270 citations · 842 across the 21 of their papers we have counts for
21 papers
Curvature and Torsion estimation of 3D functional data: A geometric approach to build the mean shape under the Frenet Serret framework
Juhyun Park, Nicolas Brunel, Perrine Chassat
The analysis of curves has been routinely dealt with using tools from functional data analysis. However its extension to multi-dimensional curves poses a new challenge due to its i…
Storage capacity of networks with discrete synapses and sparsely encoded memories
Yu Feng, Nicolas Brunel
Attractor neural networks (ANNs) are one of the leading theoretical frameworks for the formation and retrieval of memories in networks of biological neurons. In this framework, a p…
Forgetting leads to chaos in attractor networks
Ulises Pereira-Obilinovic, Johnatan Aljadeff, Nicolas Brunel
Attractor networks are an influential theory for memory storage in brain systems. This theory has recently been challenged by the observation of strong temporal variability in neur…
Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models
Salim I. Amoukou, Nicolas J. B Brunel
To explain the decision of any model, we extend the notion of probabilistic Sufficient Explanations (P-SE). For each instance, this approach selects the minimal subset of features…
Accurate Shapley Values for explaining tree-based models
Salim I. Amoukou, Nicolas J-B. Brunel, Tangi Salaün
Shapley Values (SV) are widely used in explainable AI, but their estimation and interpretation can be challenging, leading to inaccurate inferences and explanations. As a starting…
Bayesian reconstruction of memories stored in neural networks from their connectivity
Sebastian Goldt, Florent Krzakala, Lenka Zdeborová +1
The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such…