Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Neuronal models of associative memories are recurrent networks able to learn quickly patterns as stable states of the network. Their main acknowledged akness is related to catastrophic interference when too many or too close examples are stored. Based on biological data  have recently proposed a model resistant to some kinds of interferences related to heteroassociative learning.   report numerical experiments that highlight this robustness and demonstrate very good performances of memorization.  also discuss convergence of interests for such an adaptive mechanism for biological modeling and information processing in the domain of machine learning.
['Randa Kassab', 'Frédéric Alexandre']
I am writing a research paper on a idea A heteroassociative learning model robust to interference
[{"from": "human", "value": "I am writing a research paper on a idea A heteroassociative learning model robust to interference"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Neuronal models of associative memories are recurrent networks able to learn quickly patterns as stable states of the network. Their main acknowledged akness is related to catastrophic interference when too many or too close examples are stored. Based on biological data  have recently proposed a model resistant to some kinds of interferences related to heteroassociative learning.   report numerical experiments that highlight this robustness and demonstrate very good performances of memorization.  also discuss convergence of interests for such an adaptive mechanism for biological modeling and information processing in the domain of machine learning."}]