We calculate lower bounds on the size of sigmoidal neural networks
that approximate continuous functions. In particular, we show that
for the approximation of polynomials the network size has to grow
as $\Omega((\log k)^{1/4})$ where $k$ is the degree of the polynomials.
This bound is ... 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 >>>

It had previously been shown that generic cortical microcircuit
models can perform complex real-time computations on continuous
input streams, provided that these computations can be carried out
with a rapidly fading memory. We investigate in this article the
computational capability of such circuits in the ... more >>>