Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  apply to oscillatory networks a class of learning rules in which synaptic ights change proportional to pre- and post-synaptic activity, with a kernel A(τ) measuring the effect for a postsynaptic spike a time τ after the presynaptic one. The resulting synaptic matrices have an outer-product form in which the oscillating patterns are represented as complex vectors. In a simple model, the even part of A(τ) enhances the resonant response to learned stimulus by reducing the effective damping, while the odd part determines the frequency of oscillation.  relate our model to the olfactory cortex and hippocampus and their presumed roles in forming associative memories and input representations.
['Silvia Scarpetta', 'Zhaoping Li', 'John Hertz']
I am writing a research paper on a idea Spike-Timing-Dependent Learning for Oscillatory Networks
[{"from": "human", "value": "I am writing a research paper on a idea Spike-Timing-Dependent Learning for Oscillatory Networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  apply to oscillatory networks a class of learning rules in which synaptic ights change proportional to pre- and post-synaptic activity, with a kernel A(\u03c4) measuring the effect for a postsynaptic spike a time \u03c4 after the presynaptic one. The resulting synaptic matrices have an outer-product form in which the oscillating patterns are represented as complex vectors. In a simple model, the even part of A(\u03c4) enhances the resonant response to learned stimulus by reducing the effective damping, while the odd part determines the frequency of oscillation.  relate our model to the olfactory cortex and hippocampus and their presumed roles in forming associative memories and input representations."}]