For up to date information on dates and topics see the corresponding google document.

Note: The course will be take place in a hybrid fashion using zoom (see link below) and in-person. If you would like to attend the seminar and have not registered yet please contact Jochen Triesch and ask for the password.

Organization: Jochen Triesch

Seminar: Mondays, 10:00 - 11:30

Room: FIAS 0.200 - Zoom Meeting, Link

Overview

"Current Topics in Theoretical Neuroscience" serves a dual purpose as our weekly group seminar, where members of the lab present their current research and as a seminar open for students to delve into the fascinating world of theoretical neuroscience.

The theme for the research articles for this semester will be "Towards Conscious AIs"

Date	Title	Presenter
19.10.23	Scheduling of the Seminar	Group
26.10.23	Research Talk	Francisco
	Paper Talk	Marius
02.11.23	Research Talk	Marcel
	Research Talk	Timothy
09.11.23	Research Talk	Mitra
	Paper Talk	None
16.11.23	Research Talk	None
	Paper Talk	Xia
23.11.23	Research Talk	Petros
	Paper Talk	Zhengyang
30.11.23	Research Talk	Sojeong
	Paper Talk	Philip
07.12.23	Research Talk	Zhengyang
	Paper Talk	Theresa
15.12.23	Research Talk	Xia
	Paper Talk	Arthur
21.12.23	Research Talk	Antony
	Paper Talk	TBA
11.01.24	Research Talk	Marius
	Paper Talk	Marcel
18.01.24	Research Talk	Philip
	Paper Talk	Petros
25.01.24	Research Talk	Theresa
	Paper Talk	Francisco
01.02.24	Jochen Travelling	No Seminar
08.02.24	Research Talk	TBA
	Paper Talk	Markus?
End of Semester

For up to date information on dates and topics see the corresponding google document.

Note: due to the COVID-19 pandemic, the course will be take place in a hybrid fashion using zoom (see link below) and in-person. If you would like to attend the seminar and have not registered yet please contact Jochen Triesch and ask for the password.

Organization: Jochen Triesch

Seminar: Mondays, 10:00 - 11:30

Room: Zoom Meeting, Link

Overview

"Current Topics in Theoretical Neuroscience" serves a dual purpose as our weekly group seminar, where members of the lab present their current research and as a seminar open for students to delve into the fascinating world of theoretical neuroscience.

The theme for the research articles for this semester will be "Memory Systems"

Date	Title	Presenter
11.04.22	Scheduling of the Seminar	Group
18.04.22	Easter Monday	No seminar
25.04.22	Research Talk	Kshitij
	Gentle introduction, view from Psychology	Francisco Lopez
02.05.22	Research Talk	Arthur Aubret
	Associative Memory 1: Hopfield network	Tobias Thomas
09.05.22	Research Talk	Tristan Stöber
	Associative Memory 2: Modern Hopfield networks	TBA
16.05.22	PhD practice talk	Samuel Eckmann
	Complementary learning systems	TBA
23.05.22	Research Talk	Thomas
	Hippocampal Replay	TBA
30.05.22	Research Talk	Xia Xu
	Lifelong learning	Alex Lichtenstein
06.06.22	Whit Monday	No seminar
13.06.22	Research Talk	Dominik Mattern
	Reservoir Computing	Jan Marker
20.06.22	Research Talk	Zhengyang Yu
	Memory in Reinforcement Learning 1	TBA
27.06.22	Research Talk	Danylo Batulin
	Gated recurrent units in Deep Learning	Cedric
04.07.22	Research Talk	Felix Schneider
	Memory in Reinforcement Learning 2: Replay buffers in Deep RL	Nico Bohlinger
11.07.22	Research Talk	Judith Massmann
	Event Coding	Jannis Brugger
	End of Semester
End of Semester

Note: due to the COVID-19 pandemic, the course will be take place online using zoom (see link below). There may be in-person sessions later in the semester. If you would like to attend the seminar and have not registered yet please contact Jochen Triesch and ask for the password.

For up to date information on dates and topics see the corresponding google document.

Organization: Jochen Triesch

Seminar: Mondays, 10:00 - 11:30

Room: Zoom Meeting, Link

Overview

"Current Topics in Theoretical Neuroscience" serves a dual purpose as our weekly group seminar, where members of the lab present their current research and as a seminar open for students to delve into the fascinating world of theoretical neuroscience.

