  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.
