Institut
für Grundlagen der Informationsverarbeitung (7080)
Lecturer:
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/
Assoc. Prof. Dr. Robert Legenstein
Office hours: by appointment (via e-mail)
E-mail: robert.legenstein@igi.tugraz.at
Homepage: www.igi.tugraz.at/legi/
Marblestone, Adam, Greg Wayne, and Konrad Kording (2016). "Toward an Integration of Deep Learning and Neuroscience." Frontiers in Computational Neuroscience, 10. [PDF@Frontiers].
hypothesizes that biological neuronal systems may utilize learning processes that share similarities with deep learning techniques. We will discuss in this semester this paper and selected references therein.Abstract: Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) these cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.
(1) R. C. O’Reilly, D. Wyatte, and J. Rohrlich
(2014). Learning through time in the thalamocortical loops.
arXiv:1407.3432v1. https://arxiv.org/abs/1407.3432
(2) W. Lotter, G. Kreiman, and D. Cox (2016).
Unsupervised learning of visual structure using predictive
generative networks. arXiv:1511. http://arxiv.org/abs/1511.06380
(3) R. C. O’Reilly, T. E. Hazy, J. Mollick, P.
Mackie, and S. Herd (2014). Goal-driven cognition in the brain:
A computational framework. arXiv:1404.7591v1. https://arxiv.org/abs/1404.7591
(4) J. O. Rombouts, S. M. Bohte, and P. R.
Roelfsema (2015). How attention can create synaptic tags for
the learning of working memories in sequential tasks. PLOS
Computational Biology | DOI:10.1371/journal.pcbi.1004060.
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004060
(5) G. Wayne, and L. F. Abbott (2014).
Hierarchical control using networks trained with higher-level
forward models. Neural Comput 26(10):2163-2193. doi:
10.1162/NECO_a_00639. http://www.ncbi.nlm.nih.gov/pubmed/25058706
(6) T. J. Sejnowski, H. Poizner, G. Lynch, S.
Gepshtein, and R. J. Greenspan (2014). Prospective
Optimization. Proc IEEE Inst Electr Electron Eng. 2014
May;102(5):799-811. DOI: 10.1109/JPROC.2014.2314297. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4201124/
(7) E. Jonas, and K. Kording (2016). Could a
neuroscientist understand a microprocessor?. bioRxiv, 055624.
http://www.biorxiv.org/content/early/2016/05/26/055624.abstract
(8) I. Sutskever, J. Martens, G. E. Dahl, and
G. E. Hinton (2013). On the importance of initialization and
momentum in deep learning. Proceedings of the 30 th
International Conference on Machine Learning, Atlanta, Georgia,
USA, 2013. JMLR: W&CP volume 28. http://www.cs.toronto.edu/~fritz/absps/momentum.pdf
(9) J. Yosinski, J. Clune, and Y. Bengio
(2014). How transferable are features in deep neural networks?
In Advances in Neural Information Processing Systems 27:
3320-3328.
http://papers.nips.cc/paper/5347-how-transferable-are-features-in-deep-neural-networks
(10) C. Gülcehre, and Y. Bengio (2016).
Knowledge matters: Importance of prior information for
optimization. Journal of Machine Learning Research, 17(8),
1-32.
www.jmlr.org/papers/volume17/gulchere16a/gulchere16a.pdf
|
Date |
Speaker |
Talks |
|
| 24.10.2016
16:15-18:00 |
Maass,
Legenstein |
Introduction |
PDF |
|
21.11.2016
15:45-18:00 |
Absenger,
Mulle |
Goal-driven cognition in
the brain: A computational framework. R. C. O’Reilly, T.
E. Hazy, J. Mollick, P. Mackie, and S. Herd
(2014) |
PDF |
| 28.11.2016 15:45-18:00 | Marchetto,
Raggam |
Learning through time in
the thalamocortical loops. R. C. O’Reilly, D. Wyatte, and
J. Rohrlich (2014) |
|
| Harb, Micorek | Prospective Optimization. T. J.
Sejnowski, H. Poizner, G. Lynch, S. Gepshtein, and R. J.
Greenspan (2014) |
PDF |
|
| 12.12.2016 15:45-18:00 | Steger,
Zöhrer |
Unsupervised learning of visual structure using predictive generative networks. W. Lotter, G. Kreiman, and D. Cox (2016) |
PDF |
| Wohlhart, Müller |
Could a neuroscientist understand a microprocessor?. E. Jonas, and K. Kording (2016) |
PDF |
|
| 09.01.2017 15:45-18:00 |
Legenstein |
A brief introduction
into deep learning |
|
| Lindner, Narnhofer |
Knowledge matters: Importance of prior information for optimization. C. Gülcehre, and Y. Bengio (2016) |
PDF |
|
|
23.01.2017
15:45-18:00 |
Topic,
Eibl |
On the importance of initialization and momentum in deep learning. I. Sutskever, J. Martens, G. E. Dahl, and G. E. Hinton (2013) |
PDF |
| Fuchs,
Ainetter |
How transferable are features in deep neural networks? J. Yosinski, J. Clune, and Y. Bengio (2014) |
PDF |