Biologically plausible learning in neural networks with modulatory feedback.
Neural Netw
; 88: 32-48, 2017 Apr.
Article
em En
| MEDLINE
| ID: mdl-28189041
ABSTRACT
Although Hebbian learning has long been a key component in understanding neural plasticity, it has not yet been successful in modeling modulatory feedback connections, which make up a significant portion of connections in the brain. We develop a new learning rule designed around the complications of learning modulatory feedback and composed of three simple concepts grounded in physiologically plausible evidence. Using border ownership as a prototypical example, we show that a Hebbian learning rule fails to properly learn modulatory connections, while our proposed rule correctly learns a stimulus-driven model. To the authors' knowledge, this is the first time a border ownership network has been learned. Additionally, we show that the rule can be used as a drop-in replacement for a Hebbian learning rule to learn a biologically consistent model of orientation selectivity, a network which lacks any modulatory connections. Our results predict that the mechanisms we use are integral for learning modulatory connections in the brain and furthermore that modulatory connections have a strong dependence on inhibition.
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Assunto principal:
Reconhecimento Automatizado de Padrão
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Redes Neurais de Computação
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Retroalimentação
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Aprendizado de Máquina
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Modelos Neurológicos
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article