The theme for the research articles for this semester will be "unsupervised learning of object representations" and include recent contrastive learning approaches

Date	Title	Presenter
18.10.21	Scheduling of the Seminar	Group
	"SARS-CoV-2 and your brain“	Jochen Triesch
25.10.21	Research Talk	Diyuan Lu
	Krüger et al.: Deep hierarchies in the primate visual cortex: What can we learn for computer vision?	Zhengyang Yu
01.11.21	Research Talk	Francisco Lopez-Guzman
	Földiak: Learning invariance from transformation sequences	Leonie Scherer
08.11.21	Research Talk	Danylo Batulin
	Olshausen & Field: Sparse coding with an overcomplete basis set: A strategy employed by V1?	Jan Marker
15.11.21	Research Talk	Arthur Aubret
	Wiskott & Sejnowski: Slow feature analysis: Unsupervised learning of invariances	Tristan Stöber
22.11.21	Research Talk	Marius Vieth
	Rao & Ballard: Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects	Philipp Regenauer
29.11.21	Research Talk	Alex Lichtenstein
	Bambach et al.: Toddler-Inspired Visual Object Learning	Roshan Roukhosh
06.12.21	Research Talk	Felix Schneider
	Chen et al.: A simple framework for contrastive learning of visual representations	Markus Ernst
13.12.21	Research Talk	Alex Stoll
	Orhan et al.: Self-supervised learning through the eyes of a child	Francisco Lopez-Guzman
10.01.22	Research Talk	Jan Marker
	Sucevic et al.: What can babies teach us about contrastive methods?	Felix Schneider
17.01.22	Research Talk	Philip Sommer
	Wang & Isola: Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere	Enxhela Vardhami
24.01.22	Research Talk	Xia Xu
	Grill et al: Bootstrap your own latent: A new approach to self-supervised learning	Arthur Aubret
31.01.22	Research Talk	Tristan Stöber
	Liu et al.: Self-supervised Learning: Generative or Contrastive	Xia Xu
07.02.22	Research Talk	Markus Ernst
	Ali et al.: Predictive coding is a consequence of energy efficiency in recurrent neural networks	Philip Sommer
14.02.22	Research Talk	Zhenyang Yu
	TBA	TBA
End of Semester

Note: due to the COVID-19 pandemic, the course will be run fully online using zoom (see link below). If you would like to attend the seminar and have not registered yet please contact Jochen Triesch and ask for the password. For up to date information on dates and topics see the corresponding google document.

Organization: Jochen Triesch

Seminar: Mondays, 10:00 - 11:30

Room: Zoom Meeting, Link

Overview

"Current Topics in Theoretical Neuroscience" serves a dual purpose as our weekly group seminar, where members of the lab present their current research and as a seminar open for students to delve into the fascinating world of theoretical neuroscience.

Recommended background reading

Theoretical Neuroscience, P. Dayan and L. Abbott, Chapters 4 and 5

Network Neuroscience, F. Fröhlich, Chapters 1-4 and 6-8 

Date	Title	Presenter
12.04.21	Scheduling of the Seminar	Triesch Group
19.04.21	""Understanding the formation of cognitive maps""	Tristan Stöber
26.04.21	TBA	Pierre Schumacher
		Charles Wilmot
03.05.21	TBA	Thomas Barbier
	VSS practice talk	Alex Lichtenstein
10.05.21	"Predicting molecular classes in muscle-invasive bladder cancer with deep learning"	Alex Stoll
		–
17.05.21	TBA	Tristan Stöber
		–
24.05.21	No Seminar - Whitmonday
31.05.21	TBA	Danylo Batulin
		Gökhan Güney
07.06.21	TBA	Marius Vieth
		–
14.06.21	TBA	Alan Hsu
		Maria Vo
21.06.21	PhD practice talk	Tristan Stöber
		Jan Marker
28.06.21	PhD practice talk	Diyuan Lu
		Philip Sommer
05.07.21	TBA	Tomislav
		Jonas Mahn
12.07.21	TBA	Achref Jaziri
		Santiago Martinez
End of Semester

Note: due to the COVID-19 pandemic, the course will be run fully online using zoom (see link below). If you would like to attend the seminar and have not registered yet please contact Markus Ernst or Jochen Triesch and ask for the password.

Organization: Jochen Triesch

Seminar: Mondays, 10:00 - 11:30

Room: Zoom Meeting, Link

Overview

"Current Topics in Theoretical Neuroscience" serves a dual purpose as our weekly group seminar, where members of the lab present their current research and as a seminar open for students to delve into the fascinating world of theoretical neuroscience.

