\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 is motivated ... 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 receive during learning to be ... more >>>