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Prof. Dr. Thomas Ott

Prof. Dr. Thomas Ott

Prof. Dr. Thomas Ott

ZHAW Life Sciences und Facility Management
Institut für Computational Life Sciences
Schloss
8820 Wädenswil

+41 (0) 58 934 56 84
thomas.ott@zhaw.ch

Arbeit an der ZHAW

Tätigkeit

Institutsleitung Institut für Computational Life Sciences

Arbeits- und Forschungsschwerpunkte

Bio-inspired Computing, Applied Neuroinformatics (Pattern Recognition, Machine Learning), Modelling of Complex Systems Forecast methodologies for traffic and logistics Digital Health Assistance Systems

Lehrtätigkeit

  • Systeme und Modelle der Physik (BSc ADLS)
  • Optimisation Methods and Bio-inspired Algorithms (MSc Life Sciences ACLS)

Netzwerk

Mitglied in Netzwerken

ORCID digital identifier

ORCID ID: 0000-0003-1635-7816

Social Media

Linkedin

Projekte

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed
Buchbeiträge, peer-reviewed
Konferenzbeiträge, peer-reviewed
Weitere Publikationen
Mündliche Konferenzbeiträge und Abstracts

Publikationen vor Tätigkeit an der ZHAW

  • • T. Ott and R. Stoop; Benefits and Pitfalls of Belief Propagation-mediated Superparamagnetic Clustering. Physical Review E 74 (4): 042103, 2006. • M. Christen, A. Nicol, K. Kendrick, T. Ott and R. Stoop; Odour Encoding in Olfactory Neuronal Networks Beyond Synchronisation. Neuroreport, 17 (14): 1499-1503, 2006. • M. Christen, A. Kohn, T. Ott and R. Stoop; Measuring Spike Pattern Reliability with the Lempel-Ziv Distance. Journal of Neuroscience Methods, 156: 342-350, 2006. • T. Ott and R. Stoop; The Neurodynamics of Belief Propagation on Binary Markov Random Fields. Advances in Neural Information Processing Systems NIPS ’06, 2006. • R. Stoop and T. Ott; Towards a Quantitative Theory of Biocomputation. Proc. of NOLTA ’06 Bologna, 2006. • N. Stoop, T. Ott and R. Stoop; Loopy Belief Propagation: Benefits and Pitfalls on Ising-like Systems. Proc. of NOLTA ’06 Bologna, 2006. • T. Ott, A. Kern and R. Stoop; Faster Spike Sorting with Belief Propagation. Proc. of NOLTA ’06 Bologna, 2006.
  • • A. Kern, T. Ott and R. Stoop; Acoustic Source Separation by Atomic Signal Decomposition. Proceedings of NOLTA ’06 Bologna, 2006. • A. Kern, T. Ott and R. Stoop; Source Separation by Atomic Signal Decomposition. Proc. of NDES’06, p. 77-80, 2006. • T. Ott, M. Christen and R. Stoop; An Unbiased Clustering Algorithm Based on Self-organisation processes in Spiking Neural Networks. Proc. of NDES’06, p. 143-146, 2006. • Y. Uwate, T. Ott, R. Stoop and Y. Nishio; Performance of Feedforward Neural Network with External Influence Function for Back Propagation Learning. RISP InternationalWorkshop on Nonlinear Circuits and Signal Processing (NCSP’06), 2006. • T. Ott, A. Kern, W.-H. Steeb and R. Stoop; Sequential Clustering: Tracking Down the Most Natural Clusters. Journal of Statistical Mechanics: theory and experiment: P11014, 2005.
  • • M. Christen, T. Ott, A. Kern, N. Stoop and R. Stoop; Periodic Economic Cycles: The Effect of Evolution and Control. Journal of Statistical Mechanics: theory and experiment: P11013, 2005. • T. Ott, M. Frey, R. Stoop; Criticality and Computation in Random Threshold Networks with Noise. Proc. of NOLTA 2005 Bruges, 2005. • T. Ott, J. Dauwels and R. Stoop; Sequential Clustering by Loopy Belief Propagation. Proc. of ECCTD 2005 Cork, 2005. • T. Ott, A. Kern, A. Schuffenhauer, M. Popov, P. Acklin, E. Jacoby and R. Stoop; Sequential Superparamagnetic Clustering for Unbiased Classification of High-dimensional Chemical Data. Journal of Chemical Information and Computer Sciences, 44 (4): 1358-1364, 2004.
  • • J.-J. van der Vyver, M. Christen, N. Stoop, T. Ott, W.-H. Steeb and R. Stoop; Towards Genuine Machine Autonomy. Robotics and Autonomous Systems, 46 (3): 151-157, 2004. • T. Ott and R. Stoop; Human-like Perception Using Sequential Superparamagnetic Clustering. Proceedings of NOLTA’04 Fukuoka, 2004. • T. Ott, W.-H. Steeb and R. Stoop; The Stochastic Network Approach to Clustering. Proc. of NOLTA’04 Fukuoka, 2004.   • M. Christen, T. Ott and R. Stoop; Spike Train Clustering Using a Lempel-Ziv Complexity-based Distance Measure. Proc. of NOLTA’04 Fukuoka, 2004.