Hierarchical Anticipatory Learning (HAL Lab)
The brain is an amazing adaptive system able to make sense of the world and guide flexible behavior. We study how such a system learns from experience and guides decision-making. Besides modeling specific brain processes in conjunction with physiological and behavioral studies, we apply such cognitive processes to robots.
While we are specifically interested in biological information processing, the field of machine learning and computational neuroscience has become the forefront in application development at Google and Microsoft. It can also be applied to advanced data mining and artificial intelligence.
|Contact Information:||Thomas Trappenberg at: email@example.com|
|Research Areas and
|Graduate Students and
|Seminar Series:||Hallab chats Wednesdays 10am-11:30|
|Related Conferences and
|Cosyne, NIPS, IJCNN|
D. Standage and T. Trappenberg (2011) Cognitive Neuroscience, in The Cambridge Handbook to Cognitive Science, Keith Frankish and William Ramsey), Cambridge University Press, in press
T.P. Trappenberg (2010) Fundamentals of Computational Neuroscience, 2nd edition, Oxford University Press, ISBN13: 9780199568413, ISBN10: 0199568413.
T.P. Trappenberg (2008) Decision making and population decoding with strongly inhibitory neural field models, in Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour', Psychology Press, London, Dietmar Heinke & Eirini Mavritsaki (eds.).
P. Hollensen, P. Hartono and T. Trappenberg (2011) Topographic RBM as robot controller, Annual Conference of the Japanese Neural Network Society (JNNS) 2011.
A. Hoggarth, R. Rankaduwa, A. Fine and T. Trappenberg (2011) Temporal sequence learning and the hippocampus: A continuous attractor model of location based learning in sequential activation of place cells, Annual Conference of the Japanese Neural Network Society (JNNS) 2011.
R. Marino, T. Trappenberg, M. Dorris, D.Munoz (2011), Spatial Interactions in the Superior Colliculus Predict Saccade Behavior in a Neural Field Model, Journal of Cognitive Neuroscience.
Z. Wang, J. Satel, T. Trappenberg and R. Klein (2011), Aftereffects of Saccades Explored in a Dynamic Neural Field Model of the Superior Colliculus, Journal of Eye Movement Research 4(2):1, 1--16.
P. Connor and T. Trappenberg (2011), Characterizing a Brain-Based Value-Function Approximator, in Advances in Artificial Intelligence LNAI 2056, Eleni Stroulia and Stan Matwin (eds), Springer 2011.
P. Hollensen, W. Connors and T. Trappenberg (2011), Comparison of Learned Versus Engineered Features for Detection of Mine Like Objects from Raw Sonar Images, in Advances in Artificial Intelligence LNAI 2056, Eleni Stroulia and Stan Matwin (eds), Springer 2011.
P. Hartono and T. Trappenberg, Autonomous Robot with Internal Topological Representation, The 3rd Int. Conf. on Cognitive Neurodynamics.
P. Connor and T. Trappenberg (2011), A new functional role for lateral inhibition in the striatum: Pavlovian conditioning, Computational and System Neuroscience (COSYNE) 2011
J Satel, Z. Wang, T. Trappenberg and R. Klein (2011), Modeling inhibition of return as short-term depression of early sensory input to the superior colliculus, Vision Research 51 (2011) 987–996.
T. Trappenberg, A Saito and P. Hartono (2010), Selective attention improves self-organization of cortical maps with multiple inputs, IJCNN 2010.
W. Connors, P. Connor and T. Trappenberg (2010), Detection of Mine-Like Objects using Restricted Boltzmann Machines, Lecture Notes in Computer Science 6085, AI2010, Atefeh Farzindar and Vlado Keselj (eds), 362-365, Springer.
T. Trappenberg, P. Hartono, and D. Rasmusson (2009), Top-down control of learning in biological self-organizing maps, Lecture Notes in Computer Science 5629, WSOM 2009, J. Principe and R. Miikkulainen (eds), 316-324, Springer.
J.P. Salmon, J. P. and T.P. Trappenberg (2008), Modeling the integration of expectations in visual search with centre-surround neural fields. Neural Networks, 21: 1476-1492.
T. Trappenberg (2008), Tracking population densities using dynamic neural fields with moderately strong inhibition, Cognitive Neurodynamics 2:171–177.
S. Wu and T. Trappenberg (2008), Learning in sparse attractor networks with inhibition, in Advances in Cognitive Neurodynamics, Wang, Rubin; Gu, Fanji; Shen, Enhua (Eds.), Springer.
D. Standage, S. Jalil and T. Trappenberg (2007), Computational consequences of experimentally derived spike-time and weight dependent plasticity rules, Biological Cybernetics, Vol. 96, No. 6. (June 2007), pp. 615-623.
M. Boardman, T. Trappenberg (2006), A Heuristic for Free Parameter Optimization with Support Vector Machines, WCCI 2006, 1337-1344. Source Code. Homepage
M. Lawrence , T. Trappenberg, A. Fine (2006) Rapid learning and robust recall of long sequences in modular associator networks, Neurocomputing , 69(7-9): 634-641.