Matthias Krause, Stefan Lucks

\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 ...
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Michael Schmitt

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 ...
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Michael Schmitt

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 ...
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