Back to Teaching

Internationale Sommerakademie der Studienstiftung in Rot an der Rot

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

Tag Titel/Thema Literatur Vortragende/r Betreuer
Tag 1
(11.08.2015)
Biologie der Neuronen und Synapsen 1, 3 JD Tim Herfurth
Tag 1 Techniken der Steuerung und Aufnahme der neuronalen Aktivität 1, 3, 7, 8 OR Tim Herfurth
Tag 2 Überblick über die Anatomie des Gehirns und speziell der Hirnrinde 1, 3, 6 MM Tim Herfurth
Tag 2 Modellbeschreibung eines Neurons 2, 4 LS Tim Herfurth
Tag 3 Synaptische Plastizität 1, 3, 10, 11 GF Tim Herfurth
Tag 3 Mean field Ansatz im neuronalen Kontext 4, 22-26 BC Marc Leonhardt
Tag 4 “Balanced Networks”: Ein Modell kortikaler Aktivität 4, 25, 27-31 YP Marc Leonhardt
Tag 4 Gruppenarbeit: Gruppe 1 (Mathematische Rechnungen), Gruppe 2 (Simulationen eines Neurons)    Alle Tatjana Tchumatchenko, Tim Herfurth, Marc Leonhardt
Tag 5 Einführung in das Hopfield Modell 14, 15, 16 TF-H Tim Herfurth
Tag 5 Ising Modell aus der Sicht der Physik 23, 24, 32 Marc Leonhardt
Tag 6 Ising Modell angewendet auf Neurowissenschaft 33-36 MG Marc Leonhardt
Tag 6 Informationstheorie: Betrachtung einzelner Neuronen 2, 5, 19, 21 GS Tim Herfurth
Tag 7 Informationstheorie in neuronalen Netzwerken 5, 18, 17, 20 RJ Tim Herfurth
Tag 7 Logische Gatter als kleinste Recheneinheit eines Computers 14, 37 PF Marc Leonhardt
Tag 8 Klassifizierung mit Hilfe des Perceptron Modells und die Rolle logischer Gatter 14, 38-42 DS Marc Leonhardt
Tag 8 Einführung in Machine Learning und Klassifikationsalgorithmen 38, 39, 41-43 FT Marc Leonhardt
Tag 9 Training von feed-forward Netzwerken am Beispiel von Backpropagation 38, 39, 41,43 GK Marc Leonhardt
Tag 9 Vorstellung und Diskussion der Anwendungen des Machine Learning 43-48 DD Marc Leonhardt
Tag 9 Diskussion und Review über alle Vorträge - Tatjana Tchumatchenko

Literatur

[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 ]

Back to Teaching