Institut f�r Grundlagen der Informationsverarbeitung
(708)

**O.Univ.-Prof. Dr. Wolfgang Maass**

Office hours: by appointment (via e-mail)

E-mail: maass@igi.tugraz.at

Homepage: https://igi-web.tugraz.at/people/maass/

Location: IGI-seminar room, Inffeldgasse 16b/I, 8010 Graz

Date: starting on 2nd of March 2011, 4.15 p.m.

In addition
talks can be presented on the following material, that is closely
related to research projects at our Institute. Some of these
topics could also serve as first step towards a Master project or
-thesis.

The remaining slots for talks (two 40-minute talks at each
seminar meeting) will be assigned at the Organization meeting on
march 2.

If you are particularly interested in a particular one of these
topics (or want to propose an additional one) you can also send
anytime email to Wolfgang Maass.

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Probabilistic Inference in Graphical Models:

Selected sections from the book:

Koller-Friedman, Probabilistic Graphical Models MIT-Press,
2009,

chosen with regard to their possible relevance for our
research:

(I would like to suggest, that most talks in this seminar should
cover material from this book, and/or alternative sources for the
same material)

--parts of ch. 13 on MAP inference

--ch. 15: Inference in Temporal Models

--pp 741-754: Bayesian parameter estimation in Bayesian
Networks

--parts of ch. 18: Structure learning in Bayesian networks

--ch. 19: Partially Observed Data

--ch. 20: Learning Undirected Models

--ch. 23: Structured Decision Problems

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16.03.11: Gerhard Neumann, Slides,

06.04.11: Dejan Pecevski

13.04.11: Stefan Klampfl,
Slides, Bernhard Nessler,Slides

11.05.11: Johannes Bill, Slides,
David Kappel,
Slides,

20.05.11:
12.00 - 14.00h - Brown-Bag-Seminar

Stefan H�usler,
Slides,
Stefan Habenschuss,
Slides,

25.05.11: Jing Fang, Slides,

01.06.11: Sabine Schneider,
Slides, Tim
Genewein,
Slides,

15.06.11: Patrick Ofner, Gernot
Griesbacher,Slides, Zeno
Jonke, Slides,

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Applications of Probabilistic Inference in Motor Control for
Robots etc

--Tobias Lang, Marc Toussaint (2010): Probabilistic backward and
forward reasoning in stochastic relational worlds. 26th
International Conference on Machine Learning (ICML
2010).

--Marc Toussaint: Approximate inference control
(preprint),

[outlines a promising approach for solving motor control tasks
via Bayesian networks, shows that stochastic optimal control is a
special case of this approach, an MA-thesis in this direction
could complement already ongoing work in this direction at
IGI]

--Tobias Lang, Marc Toussaint (2010): Planning with Noisy
Probabilistic Relational Rules. Journal of Artificial
Intelligence Research, 39, 1-49.

http://www.jair.org/media/3093/live-3093-5172-jair.pdf

[very interesting new approach involving both probabilistic
inference and relational rules; an MA-thesis in this direction
could complement already ongoing work in this direction at IGI
]

--SJ Gershman, Y Niv: Learning latent structure: carving nature
at its joints, Current Opinion in Neurobiology, 2010
http://www.sciencedirect.com/science/article/pii/S0959438810000309

[This paper provides very important new ideas for robot learning
etc, exploiting learning simultaneously on several layers of
abstraction, on which we will work at our Institute; the paper
itself review primarily related evidence from biological motor
control. In conjunction with some cited papers this review paper
would also provide material for more than 1 talk. This paper
would provide a good basis for a MA-thesis on the development of
methods for fast robot learning.]

