The course is focused on experimental and theoretical methods to study how the brain operates at the level of neuronal circuits. We cover various optical and electrophysiological concepts and techniques used currently in systems neuroscience from the basics to advanced topics on both theoretical and experimental grounds. TENSS also provides important insights into modern machine learning techniques and artificial intelligence, with application to advanced neuroscience data analysis.
The course is designed to be a highly interactive, hands-on experience, reflecting the atmosphere of CSHL, Woods Hole or Champalimaud courses.
Typically, each course day will contain an extended lab session and several theoretical lectures.
Hard work will be combined with a few trips through the beautiful Transylvanian countryside.
The course is addressed to a graduate student/postdoc audience.
Program coming soon.
See last year's program for more details.
Athena Akrami Sainsbury Wellcome Centre, UCL, UK
Upinder Bhalla National Centre for Biological Sciences, India
Juan Burrone Kings College London, UK
Federico Carnevale DeepMind Technologies, London, UK
Michael Dickinson California Institute of Technology, USA
Florian Engert Harvard University, USA
Sonja Hofer Sainsbury Wellcome Centre, UCL, UK
Tomα Hromαdka Slovak Academy of Sciences, Slovakia
Mark Hübener Max Planck Institute of Neurobiology, Germany
Na Ji Howard Hughes Medical Institute, Janelia Farm, USA
Benjamin Judkewitz Einstein Center for Neuroscience, Germany
Georg Keller Friedrich Miescher Institute, Switzerland
Emilie Mace Max Planck Institute of Neurobiology, Germany
MacKenzie Mathis Rowland Institute, Harvard University
Hannah Monyer University of Heidelberg, Germany
Tom Mrsic-Flögel Sainsbury Wellcome Centre, UCL, UK
Ruben Portugues Max Planck Institute of Neurobiology, Germany
Tobias Rose Max Planck Institute of Neurobiology, Germany
Botond Roska IOB Basel, Switzerland
Rava da Silveira Ecole Normale Supιrieure de Paris, France
Wolf Singer Max Planck Institute for Brain Research, Germany
Nao Uchida Harvard University, USA
Daniela Vallentin Max-Planck-Institute for Ornithology, Germany
Mitsuko Watabe-Uchida Harvard University, USA
Chris Xu Cornell University, USA
Petr Znamenskiy Francis Crick Institute, UK
- Florin Albeanu Cold Spring Harbor Laboratory, NY, USA
- Adam Kampff Sainsbury Wellcome Centre, University College London, UK
- Raul Mureşan Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
Teaching assistants & organizing team
- Harald Bârzan Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Antonin Blot Sainsbury Wellcome Centre, UCL, UK
- Rob Campbell Sainsbury Wellcome Centre, UCL, UK
- Andrei Ciuparu Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Medorian Gheorghiu Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Matνas Goldin Institut des Neurosciences Paris-Saclay, France
- Priyanka Gupta Cold Spring Harbor Laboratory, NY, USA
- Mitra Javadzadeh Sainsbury Wellcome Centre, UCL, UK
- Ana Maria Ichim Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Mateusz Kostecki Nencki Institute for Experimental Biology, Warsaw, Poland
- Gonçalo Lopes Sainsbury Wellcome Centre, UCL, UK
- Fred Marbach Sainsbury Wellcome Centre, UCL, UK
- Vasile V. Moca Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Adriana Nagy-Dăbâcan Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Jon Newman Massachusetts Institute of Technology, USA
- Bruno Pichler INSS (Independent NeuroScience Services), UK
- Maxime Rio Sainsbury Wellcome Centre, UCL, UK
- Nacho Sanguinetti Bernstein Center for Computational Neuroscience, Berlin, Germany
- Iuliu Vasilescu Politechnica University, Bucharest, Romania
- Jakob Voigts Massachusetts Institute of Technology, USA
Support and administration
- Cosmina Pavel Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
- Attila Kelemen Babes-Bolyai University, Cluj-Napoca, Romania
- Basic Optics Diffraction and Resolution. Illumination Techniques. Numerical Aperture.
- Optical bench exercises Lenses, optical systems, illumination methods, basic microscopy techniques. How to custom build different kinds of microscopes.
- Noise measurements and photo-sensors Shot noise, optical detectors, amplifiers, NI-DAQ, CCD cameras, photodiodes, photo multiplier tubes (PMTs).
- Light and fluorescence microscopy Fluorescence, FRAP, photo-activation, photo-conversion. Point spread function measurements, basic image analysis (deconvolution, denoising, PCA).
- Fluorescence probes GFP, GFP based chromophores, organic calcium dyes, genetically encoded calcium dyes, pHluorins, voltage sensitive dyes.
- Intrinsic Optical Imaging Visual, auditory & barrel cortex; olfactory bulb. Students will build a custom wide field fluorescence and intrinsic optical imaging rig.
- Scanning microscopy Confocal and two-photon microscopy. Lasers. Students will build a two-photon microscope and write custom scanning and acquisition software in MATLAB and NI DAQmx. The ScanImage API.
- Viral approaches to label, monitor and alter neuronal circuits.
- Optogenetics Light activated ion channels and pumps. Patterned photo-stimulation techniques.
- Benchtop electronics and basic electrophysiology Impedence and Dipoles. Amplifiers. Extracellular and intracellular recordings. LFP; single unit, multi-unit extracellular recordings, tetrodes, electrode arrays; patch clamp.
- Awake head fixed and freely moving optical and electrophysiological recording strategies in rodents Microdrives. Fiber optic based systems. Open source systems. Open Ephys.
- Techniques for electrophysiological data analysis.
- Monitoring animal behavior Open Source tools for acquisition and analysis of video data. Intro to Bonsai and Arduino. Training Strategies. Closed loop systems.
- Neuronal functional connectivity and neuronal connectomics Serial electron-microscopy and trans-synaptic labeling methods.
- Synchrony and oscillations.
- Cortical attention, sparse neuronal codes.
- Decision making, uncertainty, neuro-modulatory systems.
- Machine learning and artificial intelligence.
- Governance and ethics.