Dr Johannes Friedrich

Johannes Friedrich

University position

Research Associate


Department of Engineering



Home page

http://www.stat.columbia.edu/~johannes (personal home page)

Research Themes

Systems and Computational Neuroscience

Cognitive and Behavioural Neuroscience


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.

Most of my work centered on decision making, in particular on implementations of model-free and model-based reinforcement learning in spiking neural networks.

Currently my focus is on large-scale neural data analysis, obtained with whole-brain recording at single cell resolution, to study how the brain learns and controls behavior.

Research Focus


synaptic plasticity

decision making

reinforcement learning

Bayesian inference

spiking neurons

Clinical conditions

No direct clinical relevance


Computational modelling


No collaborators listed

Associated News Items

    Key publications

    Friedrich J, Lengyel M (2016), “Goal-Directed Decision Making with Spiking Neurons.” J Neurosci 36(5):1529-46

    Friedrich J, Urbanczik R, Senn W (2011), “Spatio-temporal credit assignment in neuronal population learning.” PLoS Comput Biol 7(6):e1002092 Details



    Clarke AM, Friedrich J, Tartaglia EM, Marchesotti S, Senn W, Herzog MH. (2015), “Human and machine learning in non-Markovian decision making.” PLoS One 10(4):e0123105


    Friedrich J, Urbanczik R, Senn W (2014), “Code-specific learning rules improve action selection by populations of spiking neurons.” Int J Neural Syst 24(5):1450002 Details


    Friedrich J, Senn W (2012), “Spike-based decision learning of Nash equilibria in two-player games.” PLoS Comput Biol 8(9):e1002691 Details


    Friedrich J, Urbanczik R, Senn W (2010), “Learning spike-based population codes by reward and population feedback.” Neural Comput 22(7):1698-717 Details


    Friedrich J, Kinzel W (2009), “Dynamics of recurrent neural networks with delayed unreliable synapses: metastable clustering.” J Comput Neurosci 27(1):65-80 Details