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- W2051145955 abstract "Abstract It has been hypothesised that recurrent neural networks, known as Cell Assemblies (CAs), are the neural basis of concepts. One of the essential properties of such a network is that neurons can participate in multiple CAs; the CAs overlap. We derive a correlatory Hebbian learning rule that makes the synaptic weight approximate the percentage of time the postsynaptic neuron fires when the presynaptic neuron fires. We then modify this rule to a compensatory rule that normalizes the total synaptic weight leaving a neuron. This rule allows sparse patterns to form CAs, and eases the formation of overlapping CAs. Simulations show the formation of overlapping CAs." @default.
- W2051145955 created "2016-06-24" @default.
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- W2051145955 date "2004-01-01" @default.
- W2051145955 modified "2023-09-23" @default.
- W2051145955 title "Overlapping cell assemblies from correlators" @default.
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- W2051145955 doi "https://doi.org/10.1016/j.neucom.2003.08.003" @default.
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