Arbeitsgruppe 1: Konzepte der theoretischen Neurowissenschaft: Physik trifft Gehirn

Leiter der Arbeitsgruppe: Dr. Tatjana Tchumatchenko (tatjana.tchumatchenko@brain.mpg.de), Tim Herfurth (tim.herfurth@brain.mpg.de) , Marc Leonhardt (marc.leonhardt@brain.mpg.de)

Zeit: 10. bis 22. August 2015

Vorträge:
Die Dauer der Vorträge inklusive Diskussion: 50 Minuten

Die pdf Versionen der Vorträge werden im Laufe der Akademie gesammelt und allen Teilnehmern zur Verfügung gestellt.

Organisation: Bitte emailen Sie die Vorträge im pdf Format per E-Mail an den jeweiligen Betreuer ihres Vortrages. Dabei wäre es nett, wenn Sie die Dateien Größe unter 3 MB halten und die Dateien nach dem "Nr_Titel_Nachname.pdf" Muster benennen.

Zeitplan

Reihenfolge

Tag

Titel/Thema

Literatur

Vortragende/r

Betreuer

1

Tag 1
(11.08.2015)

Biologie der Neuronen und Synapsen

1, 3

JD

Tim Herfurth

2

Tag 1

Techniken der Steuerung und Aufnahme der neuronalen Aktivität

1, 3, 7, 8

OR

Tim Herfurth

3

Tag 2

Überblick über die Anatomie des Gehirns und speziell der Hirnrinde

1, 3, 6

MM

Tim Herfurth

4

Tag 2

Modellbeschreibung eines Neurons

2, 4

LS

Tim Herfurth

5

Tag 3

Synaptische Plastizität

1, 3, 10, 11

GF

Tim Herfurth

6

Tag 3

Mean field Ansatz im neuronalen Kontext

4, 22-26

BC

Marc Leonhardt

7

Tag 4

“Balanced Networks”: Ein Modell kortikaler Aktivität

4, 25, 27-31

YP

Marc Leonhardt

8

Tag 4

Gruppenarbeit: Gruppe 1 (Mathematische Rechnungen), Gruppe 2 (Simulationen eines Neurons) 

 