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Computational
Neuroscience

--Litvak, Ullman, Cortical circuitry implementing graphical
models von. Neural Computation, 2009.
https://www.mitpressjournals.org/doi/abs/10.1162/neco.2009.05-08-783

--S. Shinomoto et al, Relating neuronal firing patterns to
functional differentiation of cerebral cortex, PLOS Computational
Biology 2009

http://www.jneurosci.org/cgi/reprint/29/10/3233

--W. Li, V. Piech, and C. D Gilbert. Perceptual learning and
top-down influences in primary visual cortex. Nature
Neuroscience, 7(6):651-657,
2004.http://www1.nin.knaw.nl/viscog/temp/learning%20v1%20Gilbert.pdf
http://www1.nin.knaw.nl/viscog/temp/learning v1
Gilbert.pdf

[This paper provides the main background for a potential
MA-thesis (supervised by Dr. Legenstein). MA thesis short
description: "Self-organization of task-dependent computation in
sensory circuits". Results from Li, Piech, and Gilbert (2004)
show that neurons in primary visual cortex can drastically and
rapidly change their feature preferences in dependence of the
currently performed task. This ability suggests that sensory
cortical areas act as adaptive processors, performing different
calculations according to the immediate perceptual demands
(Gilbert et al. 2009). Such complex neuronal behavior is probably
a result of local neural circuits that exploit nonlinear
dendritic properties of single neurons as well as feedback from
other cortical areas. This MA thesis will explore how feedback
indicating the current task can influence the computation of
local circuits of spiking neurons and how the computation can be
self-organized through synaptic plasticity mechanisms.

Further readings: C. D. Gilbert, W. Li, and V. Piech.
Perceptual learning and adult cortical plasticity .
The Journal of Physiology, pages

2743-2751, 2009. (review)

M. Sigman and C. D. Gilbert. Learning to find a shape. Nature
Neurosci., 2000.]

--R. Pascanu and H. Jaeger, A Neurodynamical Model for Working
Memory

Preprint, 2010

--A Mazzoni, S Panzeri, N Logothetis, N Brunel (2008) Encoding of
Naturalistic Stimuli by Local Field Potential Spectra in Networks
of Excitatory and Inhibitory Neurons, PLOS Comp. Biol.,
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000239

--A. Mazzoni, K. Whittingstall, N. Brunel, N. K. Logothetis, S.
Panzeri

Understanding the relationships between spike rate and
delta/gamma frequency bands of LFPs and EEGs using a local
cortical network model

NeuroImage 52, 956-972, 2010

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Applications of
Probabilistic Inference in Cognitive Science (with the prospect
of porting similar capabilities into machines)

Theory acquisition as stochastic search. T. D. Ullman, N.
D. Goodman and J. B. Tenenbaum (2010). Proceedings of the
Thirty-Second Annual Conference of the Cognitive Science
Society http://web.mit.edu/tomeru/www/papers/tlss2010.pdf

Kemp, C., Goodman, N. & Tenenbaum, J. (2010). Learning to
learn causal models. Cognitive Science, 34(7),1185-1243
http://www.psy.cmu.edu/~ckemp/papers/kempgt10_learningtolearncausalmodels.pdf

Theory acquisition and the language of thought. C. Kemp, N. D.
Goodman, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth
Annual Conference of the Cognitive Science Society. http://repository.cmu.edu/psychology/967/

Learning Structured Generative Concepts. A. Stuhlmueller, J. B.
Tenenbaum, and N. D. Goodman (2010). Proceedings of the
Thirty-Second Annual Conference of the Cognitive Science
Society.http://www.mit.edu/~ast/papers/structured-generative-concepts-cogsci2010.pdf

Shi, L., Feldman, N. H., & Griffiths, T. L. (2008).

Performing Bayesian inference with exemplar models. Proceedings
of the 30th Annual Conference of the Cognitive Science
Society http://cocosci.berkeley.edu/tom/papers/exemplar.pdf

(more details are in Shi, L., Griffiths, T. L., Feldman, N.
H, & Sanborn, A. N. (in press). Exemplar models as a
mechanism for performing Bayesian inference. Psychonomic Bulletin
& Review. http://cocosci.berkeley.edu/tom/papers/mechanism.pdf
)

rh/13.10.11