Code-specific policy gradient rules for spiking neurons. - Henning Sprekeler; Guillaume Hennequin; Wulfram Gerstner (NIPS, 2009)

STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission. - Guillaume Hennequin; Wulfram Gerstner; Jean-Pascal Pfister (Frontiers Comput. Neurosci., 2010)

Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector. - Friedemann Zenke; Guillaume Hennequin; Wulfram Gerstner (PLoS Comput. Biol., 2013)

Analog Memories in a Balanced Rate-Based Network of E-I Neurons. - Dylan Festa; Guillaume Hennequin; Máté Lengyel (NIPS, 2014)

Fast Sampling-Based Inference in Balanced Neuronal Networks. - Guillaume Hennequin; Laurence Aitchison; Máté Lengyel (NIPS, 2014)

Information Transmission in Dynamical Networks: The Normal Network Case. - Giacomo Baggio; Virginia Rutten; Guillaume Hennequin; Sandro Zampieri (CDC, 2018)

Exact natural gradient in deep linear networks and its application to the nonlinear case. - Alberto Bernacchia; Máté Lengyel; Guillaume Hennequin (NeurIPS, 2018)

Efficient Communication over Complex Dynamical Networks. - Giacomo Baggio; Virginia Rutten; Guillaume Hennequin; Sandro Zampieri (CoRR, 2019)

Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. - Kristopher T. Jensen; Ta-Chu Kao; Marco Tripodi; Guillaume Hennequin (NeurIPS, 2020)

Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. - Virginia Rutten; Alberto Bernacchia; Maneesh Sahani; Guillaume Hennequin (NeurIPS, 2020)

Automatic differentiation of Sylvester, Lyapunov, and algebraic Riccati equations. - Ta-Chu Kao; Guillaume Hennequin (CoRR, 2020)

Scalable Bayesian GPFA with automatic relevance determination and discrete noise models. - Kristopher T. Jensen; Ta-Chu Kao; Jasmine Stone; Guillaume Hennequin (NeurIPS, 2021)

Natural continual learning: success is a journey, not (just) a destination. - Ta-Chu Kao; Kristopher T. Jensen; Gido van de Ven; Alberto Bernacchia; Guillaume Hennequin (NeurIPS, 2021)

Natural continual learning: success is a journey, not (just) a destination. - Ta-Chu Kao; Kristopher T. Jensen; Alberto Bernacchia; Guillaume Hennequin (CoRR, 2021)

iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data. - Marine Schimel; Ta-Chu Kao; Kristopher T. Jensen; Guillaume Hennequin (ICLR, 2022)

Fisher-Legendre (FishLeg) optimization of deep neural networks. - Jezabel R. Garcia; Federica Freddi; Stathi Fotiadis; Maolin Li; Sattar Vakili; Alberto Bernacchia; Guillaume Hennequin (ICLR, 2023)

Learning interpretable control inputs and dynamics underlying animal locomotion. - Thomas Soares Mullen; Marine Schimel; Guillaume Hennequin; Christian K. Machens; Michael B. Orger; Adrien Jouary (ICLR, 2024)

Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization. - Davide Buffelli; Jamie McGowan; Wangkun Xu; Alexandru Cioba; Da-shan Shiu; Guillaume Hennequin; Alberto Bernacchia (NeurIPS, 2024)

Second-order forward-mode optimization of recurrent neural networks for neuroscience. - Youjing Yu; Rui Xia; Qingxi Ma; Máté Lengyel; Guillaume Hennequin (NeurIPS, 2024)

Efficient Model Compression Techniques with FishLeg. - Jamie McGowan; Wei Sheng Lai; Weibin Chen; Henry Aldridge; Jools Clarke; Jezabel Garcia; Rui Xia; Yilei Liang; Guillaume Hennequin; Alberto Bernacchia (CoRR, 2024)