A novel stochastic Hebb-like learning rule for neural networks

We present a novel stochastic Hebb-like learning rule for neural networks. This learning rule is stochastic with respect to the selection of the time points when a synaptic modification is induced by preand postsynaptic activation. Moreover, the learning rule does not only affect the synapse between preand postsynaptic neuron which is called homosynaptic plasticity but also on further remote synapses of the preand postsynaptic neuron. This form of plasticity has recently come into the light of interest of experimental investigations and is called heterosynaptic plasticity. Our learning rule gives a qualitative explanation of this kind of synaptic modification.