Working papers

PDF Asymptotic scaling properties of the posterior mean and variance in the Gaussian scale mixture model.
Rodrigo Echeveste, G. Hennequin and M. Lengyel (2017).
(arXiv preprint)

PDF Stabilized supralinear network dynamics account for stimulus-induced changes of noise variability in the cortex.
G. Hennequin, Y. Ahmadian*, D. B. Rubin*, M. Lengyel and K. D. Miller (2016).
(* and †: equal contributions)

PDF Characterizing variability in nonlinear recurrent neuronal networks.
G. Hennequin and M. Lengyel (2016).
(arXiv preprint)

Published papers

PDF Inhibitory plasticity: Balance, Control, and Codependence.
G. Hennequin*, E. J. Agnes* and T. P. Vogels (2017).
Annual Review of Neuroscience.

PDF Neural networks subtract and conquer.
G. Hennequin (2017).
eLife.

PDF Fast sampling-based inference in balanced neuronal networks.
G. Hennequin, L. Aitchison and M. Lengyel (2014).
Advances in Neural Information Processing Systems 27.

PDF Analog memories in a balanced rate-based network of E-I neurons.
D. Festa, G. Hennequin and M. Lengyel (2014).
Advances in Neural Information Processing Systems 27.

PDF Optimal control of transient dynamics in balanced networks supports generation of complex movements.
G. Hennequin, T.P. Vogels* and W. Gerstner* (2014) [* = Co-senior author].
Neuron - PDF Suppl. info. - see also the preview article by Alfonso Renart.

Synaptic plasticity in neural networks needs homeostasis with a fast rate detector.
F. Zenke, G. Hennequin and W. Gerstner (2013).
PLoS Computational Biology.

Nonnormal amplification in random balanced neuronal networks.
G. Hennequin, T.P. Vogels and W. Gerstner (2012).
Physical Review E (+ e-print on arxiv.org)

STDP in adaptive neurons gives close-to-optimal information transmission.
G. Hennequin, W. Gerstner and J.-P. Pfister (2010).
Frontiers in Computational Neuroscience.

Code-specific policy gradient learning rules for spiking neurons.
H. Sprekeler, G. Hennequin and W. Gerstner (2009).
Advances in Neural Information Processing Systems 22.

Preprints

Fast sampling for Bayesian inference in neural circuits. G. Hennequin, L. Aitchison and M. Lengyel (2014). arXiv.org preprint (preliminary write-up of our Cosyne abstract).

Conference abstracts

Limits on fast, high-dimensional information processing in recurrent circuits. V. Rutten, G. Hennequin (2017). COSYNE abstract (Salt-Lake City, February).

How much to gain: targeted gain modulation facilitates learning in recurrent motor circuits. J. Stroud, G. Hennequin, M. Porter and T. Vogels (2017). COSYNE abstract (Salt-Lake City, February).

Dale's principle preserves sequentiality in neural circuits. A. Bernacchia, J. Fiser, G. Hennequin and M. Lengyel (2017). COSYNE abstract (Salt-Lake City, February).

GSM=SSN: recurrent neural circuits optimised for probabilistic inference. R. Echeveste, G. Hennequin and M. Lengyel (2017). COSYNE abstract (Salt-Lake City, February).

Cherchez les auxiliaires: interneurons are key for high-capacity attractor networks. D. Festa, G. Hennequin and M. Lengyel (2017). COSYNE abstract (Salt-Lake City, February).

Balance out of control: robust stabilization of recurrent circuits via inhibitory plasticity. G. Hennequin and T. Vogels (2016). COSYNE abstract (Salt-Lake City, February).

The dynamics of variability in nonlinear recurrent circuits. G. Hennequin and M. Lengyel (2014). COSYNE abstract (Salt-Lake City, February).

Fast sampling in recurrent neuronal circuits. G. Hennequin, L. Aitchison and M. Lengyel (2014). COSYNE abstract (Salt-Lake City, February).

Graded memories in balanced attractor networks. D. Festa, G. Hennequin and M. Lengyel (2014). COSYNE abstract (Salt-Lake City, February).

Transient collective dynamics in inhibition-stabilized motor circuits. G. Hennequin, T.P. Vogels and W. Gerstner (2013). COSYNE abstract (Salt-Lake City, February).

Nonnormal amplification in random balanced neuronal networks. G. Hennequin, T.P. Vogels and W. Gerstner (2012). COSYNE abstract (Salt Lake City, February).

Fast and richly structured activity in cortical networks with local inhibition. G. Hennequin, T.P. Vogels and W. Gerstner (2011). CNS abstract (Stockholm, July).

Plasticity and stability in recurrent neural networks. F. Zenke, G. Hennequin, H. Sprekeler, T. Vogels and W. Gerstner (2011). CNS abstract (Stockholm, July).

STDP interacts with neural dynamics to enhance information transmission. G. Hennequin, J.-P. Pfister and W. Gerstner (2009). COSYNE abstract (Salt Lake City, February).

Theses

Stability and amplification in plastic cortical circuits. G. Hennequin (2012). PhD thesis, EPFL.

Computational explorations in perceptual learning. G. Hennequin (2007). MSc dissertation, University of Edinburgh.