We introduce a model for analog computation with discrete
time in the presence of analog noise
that is flexible enough to cover the most important concrete
cases, such as noisy analog neural nets and networks of spiking neurons.
This model subsumes the classical model for digital computation in
the presence of noise.
We show that the presence of arbitrarily small amounts of analog noise
reduces the power of analog computational models to that of finite
automata, and we also prove a new type of upper bound for the
VC-dimension of computational models with analog noise.