Our research is driven by the desire to understand how cognitive phenomena can arise from the collective interactions of simple neural elements. In particular, we investigate how the brain's networks and subsystems can self-organize to give rise to intelligent perception and action. Our research builds computational models of various aspects of visual perception, action, and learning, but we also complement this research by testing specific implications of computational theories with psychological experiments and by testing different approaches in computer vision applications.
We firmly believe studying the organizational principles of neural information processing, through computational modeling, will further our understanding of brain function and organization while making progress towards a new generation of intelligent artificial information processing systems with potentially profound social and economic implications. The long-term goal we are pursuing is an embodied computational account of the developing human visual system, that autonomously learns to perceive, understand, and interact with its environment under minimal external supervision.
P. Zheng, C. Dimitrakakis, and J. Triesch.
Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex
PLoS Computational Biology, 9(1), doi:10.1371/journal.pcbi.1002848, 2013. (online)
C. Savin, P. Joshi, and J. Triesch.
Independent Component Analysis in Spiking Neurons
PLoS Computational Biology, 6(4), doi:10.1371/ journal.pcbi.1000757, 2010. (online)
A. Lazar, G. Pipa, and J. Triesch.
SORN: a Self-organizing Recurrent Neural Network
Frontiers in Computational Neuroscience, 3(23), doi:10.3389/neuro.10.023, 2009. (online)
C. Weber, and J. Triesch.
A Sparse Generative Model of V1 Simple Cells with Intrinsic Plasticity
Neural Computation 20:1261-1284, 2008. (pdf)
J. Triesch, H. Jasso, and G. Deak.
Emergence of Mirror Neurons in a Model of Gaze Following
Adaptive Behavior 15(2):149-165, 2007. (pdf)
Synergies between Intrinsic and Synaptic Plasticity Mechanisms
Neural Computation, 19:885-909, 2007. (pdf)
J. Triesch, C. Teuscher, G. Deak, and E. Carlson.
Gaze Following: why (not) learn it?
Developmental Science, 9(2):125-147, 2006. (pdf)
J. Triesch, D.H. Ballard, M.M. Hayhoe, and B.T. Sullivan.
What you see is what you need.
Journal of Vision, 3:86-94, 2003. (online)
J. Triesch and C. v.d. Malsburg.
Democratic Integration: Self-Organized Integration of Adaptive Cues.
Neural Computation, 13(9):2049-2074, 2001. (pdf)
Vikram Narayan joined the group as a postdoc researcher. Welcome!