Institut f�r Grundlagen der Informationsverarbeitung
Leaders of the Seminar:
Assoc. Prof. Dr. DI Robert Legenstein
O.Univ.-Prof. Dr. Wolfgang Maass
Office hours: by appointment (via e-mail)
Papers related to Principles of Brain Computation/Human Brain Projec
Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T.,
DeWolf, T., Tang, C., & Rasmussen, D. (2012). A large-scale
model of the functioning brain. Science, 338(6111),
Supplementary Material PDF
Commentary: Machens, C. K. (2012). Building the Human Brain. Science, 338(6111), 1156-1157. PDF
Three presentations should cover this material.
2. A model for stochastic computation in cortical microcircuits (new paper by Habenschuss, Jonke, Maass), preprint will be made available to seminar participants
This paper should be covered by 2 talks (one on stationary distributions of network states, one on Sudoku application).
3. Larkum, M. (2012). A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex. Trends in Neurosciences. PDF
One talk can cover this paper.
Sporns, Olaf. "Network attributes for segregation and
integration in the human brain." Current opinion in neurobiology
One talk can cover this paper.
S. Haeusler and W. Maass. A statistical analysis of
information processing properties of lamina-specific cortical
microcircuit models. Cerebral Cortex, 17(1):149-162,2007.
One or two talks can be given on this paper. (Possibly the talks can cover also some related new results for a column model of the Human Brain Project)
Papers related to machine learning and artificial neural networks:
1. Neal, R. (1992). Connectionist learning of belief networks. Artificial Intelligence, 56: 71-113
This paper introduces sigmoidal belief networks and relates them to Boltzmann machines. This talk should be the basis for a later talk on deep belief networks. (The PDF is not freely available. It will be provided to seminar participants)
2. Hinton, G. E., Osindero, S. and Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation 18, pp 1527-1554. 2006.
Introduces greedy-layerwise training in deep belief
3. Salakhutdinov R. (2010). Learning Deep Boltzmann Machines using Adaptive MCMC. Proc. of the 27th International Conference on Machine Learning.
Introduces a novel way to
learn deep Boltzmann machines (as opposed to DBNs).
4. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. and Salakhutdinov, R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. Technical Report.
Introduces a novel
technique to prevent overfitting in deep feed-forward neural
5. G. M. Hoerzer, R. Legenstein, and Wolfgang Maass (2012). Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. Cerebral Cortex.
This paper is related to Echo State Networks that were discussed briefly in NNA, but it is extended and discussed in a biological context.
Papers related to machine learning and robotics research for the EU-project AMARSi:
1. Ijspeert, A.;Nakanishi, J.; Pastor, P; Hoffmann, H.; Schaal, S. (2013). Dynamical Movement
Primitives: Learning Attractor Models for Motor Behaviors, Neural Computation, 25, pp.328�373.
This journal paper provides a detailed discussion on parametrized elementary movements using dynamical systems. This approach is the most widely used movement primitive representation in robotics.
2. Muelling, K.; Kober, J.; Kroemer, O.; Peters, J. (2013). Learning to Select and Generalize Striking
Movements in Robot Table Tennis, International Journal of Robotics Research
This paper presents
a motor skill learning approach implementing a movement primitive
library. In impressing exeriments the authors show how a robot
learns to play table tennis.
3. Matthew Botvinick, Marc Toussaint (2012): Planning as Inference. Trends in Cognitive Sciences,
16(10), 485-488, 2012.
Paper on how the human brain may implement motor planning. The authors discuss recent advances in motor planning using probabilistic models.
Can be combined with a more theoretical machine learning paper, i.e. Konrad Rawlik, Marc Toussaint, Sethu Vijayakumar: On stochastic optimal control and reinforcement learning by approximate inference. Int. Conf. on Robotics Science and Systems (R:SS 2012). Best paper runner up award. http://ipvs.informatik.uni-stuttgart.de/mlr/marc/publications/12-rawlik-toussaint-vijayakumar-RSS.pdf
4. Quadrupet Robots and Motor Control Approaches: The following three papers should be
combined. The discuss robot modelling appraoches and trajectory formation strategies:
Sproewitz A, Kuechler L, Tuleu A, Ajallooeian M, D�Haene M, Moeckel R, Ijspeert AJ. (2011): Oncilla Robot, A Light-weight Bio-inspired Quadruped Robot for Fast Locomotion in Rough Terrain. In: Symposium on Adaptive Motion of Animals and Machines (AMAM2011). Symposium on Adaptive Motion of Animals and Machines (AMAM2011). ; 2011. p. 63-64.
A. Sproewitz, M. Fremerey, K. Karakasiliotis, S. Rutishauser and L. Righetti (2009). Compliant Leg Design for a Quadruped Robot. Dynamic Walking 2009, Vancouver, Canada.
S. Rutishauser, A. Sproewitz, L. Righetti and A. J. Ijspeert (2008). Passive compliant quadruped robot using central pattern generators for locomotion control. International Conference on Biomedical Robotics and Biomechatronics, Scottsdale, 2008.