Recommended background reading

Theoretical Neuroscience, P. Dayan and L. Abbott, Chapters 4 and 5

Network Neuroscience, F. Fröhlich, Chapters 1-4 and 6-8 

Date	Title	Presenter
03.11.20	Scheduling and Lightning Research Talks (5min each)	Triesch Group
09.11.20	"Differentiable computing and cognitive processes"	Charles Wilmot
16.11.20	"Studying the role of neuroimmune interactions in development of epilepsy with a mathematical model"	Danylo Batulin
23.11.20	"Unsupervised Learning of Spatio-Temporal Receptive Fields from an Event-Based Vision Sensor"	Thomas Barbier
30.11.20	"Development of a Perimetry Test in Virtual Reality using Unity3D and ZEST Logic"	Ken Oehler
07.12.20	"Multiple timescale Self Organizing Recurrent Network and the SORN-Simulator"	Marius Vieth
14.12.20	"Machine learning for healthcare — a focus on epilepsy"	Diyuan Lu
Christmas Break
12.01.21	"Hierarchical Reinforcement Learning with Deep Neural Networks for Applications in Robotics"	Pierre Schumacher
	Brunel, Journal of Computational Neuroscience, 2000 (mentor: Jan)	()
18.01.21	"Deep learning in digital pathology - predicting gene mutations in muscle invasive bladder cancer"	Alex Stoll
	Zenke et al., Nature Communications, 2015 (mentor: Jan)	()
25.01.21	"Understanding Object Representations with Supervised and Unsupervised learning methods"	Markus Ernst
	Fiete et al., Neuron, 2010 (mentor: Tristan)	Vasilios Mitsiioannou
01.02.21	(no research talk)	–
	Vogels et al., Science, 2011 (mentor: Tristan)	Vanessa Mayer
08.02.21	"Synaptic lifetimes in near-critical recurrent spiking networks"	Jan Marker
	Gerstner et al., Nature, 1996 (mentor: Jan)	Kai Wo Yau
15.02.21	"Investigating Dentate Spikes in Epileptogenesis"	Tristan Stöber
	F Zenke & S Ganguli, Neural computation, 2018 (mentor: Tristan)	Quang Anh Le Hong
End of Semester

Organization: Jochen Triesch

Seminar: Tuesday 10:00 - 11:30

Room: FIAS 0.200 

Overview

"Current Topics in Theoretical Neuroscience" serves a dual purpose as our weekly group seminar, where members of the lab present their current research and as a seminar open for students to delve into the fascinating world of theoretical neuroscience.

Date	Title	Presenter
29.10.19	Scheduling and Lightning Research Talks (5min each)	Triesch Group
05.11.19	(no meeting)	-
12.11.19	"Recurrent connections aid occluded object recognition" (weblink)	Markus
	"Towards early diagnosis of epilepsy with deep neural networks"	Diyuan
19.11.19	(no meeting, Giersch International Symposium)	-
26.11.19	(no meeting, Jochen in Bordeaux)	-
03.12.19	Research Overview	Jochen
10.12.19	(no meeting, Jochen in Clermont-Ferrand)	-
17.12.19	Mathematical modeling of neuroimmune interactions in epileptogenesis	Danylo
07.01.20	OpenEyeSim 2.1 - simulation of accommodation and depth-of-field effects including chromatic aberrations	Alex Li.
	On synaptic lifetime distributions measured in vivo	Felix
14.01.20	A hierarchical approach to reinforcement learning	Charles
	Visual change detection	Alex Le.
21.01.20	A Multi-Timescale SORN	Marius
		Lukas
28.01.20	(no meeting, Jochen in France)
04.02.19	"Resolving challenges in deep learning-based analyses of histopathological images using explanation methods" (weblink)	Hendrik Edelmann
	"Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition" (weblink)	Laura Rumpf
	"Changes of dimension of EEG/ECoG nonlinear dynamics predict epileptogenesis and therapy outcomes" (weblink)	Daniel Helmke
11.02.19	"Data Augmentation by Pairing Samples for Images Classification" (weblink)	Jan Marker
	TBA	Sercan Alipek

Organization: Jochen Triesch

Seminar: Tuesday 10:00 - 11:30

Room: FIAS 0.200 

Overview

"Current Topics in Theoretical Neuroscience" serves a dual purpose as our weekly group seminar, where members of the lab present their current research and as a seminar open for students to delve into the fascinating world of theoretical neuroscience.

Date	Title	Presenter
16.04.19	Welcome & Scheduling	Omar Al-Lahham
23.04.19	Stable memories despite large spontaneous synaptic fluctuations	Samuel (R)
30.4.2019	Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders	Markus (R)
	Autonomous Development of Active Binocular and Motion Vision through Active Efficient Coding	Alexander (R)
07.05.19	Starting the role of neuroimmune interactions in epileptogenesis	Danylo (R)
	On the role of glial cells in neural network stability	Fereshteh (R)
14.05.19	(no meeting)	--
21.05.19	The development of active binocular vision under normal and alternate rearing conditions	Lukas (R)
	Intrinsically motivated learning	Charles (R)
28.05.19	(no meeting)	--
04.06.19	A fast interocular suppression test using dichoptic suppression	Yu Yi (R)
	CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning	Francois (P)
11.06.19	A spiking neuronal network for experience-induced changes in Binocular Rivalry	Norman (R)
	Models of synaptic survivability	Felix (R)
18.06.19	Epileptogenisis prediction with deep learning	Dyuan (R)
	Epileptic discharges and synaptic gain	Sima (R)
25.06.19	Goal-Robots Meeting	--
02.07.19	IMOL Workshop	--
09.07.19	(no meeting)	--
16.07.19	Yolo: An Introduction to Deep Neural Networks	Robert
	Forward Propagation Closed Loop Learning	Jonas
	Analysing Magnetic Resonance Spectra with Unsupervised Machine Learning Algorithms	Nils
23.7.19	TBA

