Correlations strike back (again): the case of associative memory retrieval Correlations strike back (again): the case of associative memory retrieval
Paper summary 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).
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Correlations strike back (again): the case of associative memory retrieval
Savin, Cristina and Dayan, Peter and Lengyel, Máté
Neural Information Processing Systems Conference - 2013 via Bibsonomy
Keywords: dblp


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