Johannes Friedrich

Associate Research Scientist
Neural Data Science, Computational Neuroscience, Machine Learning

About

I am currently an associate research scientist at Simons Foundation's Flatiron Institute as a member of the Neural Circuits and Algorithms group at the Center for Computational Neuroscience. Prior to joining the foundation, I was a Grossman Center postdoctoral research scientist at Columbia University, where I worked with Liam Paninski. I further collaborated with the group of Misha Ahrens at Janelia Research Campus, as well as with Andrea Giovannucci and Eftychios Pnevmatikakis at the Flatiron Institute.
My research lies at the intersection of computational neuroscience and machine learning, targeting the general question of how statistics can help us decipher neural computation.
Currently my research focuses on one side on efficient machine-learning and optimization algorithms for statistical analysis of large-scale neural data, in particular online inference of neural activity of calcium imaging data, and on the other side on theories of neural computation, such as implementations of machine learning algorithms in biologically realistic neural networks.
My previous work, while at the University of Bern and the University of Cambridge, centered on decision making, in particular on implementations of model-free and model-based reinforcement learning in spiking neural networks.

I am a regular contributor to the top machine learning/AI conference NeurIPS (4 first author papers and counting), and also target the neuroscientific/general audience with papers in high-ranked journals such as PLoS Comp Bio, eLife and Science.

I have enjoyed outdoor activities in the mountains since an early age. Living in New York I miss the Swiss Alps, but at least I got to enjoy funding from the Swiss National Science Foundation (SNSF).

Publications

Journal Papers

Friedrich J., Golkar S., Farashahi S., Genkin A., Sengupta A.M. and Chklovskii D. Neural optimal feedback control with local learning rules. NeurIPS, 34, 2021.
source code, video
Cai C., Friedrich J., Singh A., Eybposh M.H., Pnevmatikakis E.A., et al. VolPy: Automated and scalable analysis pipelines for voltage imaging datasets. PLoS Comput Biol, 17(4):e1008806, 2021.
Friedrich J., Giovannucci A. and Pnevmatikakis E.A. Online analysis of microendoscopic 1-photon calcium imaging data streams. PLoS Comput Biol, 17(1):e1008565, 2021.
Friedrich J. Neuronal Gaussian Process Regression. NeurIPS, 33:7090-7100, 2020.
source code, video abstract
Abdelfattah A.S., et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science, 365(6454):699-704, 2019.
Giovannucci A., Friedrich J., et al. CaImAn an open source tool for scalable calcium imaging data analysis. eLife, 8:e38173, 2019.
Berens P., et al. Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput Biol, 14(5):e1006157, 2018.
Zhou, P., et al. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife, 7:e28728, 2018.
Giovannucci A., Friedrich J., Kaufman M., Churchland A., Chklovskii D., Paninski L. and Pnevmatikakis E.A. OnACID: Online Analysis of Calcium Imaging Data in real time. NIPS, 30:2378-2388, 2017.
Friedrich J., Yang W., Soudry D., Mu Y., Ahrens M., Yuste R., Peterka D. and Paninski L. Multi-scale approaches for high-speed imaging and analysis of large neural populations. PLoS Comput Biol, 13(8): e1005685, 2017.
Friedrich J., Zhou P. and Paninski L. Fast online deconvolution of calcium imaging data. PLoS Comput Biol, 13(3):e1005423, 2017.
source code
Friedrich J. and Paninski L. Fast active set methods for online spike inference from calcium imaging. NIPS, 29:1984-1992, 2016.
source code
Friedrich J. and Lengyel M. Goal-directed decision making with spiking neurons. J Neurosci, 36(5):1529-1546, 2016.
supplementary material & source code
Clarke A., Friedrich J., Senn W., Tartaglia E., Marchesotti S. and Herzog M. Human and machine learning in non-Markovian decision making. PLoS ONE, 10(4):e0123105, 2015.
Friedrich J., Urbanczik R. and Senn W. Code-specific learning rules improve action selection by populations of spiking neurons. Int J Neur Syst, 24(5):1450002, 2014.
Friedrich J. and Senn W. Spike-based decision learning of Nash equilibria in two player games. PLoS Comput Biol, 8(9):e1002691, 2012.
Friedrich J., Urbanczik R. and Senn W. Spatio-temporal credit assignment in neuronal population learning. PLoS Comput Biol, 7(6):e1002092, 2011.
Friedrich J., Urbanczik R. and Senn W. Learning spike-based population codes by reward and population feedback. Neural Comput, 22(7):1698-1717, 2010.
Friedrich J. and Kinzel W. Dynamics of recurrent neural networks with delayed unreliable synapses: metastable clustering. J Comput Neurosci, 27:65-80, 2009.

