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 pre- and postsynaptic activation. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron which is called homosynaptic plasticity but also on further remote synapses of the pre- and 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.