Instructor: Jochen Triesch
Lecture: Wednesday 08:00 - 09:30
Room: Otto Stern Zentrum, seminar room 4

Overview
Reinforcement Learning is a research field at the intersection of Computer Science/Engineering (machine learning, artificial intelligence, control theory, robotics) and Neuroscience/Psychology (reward systems, human and animal learning, emotion). It describes how agents (biological or artificial) can learn to optimize their behavior in the presence of feedback that takes the form of rewards and punishments. This course provides an introduction to the theory of reinforcement learning and discusses applications of these concepts to modeling learning processes in biological systems. Covered topics include: Biology of reward systems, basics of animal learning, Markov Decision Processes, Dynamic Programming, Monte Carlo Methods, Temporal Difference Learning, Plannning, Mirror System, Imitation.Textbook

Textbook

The course is based on the text "Reinforcement Learning" by Rich Sutton and Andrew Barto, MIT Press, 2018. It contains errata, figures, links to software, an html version of the book and a link to the "incomplete and in progress" second edition, as well as links to related courses.

Slides

• Organization
  
• Projects
• Chapter 1
• Chapter 2
• Chapter 3
  
• Chapter 4
• Chapter 5
  
• Chapter 6, 7 and 8
• Chapter 9
• Chapter 10-12
• Chapter 13
• Chapter 16

Useful Links

• Scholarpedia has articles on a number of relevant topics.
• The book: Reinforcement Learning, an Introduction by Richard S. Sutton and Andrew G. Barto

Candidate papers can be found here.

Date	Title	Presenter
Oct 16	Welcome & Scheduling	Florence
Oct 23	(no meeting)
Oct 30	Combined mechanisms of neuronal firing rate homeostasis	Florence (P)
	Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning.	Alex Lichtenstein (P)
Nov 6	Additive Scaling of Effective Weights can Lead to Multiplicative Scaling	Suneet (R)
	Long-tailed distributions in neural networks: theory and simulation.	Florence (R)
Nov 13	Dendritic Error Backpropagation in Deep Cortical Microcircuits	Markus (P)
	Master's Thesis Presentation	Shawn (R)
Nov 20	Disentangling the independently controllable factors of variation by interacting with the world. (P)	Alex Lelais (P)
	Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data.	Diyuan (P)
Nov 27	(no meeting)
Dec 4	Two distinct mechanisms for experience-dependent homeostasis	Samuel (R)
	A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning	Felix (P)
Dec 11	Can we measure Stereoacuity with Virtual Reality?	Ivan (R)
	Development of Tests and Treatment Games for Amblyopia in Virutal Reality with Focus on Suppression	Yu Yi(R)
Dec 18	Control of synaptic plasticity in deep cortical networks	Norman (R)
	Upward synaptic scaling is dependent on neurotransmission rather than spiking	Samuel (P)
Winter Break	(no meeting)	--
Jan 8	Theoretical methods in Epilepsy	Sima (R)
	Open Eye Sim 2.0	Alex Lichtenstein (R)
Jan 15	Intrinsic Motivations	Charles (R)
	A review of predictive coding algorithms	Lukas (P)
Jan 22	no meeting
Jan 29	no meeting
Feb 5	PCA	Nils Möbius
	Behavioral Biometrics with LSTM networks	Marc Morone
Feb 12	Learning sensori-motor mappings using little knowledge: Application to manipulation robotics	Francois de La Bourdonnaye
Feb 19	TBA

Instructor: Jochen Triesch

Lecture: Friday, 9:00 - 11:00 (unless announced otherwise) 

Exercises: Friday, 11:00 - 12:00 

Room: Otto-Stern-Zentrum (IZR) - OSZ S2 (Except on 11.05. & 22.06: FIAS seminar room 0.200)

Overview

This course offers an introduction to the field of complex systems science. It presents some fundamental methods for the study of complex systems covering concepts from nonlinear dynamics, network theory, information theory and other things.

