Enhanced representation learning with temporal coding in sparsely spiking neural networks

Autor: Adrien Fois, Bernard Girau

Publikováno v: Frontiers in Computational Neuroscience, Vol 17 (2023)

Current representation learning methods in Spiking Neural Networks (SNNs) rely on rate-based encoding, resulting in high spike counts, increased energy consumption, and slower information transmission. In contrast, our proposed method, Weight-Tempora

Externí odkaz: https://doaj.org/article/734f4a18fdd7484fa6d5391dadb3b658

Configurable Embedded CPG-Based Control for Robot Locomotion

Autor: Jose Hugo Barron-Zambrano, Cesar Torres-Huitzil, Bernard Girau

Publikováno v: International Journal of Advanced Robotic Systems, Vol 9 (2012)

Recently, the development of intelligent robots has benefited from a deeper understanding of the biomechanics and neurology of biological systems. Researchers have proposed the concept of Central Pattern Generators (CPGs) as a mechanism for generatin

Externí odkaz: https://doaj.org/article/62bb339e52f74d77ae86fb8a9030c525

Unsupervised learning of visual representations using delay-weight spike-timing-dependent plasticity

Autor: Adrien Fois, Horacio Rostro-Gonzalez, Bernard Girau

Publikováno v: IEEE WCCI 2022-INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
2022 International Joint Conference on Neural Networks (IJCNN)
2022 International Joint Conference on Neural Networks (IJCNN), Jul 2022, Padua, Italy. ⟨10.1109/IJCNN55064.2022.9892486⟩

International audience; Unsupervised learning in Spiking Neural Networks (SNN) is performed by adjusting the synaptic parameters with spike-timing-dependent plasticity rules (STDP), that leverage the firing times of neurons. Commonly the targetted pa

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2382a0edd68db5e53b50e6376dc1fc79
https://inria.hal.science/hal-04052878

Novelty Detection in Images Using Vector Quantization with Topological Learning

Autor: Nicolas Hueber, Bernard Girau, Yann Bernard

Publikováno v: ICECS
ICECS 2020, 27th IEEE International Conference on Electronics Circuits and Systems
ICECS 2020, 27th IEEE International Conference on Electronics Circuits and Systems, Nov 2020, Glasgow/Virtual, United Kingdom

International audience; Novelty detection is a key component of biological vision systems, where its role is to extract critical elements for the agents survival from the massive amount of information present in his visual environment. Current vision

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a47a4936a3fd283aedba58207a0f7b70
https://doi.org/10.1109/icecs49266.2020.9294957

A fast algorithm to find Best Matching Units in Self-Organizing Maps

Autor: Nicolas Hueber, Yann Bernard, Bernard Girau

Publikováno v: ICANN 2020, 29th International Conference on Artificial Neural Networks
ICANN 2020, 29th International Conference on Artificial Neural Networks, Sep 2020, Bratislava, Slovakia
Artificial Neural Networks and Machine Learning – ICANN 2020 ISBN: 9783030616151
ICANN (2)

International audience; Self-Organizing Maps (SOM) are well-known unsupervised neural networks able to perform vector quantization while mapping an underlying regular neighbourhood structure onto the codebook. They are used in a wide range of applica

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e71d16f86ee6f9de69d4fcbfc4aa5aa8
https://hal.univ-lorraine.fr/hal-02984424/document