\begin{abstract}
A set $F$ of $n$-ary Boolean functions is called a pseudorandom function generator
(PRFG) if communicating
with a randomly chosen secret function from $F$ cannot be
efficiently distinguished from communicating with a truly random function.
We ask for the minimal hardware complexity of a PRFG. This question ... more >>>

In a great variety of neuron models neural inputs are
combined using the summing operation. We introduce the concept of
multiplicative neural networks which contain units that multiply
their inputs instead of summing them and, thus, allow inputs to
interact nonlinearly. The class of multiplicative networks
comprises such widely known ... more >>>

We study networks of spiking neurons that use the timing of pulses
to encode information. Nonlinear interactions model the spatial
groupings of synapses on the dendrites and describe the computations
performed at local branches. We analyze the question of how many
examples these networks must ... more >>>