Alle

Tatjana Tchumatchenko, Tim Herfurth, Marc Leonhardt

9

Tag 5

Einführung in das Hopfield Modell

14, 15, 16

TF-H

Tim Herfurth

10

Tag 5

Ising Modell aus der Sicht der Physik

23, 24, 32

Marc Leonhardt

11

Tag 6

Ising Modell angewendet auf Neurowissenschaft

33-36

MG

Marc Leonhardt

12

Tag 6

Informationstheorie: Betrachtung einzelner Neuronen

2, 5, 19, 21

GS

Tim Herfurth

13

Tag 7

Informationstheorie in neuronalen Netzwerken

5, 18, 17, 20

RJ

Tim Herfurth

14

Tag 7

Logische Gatter als kleinste Recheneinheit eines Computers

14, 37

PF

Marc Leonhardt

15

Tag 8

Klassifizierung mit Hilfe des Perceptron Modells und die Rolle logischer Gatter

14, 38-42

DS

Marc Leonhardt

16

Tag 8

Einführung in Machine Learning und Klassifikationsalgorithmen

38, 39, 41-43

FT

Marc Leonhardt

17

Tag 9

Training von feed-forward Netzwerken am Beispiel von Backpropagation

38, 39, 41,43

GK

Marc Leonhardt

18

Tag 9

Vorstellung und Diskussion der Anwendungen des Machine Learning

43-48

DD

Marc Leonhardt

19

Tag 9

Diskussion und Review über alle Vorträge

-

Tatjana Tchumatchenko

-

[1] Eric R Kandel, James H Schwartz, and Thomas M Jessell. Principles of Neural Science, volume 4. 2000. [ bib | DOI | http ]
[2] Peter Dayan and L F Abbott. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. 2001. [ bib | DOI ]
[3] Mark F Bear, Barry W Connors, and Michael A Paradiso. Neuroscience: Exploring the brain (3rd ed.). 2007. [ bib | http ]
[4] Wulfram Gerstner and Werner M Kistler. Spiking Neuron Models: Single Neurons, Populations, Plasticity. 2002. [ bib | DOI ]
[5] F. Rieke, D. Warland, R. De Ruyter Van Steveninck, and W. Bialek. Spikes: Exploring the Neural Code, volume 20. 1997. [ bib ]
[6] Stanley Jacobson and Elliott M Marcus. Neuroanatomy for the neuroscientist. 2008. [ bib | DOI | www: ]
[7] Areles Molleman. Patch Clamping: An Introductory Guide to Patch Clamp Electrophysiology. 2003. 2003. [ bib | DOI | http ]
[8] U Windhorst and H Johansson. Modern Techniques in Neuroscience Research. 1999. [ bib | http ]
[9] Eugene M Izhikevich. Dynamical Systems in Neuroscience. 2007. [ bib | DOI | .pdf ]
[10] L F Abbott and S B Nelson. Synaptic plasticity: taming the beast. Nat. Neurosci., 3 Suppl:1178-1183, 2000. [ bib | DOI ]
[11] Ami Citri, Ami Citri, Robert C Malenka, and Robert C Malenka. Synaptic plasticity: multiple forms, functions, and mechanisms. Neuropsychopharmacology, 33(1):18-41, 2008. [ bib | DOI | http ]
[12] H K Khalil. Nonlinear Systems, Third Edition. 2002. [ bib | DOI | http ]
[13] Steven H. Strogatz. Nonlinear Dynamics and Chaos. In Book, pages 1-505. 1994. [ bib | DOI ]
[14] R Rojas. Neural networks: a systematic introduction. 1996. [ bib | DOI | http ]
[15] J Hertz, A Krogh, and R G Palmer. Introduction to the Theory of Neural Computation, volume 1. 1991. [ bib | http ]
[16] Satish Kumar. Neural networks: a classroom approach. Tata McGraw-Hill Education, 2004. [ bib ]
[17] Elad Schneidman, Susanne Still, Michael J. Berry, and William Bialek. Network information and connected correlations. Phys. Rev. Lett., 91:238701, Dec 2003. [ bib | DOI | http ]
[18] Bruno B Averbeck, Peter E Latham, and Alexandre Pouget. Neural correlations, population coding and computation. Nat. Rev. Neurosci., 7(5):358-366, 2006. [ bib | DOI ]
[19] A Borst and F E Theunissen. Information theory and neural coding. Nat. Neurosci., 2(11):947-957, 1999. [ bib | DOI ]
[20] S Panzeri, S R Schultz, A Treves, and E T Rolls. Correlations and the encoding of information in the nervous system. Proc. Biol. Sci., 266(1423):1001-1012, 1999. [ bib | DOI ]
[21] David J C Mackay. Information Theory , Inference , and Learning Algorithms. Learning, 22(3):348-349, 2003. [ bib | DOI | http ]
[22] Paul C Bressloff. Spatiotemporal dynamics of continuum neural fields. Journal of Physics A: Mathematical and Theoretical, 45(3):033001, 2011. [ bib | DOI ]
[23] L. E. Reichl. A Modern Course in Statistical Physics, 2nd Edition, 1999. [ bib | DOI ]
[24] Franz Schwabl. Statistische Mechanik. 2006. [ bib | DOI | http ]
[25] Wulfram Gerstner, Werner M Kistler, Richard Naud, and Liam Paninski. Neuronal Dynamics - From Single Neurons to Networks and Models of Cognition. Cambridge University Press, Cambridge, new. edition, 2014. [ bib ]
[26] G Bard Ermentrout and David H Terman. Mathematical Foundations of Neuroscience -. Springer Science & Business Media, Berlin Heidelberg, 2010. aufl. edition, 2010. [ bib ]
[27] Jeffry S. Isaacson and Massimo Scanziani. How inhibition shapes cortical activity. Neuron, 72(2):231-243, 2011. [ bib | DOI | http ]
[28] Michael Okun and Ilan Lampl. Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nature neuroscience, 11(5):535-537, 2008. [ bib | DOI ]
[29] M N Shadlen and W T Newsome. The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. The Journal of neuroscience : the official journal of the Society for Neuroscience, 18(10):3870-3896, 1998. [ bib ]
[30] C van Vreeswijk and H Sompolinsky. Chaotic balanced state in a model of cortical circuits. Neural computation, 10(6):1321-1371, 1998. [ bib | DOI ]
[31] C van Vreeswijk and H Sompolinsky. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science (New York, N.Y.), 274(5293):1724-1726, 1996. [ bib | DOI ]
[32] Barry A. Cipra. An Introduction to the Ising Model. [ bib ]
[33] Elad Schneidman, Michael J Berry, Ronen Segev, and William Bialek. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440(7087):1007-1012, 2006. [ bib | DOI | arXiv ]
[34] Yair Shemesh, Yehezkel Sztainberg, Oren Forkosh, Tamar Shlapobersky, Alon Chen, and Elad Schneidman. High-order social interactions in groups of mice. eLife, 2013(2):1-19, 2013. [ bib | DOI ]
[35] Aonan Tang, David Jackson, Jon Hobbs, Wei Chen, Jodi L Smith, Hema Patel, Anita Prieto, Dumitru Petrusca, Matthew I Grivich, Alexander Sher, Pawel Hottowy, Wladyslaw Dabrowski, Alan M Litke, and John M Beggs. A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. The Journal of neuroscience : the official journal of the Society for Neuroscience, 28(2):505-518, 2008. [ bib | DOI ]
[36] Gasper Tkacik, Elad Schneidman, Michael J. Berry, and William Bialek. Spin glass models for a network of real neurons. (1):1-15, 2009. [ bib | arXiv | http ]
[37] David Harris and Sarah Harris. Digital Design and Computer Architecture -. Morgan Kaufmann Publishers, San Francisco, 2007. [ bib ]
[38] Christopher M Bishop. Pattern Recognition and Machine Learning, volume 4. 2006. [ bib | DOI | arXiv | .pdf ]
[39] Tom M Mitchell. Machine Learning, volume 1. 1997. [ bib | DOI | http ]
[40] Simon Haykin. Neural Networks and Learning Machines, volume 3. 2008. [ bib | http ]
[41] Scholarpedia Computational Neuroscience. [ bib | http ]
[42] Scholarpedia Computational Intelligence. [ bib | http ]
[43] Andrew Ng. Machine Learning. [ bib | http ]
[44] Dario Floreano and Robert J. Wood. Science, technology and the future of small autonomous drones. Nature, 521(7553):460-466, 2015. [ bib | DOI | http ]
[45] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436-444, 2015. [ bib | DOI | http ]
[46] Michael L. Littman. Reinforcement learning improves behaviour from evaluative feedback. Nature, 521(7553):445-451, 2015. [ bib | DOI | http ]
[47] Daniela Rus and Michael T. Tolley. Design, fabrication and control of soft robots. Nature, 521(7553):467-475, 2015. [ bib | DOI | http ]
[48] S Russel. Ethics of artificial intelligence. Nature, 2015. [ bib ]

This file was generated by bibtex2html 1.96.