Communications at congresses:
Probabilistic versus incremental presynaptic learning in biological plausible synapses
Year:2011
Research Areas
- Engineering
Information
|
Abstract
|
|
|---|---|
| In this paper, the presynaptic rule, a classical rule for hebbian learning, is revisited. It is shown that the presynaptic rule exhibits relevant synaptic properties like synaptic directionality, and LTP metaplasticity (long-term potentiation threshold metaplasticity). With slight modifications, the presynaptic model also exhibits metaplasticity of the long-term depression threshold, being also consistent with Artola, Brocher and Singer?s (ABS) influential model. Two asymptotically equivalent versions of the presynaptic rule were adopted for this analysis: the first one uses an incremental equation while the second, conditional probabilities. Despite their simplicity, both types of presynaptic rules exhibit sophisticated biological properties, specially the probabilistic version. | |
|
International
|
Si |
|
Congress
|
OWINAC 2011 |
|
|
960 |
|
Place
|
La Palma, Islas Canarias, España |
|
Reviewers
|
Si |
|
ISBN/ISSN
|
978-3-642-21343-4 |
|
|
10.1007/978-3-642-21344-1_9 |
|
Start Date
|
30/05/2011 |
|
End Date
|
03/06/2011 |
|
From page
|
80 |
|
To page
|
89 |
|
|
Foundations on Natural and Artificial Computation Lecture Notes in Computer Science, 2011, Volume 6686/2011 |
Open Access publication
Participants
- Autor: Francisco J Ropero Peláez Universidad Federal de ABC, Brasil
- Autor: Diego Andina De la Fuente UPM
Research Group, Departaments and Institutes related
- Creador: Grupo de Investigación: Grupo de Automatización en Señal y Comunicaciones (GASC)
- Departamento: Señales, Sistemas y Radiocomunicaciones