Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse
![Thumbnail](/dspace/bitstream/handle/2183/31380/2022_Bodgan_Martin_Learning_algorithms_for_spiking_neural_networks.pdf.jpg?sequence=5&isAllowed=y)
Use este enlace para citar
http://hdl.handle.net/2183/31380
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
Colecciones
Metadatos
Mostrar el registro completo del ítemTítulo
Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulseAutor(es)
Fecha
2022Cita bibliográfica
Bogdan, M. (2022) Learning algorithms for spiking neural networks: should one use learning algorithms from ANN/DL or neurological plausible learning? - A thought-provoking impulse. XLIII Jornadas de Automática: libro de actas, pp.201-207 https://doi.org/10.17979/spudc.9788497498418.0201
Resumen
[Abstract] Artificial Neural Networks (ANN) and Machine Learning (ML) currently also known as Deep
Learning (DL) became more and more important in industrial applications during the last decade. This is due to new possibilities by strongly increased available computational power in connection with a renaissance of ANN in terms of so-called Deep Learning (DL). As DL requires especially for Big Data extreme computational power, the question of resource preserving methods came recently into the focus. Also, the often propagated intelligence of DL resp. "Cognitive Computing" in terms of contextual information processing is more often discussed since it is effectively missed in DL solutions. One option to overcome both challenges might be the third generation of ANNs: Spiking Neural Networks (SNN). But since SNN training methods are slow compared to DL learning algorithms, the question of the way how to learn SNNs arose. We will discuss different aspects of learning algorithms for SNNs: Is it useful to adopt DL learning algorithms to SNN or not, especially if one will preserve the "cognitive" functions of
SNNs?
Palabras clave
Spiking neural networks
Learning algorithms
STDP
Hebbian learning
Deep learning
Cognitive computing
Artificial neural networks
Learning algorithms
STDP
Hebbian learning
Deep learning
Cognitive computing
Artificial neural networks
Versión del editor
Derechos
Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es
ISBN
978-84-9749-841-8
Ítems relacionados
Mostrando ítems relacionados por Título, autor o materia.
-
Deep Learning Language Models for Music Analysis and Generation
Quintillán Quintillán, Daniel (2022)[Abstract] In this project, we tackle the problem of predicting the next note in a monophonic musical piece. We choose a symbolic representation and extract it from digital sheet music. The problem is approached as four ... -
Comportamientos visuales sencillos en robots móviles Ranger mediante aprendizaje por refuerzo
García Cerqueiras, Aarón (2022)[Resumen] En este trabajo se desarrolla un sistema que permite generar, mediante aprendizaje por refuerzo, comportamientos basados en las imágenes capturadas por una cámara en un robot capaz de moverse por sí mismo. A ... -
Integración basada en estándares de un laboratorio remoto en una plataforma de gestión de aprendizaje
Lucena, Eduardo; Ruano Ruano, Ildefonso; Estévez, Elisabet; Gómez Ortega, Juan; Gámez García, Javier (Universidade da Coruña. Servizo de Publicacións, 2023)[Resumen] Los laboratorios de tipo online tienen cada vez más aceptación dentro de la educación universitaria relacionada con las ciencias, tecnologías, ingenierías y matemáticas (CTIM o STEM en inglés), donde el trabajo ...