Vašek Chvátal, Mark Goldsmith
Motivated by EEG recordings of normal brain activity, we construct arbitrarily large McCulloch-Pitts neural networks that, without any external input, make every subset of their neurons fire in some iteration (and therefore in infinitely many iterations).
Vašek Chvátal, Mark Goldsmith, Nan Yang
In a pioneering classic, Warren McCulloch and Walter Pitts proposed a model of the central nervous system. Motivated by EEG recordings of normal brain activity, Chvátal and Goldsmith asked whether or not these dynamical systems can be engineered to produce trajectories which are irregular, disorderly, apparently unpredictable. We show that they cannot build weak pseudorandom functions.
Vašek Chvátal, Mark Goldsmith, Nan Yang
In a pioneering classic, Warren McCulloch and Walter Pitts proposed a model of the central nervous system; motivated by EEG recordings of normal brain activity, Chv\' atal and Goldsmith asked whether or not this model can be engineered to provide pseudorandom number generators. We supply evidence suggesting that the answer is negative.