Workshop Papers, Abstracts, and Posters

Friedrich J., et al. OnACID: Online Analysis of Calcium Imaging Data in real time. Computational and Systems Neuroscience, Denver, CO, USA. February 2018.
extended abstract poster
Kawashima T., et al. Population voltage imaging in behaving animals reveals dynamics of sensorimotor decisions. Computational and Systems Neuroscience, Denver, CO, USA. February 2018.
Buchanan E.K., Friedrich J., et al. Constrained matrix factorization methods for denoising and demixing voltage imaging data. Computational and Systems Neuroscience, Denver, CO, USA. February 2018.
poster
Shababo B., et al. Automated high-throughput cellular resolution neural circuit mapping with online experimental design. Computational and Systems Neuroscience, Denver, CO, USA. February 2018.
poster
Friedrich J., Zhou P. and Paninski L. Fast active set methods for online deconvolution of calcium imaging data. Computational and Systems Neuroscience, Salt Lake City, UT, USA. February 2017.
extended abstract
Giovannucci A., Friedrich J., Deverett B., Staneva V., Chklovskii D. and Pnevmatikakis E. CaImAn: An open source toolbox for large scale calcium imaging data analysis on standalone machines. Computational and Systems Neuroscience, Salt Lake City, UT, USA. February 2017.
Friedrich J., Soudry D., Mu Y., Freeman J., Ahrens M. and Paninski L. Fast constrained non-negative matrix factorization for whole-brain calcium imaging data. NIPS Workshop, Montreal, Canada. December 2015.
paper
Friedrich J., Lengyel M. Model-based reinforcement learning with spiking neurons. Computational and Systems Neuroscience, Salt Lake City, UT, USA. March 2015.
extended abstract
Friedrich J., Lengyel M. Model-based reinforcement learning with spiking neurons. Computational and Systems Neuroscience, Göttingen, Germany. September 2014.
abstract
Clarke A., Friedrich J., Senn W., Tartaglia E., Marchesotti S. and Herzog M. Non-markovian human learning. 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA. October 2013.
extended abstract
Friedrich J. and Senn W. Spike-Based Decision Learning In Socio-Economic Interactions. 8th Forum of European Neuroscience, Barcelona, Spain. July 2012.
abstract
Friedrich J. and Senn W. Spike-based decision learning in two player games. International Conference on Brain Dynamics and Decision Making, Ascona, Switzerland. May 2012.
Friedrich J., Urbanczik R. and Senn W. Policy gradient rules for populations of spiking neurons. 20th Computational Neuroscience meeting, Stockholm, Sweden. July 2011.
abstract
Friedrich J., Urbanczik R. and Senn W. Spatio-temporal credit assignment in population learning. 7th Forum of European Neuroscience, Amsterdam, Netherlands. July 2010.
abstract
Friedrich J., Urbanczik R. and Senn W. Spatio-temporal credit assignment in population learning. Computational and Systems Neuroscience, Salt Lake City, UT, USA. February 2010.
Friedrich J. and Kinzel W. Synfire chains in integrate-and-fire networks with unreliable synapses. Annual Meeting of the German Physical Society, Berlin, Germany. February 2008.
abstract

Conference Talks / Invited Talks

Neural optimal feedback control with local learning rules. New York University, New York, USA. Jan 2022.
Online methods for real-time analysis of calcium imaging data. CNS Workshop, Melbourne, Australia. July 2020.
video recording
Online methods for real-time analysis of calcium imaging data. Rockefeller University, NY, USA. Jan 2020.
Goal-directed decision making with spiking neurons. Rutgers University, NJ, USA. Jan 2019.
Online multi-scale methods for fast large-scale calcium imaging. University College London, UK. June 2016.
Goal-directed decision making with spiking neurons. University of Cambridge, UK. January 2015.
Spike-based neuronal population learning in games. University of Sheffield, UK. August 2013.
Neuronal population learning in non-Markovian tasks and 2-player games. Frankfurt Institute of Advanced Studies, Germany. November 2012.
Spatio-temporal credit assignment in neuronal population learning. University of Cambridge, UK. January 2012.
Activity dependent modulation of plasticity in population learning. NIPS Workshop, Whistler, Canada. December 2010.
Policy gradient rules for populations of spiking neurons. EPFL Lausanne, Switzerland. November 2010.
Spatio-temporal credit assignment in population learning. University of Würzburg, Germany. January 2010.

Open Source Projects on GitHub

I am the developer of OASIS. The algorithm for fast online deconvolution of calcium imaging data.

I am a main developer of CaImAn. The computational toolbox for large scale Calcium Imaging Analysis, including movie handling, motion correction, source extraction, spike deconvolution and result visualization.