effects of interference, stress and depression. Brain Research 1299:45-54.

[pdf] Byrne, P. and Becker, S. (2008), A principle for learning egocentric-allocentric transformations. Neural Computation. 20(3):709-737.

Turnock, M. and Becker, S. (2008), A neural network model of hippocampal-striatal-prefrontal interactions in contextual conditioning. Brain Research 1202:87-96, doi:10.1016/j.brainres.2007.06.078, 18(12):2942-2958

[pdf] Becker, S. and Wojtowicz, J.M. (2007), A model of hippocampal neurogenesis in memory and mood disorders. Trends in Cognitive Sciences 11(2):70-76.

[pdf] Smith, A., Li, M., Becker, S. and Kapur, S. (2007), Linking animal models of psychosis to computational models of dopamine function. Neuropsychopharmacology 32(1):54-66, doi:10.1038/sj.npp.1301086,

[pdf] Smith, A., Li, M., Becker, S. and Kapur, S. (2006), Dopamine, prediction error, and associative learning: a model-based account. Network: Computation in Neural Systems 17(1):61-84.

[pdf] Dominguez M, Becker S, Bruce I, and Read H. (2006), A spiking neuron model of cortical correlates of sensorineural hearing loss: spontaneous firing, synchrony and tinnitus. Neural Computation 18(12):2942-2958,

[pdf] Chen, Z., Becker, S., Bondy, J., Bruce, I.C. and Haykin, S. (2005), A novel model-based hearing compensation design using a gradient-free optimization method. Neural Computation 17(12):2648-2671.

[pdf] Becker, S. (2005) "A computational principle for hippocampal learning and neurogenesis". Hippocampus 15(6):722-738.

[ pdf] Smith, A., Becker, S. and Kapur, S. (2005), A computational model of the selective role of the striatal D2-receptor in the expression of previously acquired behaviours. Neural Computation 17(2):361-395.

[pdf] Howell, S. R., Jankowicz, D., and Becker, S. (2005), A Model of Grounded Language Acquisition: Sensorimotor Features Improve Grammar Learning. Journal of Memory and Language 53(2):258-276.

[ pdf] Smith, A., Li, M., Becker, S. and Kapur, S. (2004), A model of antipsychotic action in conditioned avoidance: a computational approach. Neuropsychopharmacology29(6):1040-9

Becker, S. (2005), Modelling the mind: From circuits to systems. Chapter 2 in New Directions in Statistical Signal Processing: From sytems to brain. Simon Haykin, Jose C. Principe, Terrence J. Sejnowski and John McWhirter (editors), MIT Press. pdf chapter, pdf References

Becker, S. and Wojtowicz, J.M. (2004), A role for hippocampal neurogenesis in retention of long-term memories: evidence from computational modelling. Abstract. Proceedings of the 2004 Society for Neuroscience Meeting.

Haykin, S., Chen, Z. and Becker, S. (2004), Stochastic Correlative Learning Algorithms, IEEE Transactions on Signal Processing, 52(8):2200-2209.

Chen, Z., Haykin, S. and Becker, S. (2004), Theory of Monte Carlo Sampling-Based Alopex Algorithms For Neural Networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing,

[pdf] 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 2003, 15(6):347-374.

[pdf] Becker, S. and Zemel, R., (2003), ``Unsupervised learning with global objective functions'', In The Handbook of Brain Theory and Neural Networks, Second edition, (M.A. Arbib, Ed.), Cambridge, MA: The MIT Press, 2003.

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.

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.

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.

Becker, S., Chan, A., Fletcher, P. and Kapur, S. (2002), A computational network model of the neural circuits subserving motivated behaviours, Biological Psychiatry 51(8S). Abstract.

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.

Patel, G., Becker, S., and Racine, R. (2001), "2D Image modelling as a time-series prediction problem", in Kalman filtering applied to Neural Netwroks, S. Haykin (editor), Wiley.

[pdf] Howell, S. and Becker, S. (2001), Modelling language acquisition: Grammar from the lexicon?, Proceedings of the cognitive science society, 2001.

[pdf] 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).