Slides

• Week #1 (2018-04-13)
  Introduction [slides]
• Week #2 (2018-04-20)
  Information Theory [slides]
  Turing [slides]
  Projects [slides]
• Week #3 (2018-04-27)
  Information Theory 2 [slides]
• Week #4 (2018-05-04)
  Information Theory 3 [slides]
• Week #5 (2018-05-11)
  Markov Chains [slides]
• Week #6 (2018-05-18)
  Random Walks [slides]
• Week #7 (2018-05-25)
  Cellular Automata [slides]
  
• Week #8 (2018-06-01)
  Criticality [slides]
  
• Week #9 (2018-06-08)
  Synchronization [slides]
  
• Week #10 (2018-06-15)
  Fractals [slides]
  Iterative maps & chaos [slides]
  
• Week #11 (2018-06-22)
  Networks 1 [slides]  [notes]
• Week #12 (2018-06-29)
  Networks 2 [slides] [notes] [jupyter notebook]

Text books
The course touches on a range of issues and no single textbook we know of covers all of them. Below is a list of recommended readings.

• James Sethna: Statistical Mechanics: Entropy, Order Parameters and Complexity. Oxford University Press. 2006.
• Claudius Gros, Complex and Adaptive Dynamical Systems. Springer Verlag, 2008.
• Steven Strogatz, Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Westview Press, 2001.
• Yaneer Bar-Yam, Dynamics of Complex Systems. Westview Press, 2003.
• Paul S Addison, Fractals and Chaos: An illustrated course. Institute of Physics Publishing, 1997.
• Arkady Pikovsky, Michael Rosenblum and Jürgen Kurths, Synchronization: A Universal Concept In Nonlinear Sciences. Caimbridge University Press, 2001.

Useful Links

• Scholarpedia and Complex Systems section of the Wolfram Mathworld has articles on a number of relevant topics.
• Wolfram Demonstration Project contains several demonstrations that illustrates the concepts in the field of Complex systems.

Date	Title	Presenter
10.04.18	Welcome & Scheduling	Florence
17.04.18	Ioffe & Szegedy: Batch Normalization: Accelerating Deep Netw	Charles (P)
	Bittner et al.: Behavioral time scale synaptic plasticity underlies	Florence (P)
	Demonstration: Overleaf	Lukas (D)
24.04.18	Model-based spiking neural network reconstruction	Anna (R)
	Brain-State Dependent Transcranial Magnetic Stimulation	Natalie (R)
	Demonstration: Reproducibility	Felix (D)
01.05.18	(no meeting)	--
08.05.18	Computation near criticality: insights into the fading memory capacity	Bruno (R)
	Tutorial: Scientific writing - Part I	Florence (T)
	Chong et al.: Intrinsic Inflammation Is a Potential Anti-Epileptogenic Target in the Organotypic Hippocampal Slice Model. Neurotherapeutics, 2018.	Sima (P)
15.05.18	(no meeting)	--
22.05.18	Dipoppa et al.: Vision and Locomotion Shape the Interactions between Neuron Types in Mouse Visual Cortex. Neuron, 2018.	Norman (P)
	An Active Efficient Coding Model of Binocular Vision Development under Normal and Abnormal Rearing Conditions	Lukas (R)
29.05.18	LA Gatys et al.: Texture Synthesis Using Convolutional Neural Networks. arXiv, 2015.	Julius (P)
05.06.18	Wayne et al.: Unsupervised Predictive Memory in a Goal-Directed Agent. arXiv, 2018.	Charles (P)
	Tutorial: Scientific writing - Part II	Florence (T)
12.06.18	Master thesis research	Moritz Groden (R)
	Classification of focal and non-focal intracranial EEG recordings using machine learning	Diyuan (R)
	Shooner et al.: Asymmetric Dichoptic Masking in Visual Cortex of Amblyopic Macaque Monkeys. Journal of Neuroscience, 2017.	Ivan (P)
19.06.18	Simultaneous Learning of Active Binocular Vision and Smooth Pursuit	Alex Lelais (R)
	Gainey et al.: Rapid Disinhibition by Adjustment of PV Intrinsic Excitability during Whisker Map Plasticity in Mouse S1. Journal of Neuroscience, 2018.	Samuel (P)
26.06.18	--	--
03.07.18	Rich & Wallis: Decoding subjective decisions from orbitofrontal cortex. Nature Neuroscience, 2018.	Shawn (P)
	Modelling synaptic lifetime distributions with Kesten processes	Felix (R)
	Tutorial: scientific writing [slides]	Jochen(D)
10.07.18	Stochastic simulation of AMPA receptor dynamics using the Gillespie algorithm	Maria Duong (R)
	Recurrent convolutional neural networks for occluded object recognition	Markus (R)
17.07.18	Machine learning and EEG classification	Thuy (R)
	Hopfield network and associative memory	Alex Luther (R)
	Recurrent Neural Networks	Niels (R)

In the winter semester 2017/2018, the seminar takes place in room 0.101 on Tuesdays from 10:00 to 12:00. To sign up for the course, please send an e-mail to Florence and Natalie. 

