McCulloch-Pitts brains and pseudorandom functions
arXiv:1603.01573v13 citations
Originality Synthesis-oriented
AI Analysis
This addresses a theoretical limitation in neural modeling for computational neuroscience and cryptography, but is incremental as it builds on prior work.
The paper tackled the problem of whether McCulloch-Pitts brain models can generate irregular, unpredictable trajectories, and found that they cannot build weak pseudorandom functions.
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.