Evolving spiking networks with variable resistive memories.
Evol Comput
; 22(1): 79-103, 2014.
Article
em En
| MEDLINE
| ID: mdl-23614774
ABSTRACT
Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Inteligência Artificial
/
Dispositivos de Armazenamento em Computador
/
Metodologias Computacionais
/
Serviços de Informação
Idioma:
En
Revista:
Evol Comput
Assunto da revista:
BIOLOGIA
Ano de publicação:
2014
Tipo de documento:
Article
País de afiliação:
Reino Unido