6 papers
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…
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…
A Fully Tunable Ultra-Low Power Current-Mode Memory Cell in Standard CMOS Technology
Arthur Fyon, Loris Mendolia, Jean-Michel Redouté +2
This work introduces a fully tunable, ultra-low power unipolar memory cell inspired by the Schmitt-trigger comparator and designed in CMOS using only nine transistors. The proposed…
Neuromodulation supports robust rhythmic pattern transitions in degenerate central pattern generators with fixed connectivity
Arthur Fyon, Alessio Franci, Pierre Sacré +1
Many essential biological functions, such as breathing and locomotion, rely on the coordination of robust and adaptable rhythmic patterns, governed by specific network architecture…
Activity-dependent neuromodulation and calcium homeostasis cooperate to produce robust and modulable neuronal function
Arthur Fyon, Guillaume Drion
Neurons rely on two interdependent mechanisms, homeostasis and neuromodulation, to maintain robust and adaptable functionality. Calcium homeostasis stabilizes neuronal activity by…
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…