MiS Preprint Repository
We have decided to discontinue the publication of preprints on our preprint server end of 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.
Information-theoretic grounding of finite automata in neural systems
Thomas Wennekers and Nihat AyAbstract
We introduce a measure "stochastic interaction" that captures spatial and temporal signal properties in recurrent systems. The measure quantifies the Kullback-Leibler divergence of a Markov chain from a product of split chains for the single units. Maximization of stochastic interaction, also called "Temporal Infomax", is shown to induce almost deterministic dynamical systems for unconstrained Markov chains. If part of the units are clamped to prescribed stochastic processes providing external input, Temporal Infomax leads to finite automata, either completely deterministic or at most weakly non-deterministic. This way, computational capabilities may arise in neural systems.
- Received:
- 26.06.02
- Published:
- 26.06.02
- PACS:
- 84.35.+i, 87.19.La, 02.50.Ga
- Keywords:
- information theory, markov chains, neural networks