 

Date	Title	Speaker
17-10-2017	Introduction Schedule creation
24-10-2017	Grossman et al.: Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields. Cell (2017)	Natalie (P)
	Ocker & Doiron: Training and Spontaneous Reinforcement of Neuronal Assemblies by Spike Timing. arXiv (2016)	Florence (P)
31-10-2017	no meeting
07-11-2017	The Quest for Lognormal 2: Synapses and Firing Rates	Florence (R)
	Wilson et al.: Neurons in Ferret Visual Cortex Participate in Functionally Specific Networks. Neuron (2017)	Samuel (P)
14-11-2017
	Can clustering scale sublinearly with its clusters? A variational EM acceleration of GMMs and k-means (preprint)	Dennis (R)
	Silver et al.: Mastering the game of Go without human knowledge. Nature (2017)	Alex Lelais (P)
21-11-2017	Weissenberger et al.: Long Synfire Chains Emerge by Spike-Timing Dependent Plasticity Modulated by Population Activity. International Journal of Neural Systems (2017)	Bruno (P)
	Selby & Tripp: Extending the Stabilized Supralinear Network model for binocular image processing. Neural Networks (2017)	Lukas (P)
28-11-2017	Costa et al.: Synaptic Transmission Optimization Predicts Expression Loci of Long-Term Plasticity. Neuron (2017)	Felix (P)
Guo et al.: Timing-dependent LTP and LTD in mouse primary visual cortex following different visual deprivation models. PLOS ONE (2017)	Norman (P)
05-12-2017	van den Oord et al.: Wavenet: A Generative Model For Raw Audio. arXiv (2016)	Diyuan (P)
	Goodfellow et al.: Generative Adversarial Nets. Advances in neural information processing systems (2014)	Charles (P)
12-12-2017	Recurrent Dynamics in Convolutional Neural Networks for Occluded Image Recognition	Julius (R)
	Sabour et al.: Dynamic Routing Between Capsules. NIPS (2017)	Markus (P)
19-12-2017	(PhD thesis research)	Alex Lichtenstein (R)
	Modelling the effects of brain oscillations on transcranial magnetic stimulation	Julia (R)
26-12-2017	no meeting
02-01-2018	no meeting
09-01-2017	--	--
	The Cortical Mechanisms Underlying Ocular Dominance Plasticity in Adults are Not Orientationally Selective	Ivan (P)
16-01-2017	Homeostasis via Synaptic Scaling on Different Time Scales	Suneet (R)
	Hippocampal sclerosis shapes seizure-like activity: a computational model	Pira (R)
23-01-2017		Nima Amin (R)
	--	--
30-01-2017	no meeting
06-02-2017		Robert (R)
		Jonas Mahn (R/P)

In the summer semester 2017, the seminar takes place in room 0.101 on Tuesdays from 10:00 to 12:00. To sign up for the course, please send an e-mail to Florence and Max.

Date	Titles	Speakers
18-4-2017	A model of synaptic normalization and heterosynaptic plasticity based on competition for a limited supply of synaptic building blocks	Jochen
	Tutorial: MELD file comparison tool	Florence
25-4-2017	FIAS evaluation	-
2-5-2017	GOAL Robots meeting	-
9-5-2017	Autonomous, self-calibrating binocular vision based on learned attention and active efficient coding	guest: Walter
	The Quest for Lognormal	Florence
16-5-2017	Avalanche and edge-of-chaos criticality do not necessarily co-occur in neural networks	Bruno
	Assessing the role of inhibition in stabilizing neocortical networks requires large-scale perturbation of the inhibitory population	Felix
23-5-2017	Inhibitory Synapses Are Repeatedly Assembled and Removed at Persistent Sites In Vivo	Samuel
	Active cortical dendrites modulate perception	Natalie
	Tutorial: Pandoc Demo	Natalie
30-5-2017	Optimal Degrees of Synaptic Connectivity	Florence
	Look & Learn: Modeling Gaze-Contingent Learning	Max
6-6-2017	Consequences of the Oculomotor Cycle for the Dynamics of Perception	Anna
	Computational Modelling of Infant Speech Acquisition	Diyuan
13-6-2017	A predictive coding account of bistable perception - a model-based fMRI study	Lukas K
	k-means is a variational approximation of GMMs	Dennis
20-6-2017	Human Cortical Neurons in the Anterior Temporal Lobe Reinstate Spiking Activity during Verbal Memory Retrieval	Diyuan
	Advances in Developmental Robotics	Alex L
27-6-2017	Automatic Goal Generation for Reinforcement Learning Agents	Charles
	Neural Plasticity in the Early Visual Cortex	Samuel
4-7-2017	Optimization of visual training for full recovery from severe amblyopia in adults	Johann
	Handwriting recognition with self-organized neural networks	Nicklas
11-7-2017	Mechanisms underlying different onset patterns of focal seizures	Pira
	Consequences of the Oculomotor Cycle for the Dynamics of Perception	Anna
18-7-2017	"What Not" Detectors Help the Brain See in Depth	Alex P
	A spiking neural network for experience-induced changes in Binocular Rivalry	Norman
	OCNC report	Felix

For each date, the white slots refer to the journal talks, and the light blue slots refer to the research talks.

