Possible role of synchronous input spike trains in controlling the function of neural networks
To investigate how the temporal structure of neuronal activity affects the function of neural networks, we consider the effects of synchronous firing inputs on two typical functions of neural networks: winner-take-all competition and associative memory. Using a network of integrate-and-fire neurons, we study their effect. The results show that uncorrelated discharge among neuronal inputs allows the standard winner-take-all competition, whereas synchronization of the neuronal inputs prevents it. In the case of the associative memory model, we find that the timing of the next retrieval can be controlled by such a synchronyasynchrony transition of external inputs.
synchronization winner-take-all associative memory temporal coding