The computational power of formal models for

networks of spiking neurons is compared with

that of other neural network models based on

McCulloch Pitts neurons (i.e. threshold gates)

respectively sigmoidal gates. In particular it

is shown that networks of spiking neurons are

...
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RĂ¼diger Reischuk

For ordinary circuits with a fixed upper bound on the maximal fanin

of gates it has been shown that logarithmic redundancy is necessary and

sufficient to overcome random hardware faults.

Here, we consider the same question for unbounded fanin circuits that

in the noiseless case can compute ...
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Michael Schmitt

A neural network is said to be nonoverlapping if there is at most one

edge outgoing from each node. We investigate the number of examples

that a learning algorithm needs when using nonoverlapping neural

networks as hypotheses. We derive bounds for this sample complexity

in terms of the Vapnik-Chervonenkis dimension. ...
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Xi Chen, Igor Carboni Oliveira, Rocco Servedio

Let $U_{k,N}$ denote the Boolean function which takes as input $k$ strings of $N$ bits each, representing $k$ numbers $a^{(1)},\dots,a^{(k)}$ in $\{0,1,\dots,2^{N}-1\}$, and outputs 1 if and only if $a^{(1)} + \cdots + a^{(k)} \geq 2^N.$ Let THR$_{t,n}$ denote a monotone unweighted threshold gate, i.e., the Boolean function which takes ... more >>>

Alexander Kozachinskiy, Vladimir Podolskii

We suggest a generalization of Karchmer-Wigderson communication games to the multiparty setting. Our generalization turns out to be tightly connected to circuits consisting of threshold gates. This allows us to obtain new explicit constructions of such circuits for several functions. In particular, we provide an explicit (polynomial-time computable) log-depth monotone ... more >>>