In the winter semester 2016/17, the seminar takes place in room 0.200 on Mondays from 9:00 to 10:30. To sign up for the course, please send an e-mail to Florence and Max.

Date	Titles	Speakers
17-10-2016	(Preparation)	-
24-10-2016	-	-
Methods for the Study of Complex Systems	several
31-10-2016	Multi-contact synapses for stable networks: a spike-timing dependent model of dendritic spine plasticity and turnover	Florence
Self-Organization of Cortical Synaptic Dynamics and Connectivity	Daniel
-	-
7-11-2016	Fast Sequences of Non-spatial State Representations in Humans	Bruno
Intrinsic plasticity and machine learning	Charles
-	-
14-11-2016	Temporal Sequence Recognition in a Self-Organizing Recurrent Network	Antonia
How to create a Leaky Integrate-and-Fire Neuron and its environment	Lisa & Norman
-	-
21-11-2016	Rapid signalling in distinct dopaminergic axons during locomotion and reward	Max
Diffusive Homeostasis in a Self-Organizing Recurrent Neural Network	Fabian
-
28-11-2016	On the Use of Intrinsic Motivation for Visual Saliency Learning	Alex Lelais
Neuromodulated spike-timing dependent plasticity and reward-driven learning in the SORN	Bruno
-	-
5-12-2016	Multistability and perceptual inference	Lukas Klimmasch
Non-random network connectivity comes in pairs	Felix
-	-
12-12-2016	Hebbian plasticity requires compensatory processes on multiple timescales	Samuel
Truncated Variational EM for Neural Simpletrons	Dennis
scipy.stats	Felix
19-12-2016	Learning Multiple Sensory Neural Controllers for Robot Arm Tracking	Alex Priamikov
SORN: Plasticity and Memory	Lukas Köhs
-	-
9-1-2017	Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation	Nicklas
A Model of Sequence Learning in Visual Cortex	Christian
-
16-1-2017	Timing Rules for Synaptic Plasticity Matched to Behavioral Function	Anna
A computational model for the joint development of accomodation and vergence control	Samuel
-	-
23-1-2017	Reactivation of latent working memories with transcranial magnetic stimulation	Natalie
LazyEyeGym: progression and first measurement results	Johann
-	-
30-1-2017	The spatial structure of correlated neuronal variability	Felix
Computational Modelling of Transcranial Magnetic Stimulation on Neuronal Activity	Natalie
-	-
6-2-2017	Convolutional Neural Networks	guests: Julius & Shawn
Backpropagation project	guests: Michael & Merali

For each date, the first entry refers to the journal talk, the second entry (light blue) refers to the research talk, and the third entry refers to a tutorial.

In the summer semester 2016, the seminar took place in room 0.200 on mondays from 9:00 to 10:30.

Date	Titles	Speakers
11-4-2016	(Preparation)	-
18-4-2016	-	-
Diffusive Homeostasis Mechanisms in Self-Organizing Neural Networks	Fabian
Sumatra	Felix
25-4-2016	Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network Short-Term Memory in Orthogonal Neural Networks	Bruno Antonia
-	-
How to run jobs on the FIAS cluster	Lukas
2-5-2016	Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior	Max
Active Efficient Coding: Learning Depth Perception in Continuous Action Space	Alex Lelais
-	-
9-5-2016	The theoretical model of theta burst form of repetitive transcranial magnetic stimulation	Natalie
Binocular Vision by Ambiguity Reduction	Samuel
-	-
16-5-2016	(Pentecote)	-
23-5-2016	Is cortical connectivity optimized for storing information?	Florence
Predicting Neural Activity on Data from Beggs Lab	Christoph
A basic project structure (files)	Philip
30-5-2016	-	-
Workshop Report	Fabian
-	-
6-6-2016	Mastering the game of Go with deep neural networks and tree search	Dennis
Inhibitory and Excitatory weight dynamics in the LIF-SORN with excitatory and inhibitory STDP	Florence
-	-
13-6-2016	-	-
Investigation of the Learning Ability of a Topologically Constrained Self-Organizing Leaky Integrate-and-Fire Neural Network	Christian
-	-
20-6-2016	-	-
TMS-EEG summer school Look & Learn: A Model of Gaze Contingent Behavior	Natalie Max
-	-
27-6-2016	Neural and perceptual signatures of efficient sensory coding	Samuel
Beyond Neural Simpletrons	Dennis
-	-
4-7-2016	-	-
Speech perception and imitation Nengo and a basial ganglia model for gambling	Philip Bruno
-	-
11-7-2016	-	-
EyeGym: VR-Software for the treatment of Amblyopia and Strabismus	Johann
-	-

For each date, the first entry refers to the journal talk, the second entry (light blue) refers to the research talk, and the third entry refers to a tutorial.

