View on GitHub

Third workshop on advanced methods in theoretical neuroscience

June 27-29, 2018 • Göttingen, Germany

Schedule

Tuesday, June 26 [Tutorials]

09:00-09:15 Greetings and opening remarks
09:15-10:30 Dynamics in networks with excitatory and inhibitory balance [Farzad]
10:30-11:00 Break
11:00-12:30 Quantifying chaos in neural circuits [Rainer]
12:30-14:00 Lunch break
14:00-15:30 Statistical physics of learning in perceptron-based networks [Jonathan]

Wednesday, June 27

09:00-09:15 Opening remarks
09:15-10:15 Robert Guetig – Margin learning in spiking neurons
10:15-11:15 Barbara Bravi – Inference of hidden stochastic trajectories in large networks
11:15-11:45 Break
11:45-12:45 Tatyana O. Sharpee – Statistical approach for mapping the space of natural odor
12:45-14:00 Lunch break
14:00-15:00 Surya Ganguli – TBA
15:00-15:30 Contributed talk
15:30-16:00 Break
16:00-17:00 Andrea K. Barreiro – Constraining neural networks with spiking statistics
17:00-18:00 David Schwab – TBA
18:00-22:00 Poster Session + dinner and drinks

Thursday, June 28

09:00-10:00 Micha Tsodyks – Retrospective Bayesian Inference in Working Memory
10:00-11:00 Friedemann Zenke – Beyond random networks: Training spiking neural networks with surrogate gradients
11:00-11:30 Break
11:30-12:30 Ran Darshan – How strong are correlations in strongly recurrent neuronal networks?
12:30-13:45 Lunch break
13:45-14:45 Gabriel Kreiman – A tale of two pathways: bottom-up and top-down processing in cortex
14:45-15:45 Claudius Gros – Testing for strong and partially predictable chaos in spiking and rate encoding neural networks
15:45-16:15 Break
16:15-17:15 Omri Barak – Towards a theory of trained recurrent neural networks
17:30-20:00 Transit to city and Gauss tour
20:00-21:20 Free time for dinner
20:00 Happy hour

Friday, June 29

09:00-10:00 Sophie Deneve – Efficient Balanced Networks
10:00-11:00 Yoram Burak – Continuous parameter working memory in stochastic and chaotic neural networks
11:00-11:30 Break
11:30-12:00 Contributed talk
12:00-13:00 Fred Wolf - State manifolds for the plasticity of recurrent neural circuits in the visual cortex
13:00-14:00 Lunch Break
14:00 Back to town

Poster session — June 27

  Name Affiliation Title
1 David Kappel Georg-August Universität Göttingen Synaptic sampling for reward-based learning and rewiring of neural circuits
2 Sadique Sheik University of California, San Diego Membrane Potential -based Unsupervised Online Learning and Detection of Temporal Gestures Captured from Event Based Sensors
3 Johannes Zierenberg Max Planck Institute for Dynamics and Self-Organization Diversity of Dynamic States in Neural Networks induced by Homeostatic Plasticity
4 Jonas Stapmanns Institute of Neuroscience and Medicine (INM-6), Juelich, Germany Field Theory for Nonlinear Stochastic Rate Neurons
5 EWANDSON LUIZ LAMEU Humboldt University Berlin Detecting phase synchronized groups in networks of chaotic bursting neurons through spatial recurrence plots
6 Liane Klein Ernst Strüngmann Institute (ESI) for Neuroscience, Frankfurt am Main, Germany Attention and Gamma Oscillation in V1
7 Cem Uran Ernst Struengmann Institute, Frankfurt, Germany How do predictive relationships in natural images modulate V1 activity?
8 Barbara Feulner Max Planck Institute for Dynamics and Self-Organization Pinwheel pattern parameterize a manifold of optimized V1 architectures
9 Fatemeh Yavari Leibniz Institute for Labor Research at the TU Dortmund Leibniz Research Center for Working Environment and Human Factors Ardeystraße 67 To find the optimal tDCS montage in an individualized manner
10 Ludovica Bachschmid-Romano Duke University, Durham, North Carolina A statistical physics approach to learning curves for the inverse Ising problem
11 Friedrich Schuessler Technion, Haifa, Israel An Analytical Theory for Spiking Neurons driven by Colored Noise
12 Lisandro Montangie Max Planck Institute for Brain Research, Frankfurt am Main, Germany Self-organization under the triplet spike timing dependent plasticity rule
13 Dimitris Pinotsis University of London-City and MIT New models for Computational Psychiatry
14 Natalie Schieferstein Humboldt-Universität zu Berlin Modeling hippocampal ripple oscillations across scales: limitations of recurrent interneuron network models at the mesoscopic level
15 Emma Roscow University of Bristol, Bristol, UK Biasing hippocampal replay for reinforcement learning
16 Katharine Shapcott Ernst Strüngmann Institute Magnocellular influence on gamma band synchrony in area V1
17 Oleg Vinogradov 1) University of Tuebingen, 2) Max Planck Institute for Biological Cybernetics, Tübingen, Germany Bursting behavior in sparse random networks of excitatory and inhibitory neurons
18 Xavier Hinaut Inria A Simple Reservoir Model of Working Memory with Real Values
19 Bineet Kumar Gupta Shri Ramswaroop Memorial University InfoSec Sense Problem: A Big Data for Neuroinformation
20 Dylan Richard Muir aiCTX AG, Zürich, Switzerland Recurrent network computation subject to implementation constraints
21 Christian Keup Juelich Research Centre Dynamics of Cell Assemblies in Binary Neuronal Networks
22 Samuel Ocko Stanford University Emergent Elasticity in the Neural Code for Space
23 Sven Goedeke University of Bonn, Germany Growing critical: Self-organized criticality in a developing neural system
24 wenqi wu max planck institute for dynamics and self-organization,goettingen,germany A detailed Hebbian-learning model for orientation map development
25 Björn Mattes Max Planck Institute for Brain Research, Frankfurt am Main, Germany Advances in dynamic modeling
26 Siwei NIH, Bethesda, USA Finite size effects for spiking neural networks with spatially dependent coupling
27 Christoph Miehl Max Planck Institute for Brain Research, Frankfurt am Main, Germany Interaction of excitatory and inhibitory plasticity in a feedforward network model
28 Lee Susman Technion Stable memory with unstable synapses
29 Sepehr Mahmoudian Goettingen Partial Information as a window to understanding information processing in the brain
30 Alexander Antrobus Gatsby Unit, UCL, UK Inference of optimal synaptic weights from correlated signals
31 Conor Heins MPI-DS Latent variable models as a targeted approach for identifying neuronal ensembles
32 Mohammad Bashiri Technical University of Munich, Munich, Germany The Effect of Non-invasive Deep Brain Stimulation Using Temporal Interference: A Computational Study
33 Nimrod Shaham The Hebrew university of Jerusalem, Israel Neural network mechanism for fixational eye motion generation
34 Chen Beer Technion Line attractor formation as a case of sequential learning