Johannes Friedrich

Associate Research Scientist
Computational Neuroscience, Machine Learning

About

I am currently an associate research scientist at Simons Foundation's Flatiron Institute as a member of the neuroscience group at the Center for Computational Biology. 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 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

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

  • 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.

Contact

Flatiron Institute
Simons Foundation
162 5th Ave
New York, NY, 10010
jfriedrich (at) flatironinstitute.org