In the winter semester 2015/16, the seminar took place in room 0.200 on mondays from 9:00 to 10:30.

Date	Titles	Speakers
12-10-2015	(Preparation)	-
19-10-2015	A Bayesian observer model constrained by efficient coding can explain 'anti-Bayesian' percepts	Natalie
Self-Organization of Random Features in a Cortex-like Network	Daniel
range vs. xrange	Max
26-10-2015	Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex	Bruno
Precise Synaptic Alignment Suggests Potentiation Dominated Learning	Christoph
ipython-notebook (files)	Christoph
2-11-2015	Labelling and optical erasure of synaptic memory traces in the motor cortex	Christoph
OpenEyeSim & Less is more	Alex
Yield (ipython-notebook file)	Dennis
9-11-2015	Human-level control through deep reinforcement learning	Dennis
Can random stimuli drive SORN towards a critical point?	Bruno
16-11-2015	Origins of strabismus and loss of binocular vision	Alex
Neuron Population Dynamics in Zebrafish	Guest: Peter Kuhn
23-11-2015	Phonetic Feature Encoding in Human Superior Temporal Gyrus	Philip
Active Perceptions as a Complex Feedback Signal	Samuel
Profiling & Vectorization	Daniel
30-11-2015	Habit Learning by Naive Macaques Is Marked by Response Sharperning of Striatal Neurons Representing the Cost and Outcome of Acquired Action Sequences	Max
Recurrent Learning with Inference Feedback in a Generative Neural Network	Dennis
7-12-2015	(Jochen gone)	-
14-12-2015	A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks	Fabian
Firing patterns in the adaptive exponential integrate-and-fire model	Guest: Benjamin Linnik
11-1-2016	Learning Slowness in a Sparse Model of Invariant Feature Detection	Julian
Modeling of TMS-induced activation of pyramidal neurons embedded in a realistic cortical morphology	Natalie
Tqdm	Natalie
18-1-2016	-	-
A Model of Gaze Contingent Learning	Max
25-1-2016	(break)	-
1-2-2016	Learned spationtemporal sequence recognition and prediction in primary visual cortex	Christian
Command line shortcuts	Natalie
8-2-2016	A Game Platform for Treatment of Amblyopia	Johann
Lazy Eye Shooter: Making a Game Therapy for Visual Recovery in Adult Amblyopia Usable	Alex
IPython Debugger	Christoph
15-2-2016	Stability of Neuronal Networks with Homeostatic Regulation	Florence
Speaker generalisation in vowel learning	Philip
Personal FIAS Webspace	Max

For each date, the first entry refers to the journal talk, the second entry (light blue) refers to the research talk, and the third entry refers to a tutorial.

Organization: J. Lücke and J. Triesch 

Type: Course, 3 SWS 

Language: English 

Time and place: Friday 11:15 - 14:00, FIAS 200

Requirements

Open-minded students from a number of disciplines including Physics, Mathematics, Computer Science, Biology, and Psychology are welcome. Good analytical skills are essential.

Overview

In this course we study unsupervised learning, i.e. learning without a teacher. The course combines perspectives from modern machine learning and neuroscience. After a review of fundamental concepts from probability, statistics, and information theory, we study various architectures and algorithms for different aspects of unsupervised learning. We will be interested in the fundamental computational problems associated with various unsupervised learning tasks, algorithms for (approximately) solving these problems, and neurally plausible implementations of such algorithms.

Slides

• Elementary Probability Theory
• Overview and Introduction
• Information Theory
• Classification, Density Estimation, Maximum Likelihood and EM
• EM and Mixture Models
• Kernel Density Estimation
• Hebbian Learning 1
• Clustering
• PCA
• Probabilistic PCA and Sparse Coding
• ICA

Review Questions

Instructor: Jochen Triesch 

Time: Fridays, 11:00 - 14:00

Overview

This course is a basic introduction to Neural Networks. On the one hand, these can be viewed as very simplified mathematical models of information processing in the brain. On the other hand they are useful tools for pattern recognition and data analysis.

Text

The course is mostly based on the text "Neural Networks: A comprehensive foundation" by Simon Haykin, Prentice Hall, 1999.

Topics and Slides

• organization.pdf, introduction.pdf
• Connectionist model neurons, network architectures, Perceptrons (see notes)
• Supervised Learning: Multi-layer Perceptrons, Generalization, Radial Basis Function Networks (see notes)
• Associative Memory: Hopfield Networks
• Reinforcement Learning
• Unsupervised Learning: PCA, neural PCA, Clustering, Maps, Information Theory, ICA