Howell, S. and Becker, S. (2000), "Modelling language acquisition at multiple temporal scales", abstract in Proceedings of the 22nd Meeting of the Cognitive Scienc Society.

Lim, J.C. and Becker, S. (2000), "Hippocampal-frontal interactions in memory storage and recall", abstract in Proceedings of the 22nd Meeting of the Cognitive Scienc Society. Abstract

[pdf] Christianson, G.B. and Becker, S. (1999), A model for associative multiplication, Neural Information Processing Systems 11, 1999.

Becker, S., Chin, S. and Meeds, E. (1999), "Modelling episodic memory: A global cost function that leads to fast, local, high-capacity learning", abstract in Proceedings of the "Learning" workshop, Snowbird, Utah, April, 1999.

[ pdf ] Becker, S. (1999), Implicit learning in 3D object recognition: The importance of temporal context. Neural Computation, 11(2):347-374.

[ pdf] Liwanag, A. & Becker, S. (1997) "Improving Associative Memory Capacity: One-Shot Learning in Multilayer Hopfield Networks". In the Proceedings of the 19th Annual Conference of the Cognitive Science Society, Lawrence Erlbaum Associates, pages 442-447.

[ pdf] Becker, S., Moscovitch, M. Behrmann, M. and Joordens (1997), "Long-term semantic priming: A computational account and empirical evidence". Journal of Experimental Psychology: Learning, Memory and Cognition, 23(5):1059-1082.

[ pdf] Becker, S. (1997) "Learning temporally persistent hierarchical representations". In Advances in Neural Information Processing Systems 9, MIT Press, pages 824-830.

Becker, S. (1996), "An unsupervised classifier modulated by temporal history outperforms recurrent back-propagation in face recognition", abstract in Proceedings of the Machines That Learn workshop in Snowbird, Utah. abstract

[ pdf] Becker, S. (1996), "Mutual Information Maximization: Models of Cortical Self-Organization". Network: Computation in Neural Systems, 7:7-31

[pdf] Becker, S. and Plumbley, M. (1996), "Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification, " International Journal of Applied Intelligence, special issue on neural networks (F. Pineda, ed.), Vol. 6, No. 3.

[pdf] Becker, S. (1995), "JPMAX: Learning to Recognize Moving Objects as a Model-fitting Problem", in Advances in Neural Information Processing Systems 7:933-940, San Mateo, CA: Morgan Kaufmann Publishers.

[pdf] 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.

[pdf] 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, Hillsdale, NJ: Lawrence Erlbaum.

Chapman, C.A. and Becker, S. (1995), "Model synapses with frequency potentiation characteristics can cooperatively enhance Hebbian learning," Proceedings of the 3rd Annual Computation and Neural Systems Meeting, Boston, MA: Kluwer Academic Publishers. abstract

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.

Becker, S. (1994), "Unsupervised learning of population codes as a joint density fitting problem," abstract in Proceedings of the Neural Networks for Computing Conference, Snowbird, Utah.

Becker, S., Behrmann, M. and Moscovitch, M. (1993), "Word priming in attractor networks", Proceedings of the Fifteeth Annual Conference of the Cognitive Science Society, Hillsdale, NJ: Lawrence Erlbaum Associates, p. 231-236.

[pdf] 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.

[pdf] Becker, S. and Hinton, G. E. (1993), "Learning mixture models of spatial coherence," Neural Computation, Vol. 5, No. 2, pp. 267-277.

[pdf] 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. News&Views summary PDF, Mitchison & Durbin, Nature 2002

[pdf] Becker, S. and Hinton, G. E. (1992), "Learning to make coherent predictions in domains with discontinuities," in Advances in Neural Information Processing Systems 4, Morgan Kaufmann Publishers.

[ pdf p1-50, p51-100, p101-148] Becker, S. (1992), "An Information-theoretic unsupervised learning algorithm for neural networks", PhD Thesis, University of Toronto, Department of Computer Science.

[pdf] Becker, S. (1991), "Unsupervised learning procedures for neural networks," International Journal Of Neural Systems, Vol. 2, No. 1\&2, pp. 17-33.

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.

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.