Exact(17)
Another learning mechanism of biological synapses is spike-timing-dependent plasticity (STDP) [29], which implies that the change of synaptic weight is a strong function of the timing between the pre- and post-neuron spikes.
begin{aligned} {y}_{i}=sum _{n=1}^{m} W_{in}X_n end{aligned} (1 where ({y}_{i}) is the summation of synaptic weight (W_{in}) (between the input neuron n and the hidden neuron i) multiplied by the outputs of each individual neuron (X_n) and m is the number of neurons.
Simulations illustrated bistability of synaptic weight.
Bistability of synaptic weight was observed.
This positive feedback loop can generate bistability of synaptic weight.
Bistability of synaptic weight was generated by this feedback.
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total percentage of synaptic weights.
b The evolution of synaptic weights map.
Meanwhile, the change of synaptic weights in STDP has a close relationship with the relative time of presynaptic/postsynaptic stimulus.
Similar mean field models have been made, but in terms of synaptic weights; see, for example Yger and Gilson [32].
Two distinct classes of synaptic weights were implemented in the network, standard (S) and strong cross-excitation (SCE) (Fig. 5).
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