3 papers
cs.LG2026
Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications
Julien Brandoit, Arthur Fyon, Damien Ernst +1
Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term dependencies remains challengi…
cs.AR2026
Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
Arthur Fyon, Julien Brandoit, Loris Mendolia +3
Always-on AI applications, from environmental sensors to biomedical implants, require ultra-low power consumption. Analog circuits offer a path to sub-microwatt inference, yet exis…
q-bio.NC2026
Fast reconstruction of degenerate populations of conductance-based neuron models from spike times
Julien Brandoit, Damien Ernst, Guillaume Drion +1
Inferring the biophysical parameters of conductance-based models (CBMs) from experimentally accessible recordings remains a central challenge in computational neuroscience. Spike t…