  We study a learning rule based upon the temporal correlation (weighted by a
learning kernel) between incoming spikes and the internal state of the
postsynaptic neuron, building upon previous studies of spike timing dependent
synaptic plasticity (\cite{KGvHW,KGvH1,vH}). Our learning rule for the synaptic
weight $w_{ij}$ is $$ \dot w_{ij}(t)= \epsilon \int_{-\infty}^\infty
\frac{1}{T_l} \int_{t-T_l}^t \sum_\mu \delta(\tau+s-t_{j,\mu}) u(\tau) d\tau\
\Gamma(s)ds $$ where the $t_{j,\mu}$ are the arrival times of spikes from the
presynaptic neuron $j$ and the function $u(t)$ describes the state of the
postsynaptic neuron $i$. Thus, the spike-triggered average contained in the
inner integral is weighted by a kernel $\Gamma(s)$, the learning window,
positive for negative, negative for positive values of the time diffence $s$
between post- and presynaptic activity. An antisymmetry assumption for the
learning window enables us to derive analytical expressions for a general class
of neuron models and to study the changes in input-output relationships
following from synaptic weight changes. This is a genuinely non-linear effect
(\cite{SMA}).
