Publications Theoretical Neuroscience Group

J.P. Pfister, A. Ghosh.
A generalized priority-based model for smartphone screen touches. PhysRev Accepted, 2020

S.C. Surace, A. Kutschireiter and J.P. Pfister.
Asymptotically exact unweighted particle filter for manifold-valued hidden states and point process observations. IEEE Control Systems Letters 1907.10143, 2020 DOI pdf

J. Jegminat, M. Jastrzebowska, M.V. Pachai, M.H. Herzog, J.P. Pfister.
Bayesian regression explains how human participants handle parameter uncertainty. PLoS Comput Biol 16(5): 1-23, 2020 DOI

S.C. Surace, J.P. Pfister, W. Gerstner, J. Brea.
On the choice of metric in gradient-based theories of brain function. PLoS Comput Biol 16(4): 1-13, 2020 DOI

M. Gilson, J.P. Pfister.
Propagation of Spiking Moments in Linear Hawkes Networks. SIAM J Appl Dyn Syst 19(2):828-859, 2020 DOI

A. Kutschireiter, S.C. Surace, J.P. Pfister.
The Hitchhiker's Guide to Nonlinear Filtering. J. Math Psychology 94, 1-21, 2020 DOI

S.C. Surace, A. Kutschireiter and J.P. Pfister.
How to avoid the curse of dimensionality: scalability of particle filters with and without importance weights. SIAM Review 61(1), 79-91, 2019 DOI

O.S. Bykowska, C. Gontier, A.L Sax, D.W. Jia, M. Llera-Montero, A.D. Bird, C.J. Houghton, J.P. Pfister and R.P. Costa1.
Model-based inference of synaptic transmission. Front. Synaptic Neurosci. in press, 2019 DOI

S.C. Surace, J.P. Pfister.
Online Maximum Likelihood Estimation of the Parameters of Partially Observed Diffusion Processes. IEEE Transactions on Automatic Control 64(7), 2814-2829, 2019 DOI

A. Kutschireiter, S.C. Surace, H. Sprekeler & J.P. Pfister.
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception. Sci Rep 7: 8722, 2017 DOI

S.C. Surace, J.P. Pfister.
A Statistical Model for In Vivo Neuronal Dynamics. PLoS ONE 10(11): e0142435, 2015 DOI

SM Blom, J.P. Pfister, M. Santello, W. Senn & T. Nevian.
Nerve injury-induced neuropathic pain causes disinhibition of the anterior cingulate cortex. J. Neurosci. 34(17):5754-5764, 2014 DOI

W. Senn, J.P. Pfister.
Reinforcement learning in cortical networks in Encyclopedia of Computational Neuroscience, Springer 2014

W. Senn, J.P. Pfister.
Spike-Timing Dependent Plasticity, Learning Rules in Encyclopedia of Computational Neuroscience, Springer 2014

J. Brea, W. Senn, J.P. Pfister.
Matching Recall and Storage in Sequence Learning with Spiking Neural Networks. J. Neurosci. 33(23): 9565–9575, 2013 DOI

J. Gjorgjieva, C. Clopath, J. Audet, and J.P. Pfister.
A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations. PNAS 1-6, 2011 DOI

J. Brea, W. Senn, and J.-P. Pfister.
Sequence learning with hidden units in spiking neural networks.In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, and K. Weinberger, editors. Advances in Neural Information Processing Systems 24 pp. 1422-1430, 2011

G. Hennequin, W. Gerstner, J. Pfister.
STDP in adaptive neurons gives close-to-optimal information transmission. Front. Comput.Neurosci. 4:22, 2010 DOI

J.P. Pfister, P.A. Tass.
STDP in oscillatory recurrent networks: theoretical conditions for desynchronization and applications to deep brain stimulation. Front. Comput.Neurosci. 4:22, 2010

J.P. Pfister, P. Dayan, M. Lengyel.
Synapses with short-term plasticity are optimal estimators of presynaptic membrane potentials. Nat. Neurosci. 2010

J.P. Pfister, P. Dayan, M. Lengyel.
Know Thy Neighbour: A Normative Theory of Synaptic Depression in Advances in Neural Information Processing Systems22, edited by Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williamsand A. Culotta, MIT Press, Cambridge MA, 1464-1472, 2009

T. Toyoizumi , J.P. Pfister, K. Aihara, W. Gerstner.
Optimality Model of Unsupervised Spike-Timing Dependent Plasticity: synaptic memory and weight distribution. Neural Comput. 19(3):639-671, 2007

J.P. Pfister, W. Gerstner.
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects. Advances in Neural Information Processing Systems 18, edited by Y. Weiss and B. Schoellkopf and J. Platt, MIT Press, Cambridge MA, 1083-1090. 2006

J.P. Pfister, T. Toyoizumi, K. Aihara, W. Gerstner.
Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. Neural Comput. 18:1309-1339, 2006

J.P. Pfister.
Theory of Non-linear Spike-Time-Dependent Plasticity. PhD Thesis. 2006

J.P. Pfister, W. Gerstner.
Triplets of Spikes in a Model of Spike-Timing-Dependent Plasticity. J. Neurosci. 26:9673-9682, 2006

T. Toyoizumi, J.P. Pfister, K. Aihara, W. Gerstner.
Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. Proceedings of the National Academy of Science USA, 102, 5239-5244. 2005

T. Toyoizumi, J.P. Pfister, K. Aihara, W. Gerstner.
Spike-Timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model.Advances in Neural Information Processing Systems 17, edited by L.K. Saul and Y.Weiss and L. Bottou, MIT Press, Cambridge MA, 1409-1416. 2005

J.P. Pfister, D. Barber, W. Gerstner.
Optimal Hebbian Learning: A Probabilistic Point of View. Arti cial Neural Networks and Neural Information Processing - ICANN/ICONIP 2003, edited by O. Kaynak, E. Alpaydin, E. Oja and L. Xu. Berlin: Springer-Verlag, 92-98. 2003