Correlations strike back (again): the case of associative memory retrievalCorrelations strike back (again): the case of associative memory retrievalSavin, Cristina and Dayan, Peter and Lengyel, Máté2013
Paper summarynipsreviewsThe paper investigates how correlation among synaptic weights, not correlation among neural activity, influences the retrieval performance of auto-associative memory. Authors studied two types of well-known learning rules, additive learning rule (e.g., Hebbian learning) and palimpsest learning rule (e.g., cascade learning), and showed that synaptic correlations are induced in most of the cases. They also investigated optimal retrieval dynamics and showed that there exists a local version of dynamics that can be implemented in neural networks (except for an XOR cascade model).
The paper investigates how correlation among synaptic weights, not correlation among neural activity, influences the retrieval performance of auto-associative memory. Authors studied two types of well-known learning rules, additive learning rule (e.g., Hebbian learning) and palimpsest learning rule (e.g., cascade learning), and showed that synaptic correlations are induced in most of the cases. They also investigated optimal retrieval dynamics and showed that there exists a local version of dynamics that can be implemented in neural networks (except for an XOR cascade model).