41 citations · 41 across the 3 of their papers we have counts for
4 papers
Neural networks as decision trees: an analytical solution for learning and neural selectivity
Hugo Tissot, Jonas Ranft, Yves Boubenec
Nonlinear neural networks develop structured internal representations, yet how their geometry is determined by the tasks being learned remains poorly understood. Here, we develop a…
Emergence of Phonemic, Syntactic, and Semantic Representations in Artificial Neural Networks
Pierre Orhan, Pablo Diego-Simón, Emmnanuel Chemla +3
During language acquisition, children successively learn to categorize phonemes, identify words, and combine them with syntax to form new meaning. While the development of this beh…
Toward a realistic model of speech processing in the brain with self-supervised learning
Juliette Millet, Charlotte Caucheteux, Pierre Orhan +5
Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely…
Don't stop the training: continuously-updating self-supervised algorithms best account for auditory responses in the cortex
Pierre Orhan, Yves Boubenec, Jean-Rémi King
Over the last decade, numerous studies have shown that deep neural networks exhibit sensory representations similar to those of the mammalian brain, in that their activations linea…