# Spontaneous Emergence of Computation in Network Cascades

arXiv: https://arxiv.org/abs/2204.11956 · HTML: https://mlsanity.com/paper/2204.11956/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 59 references that are in the map.
- First posted on arXiv 25 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [The physics of brain network structure, function, and control](https://mlsanity.com/paper/1809.06441/) (2018)
- [Optimal percentage of inhibitory synapses in multi-task learning](https://mlsanity.com/paper/1508.05707/) (2015)
- [Universal Boolean Logic in Cascading Networks](https://mlsanity.com/paper/1910.10472/) (2019)
- [Brain, Rain and Forest Fires -- What is critical about criticality: In praise of the correlation function](https://mlsanity.com/paper/2106.09389/) (2021)

## Abstract (arXiv)

Neuronal network computation and computation by avalanche supporting networks are of interest to the fields of physics, computer science (computation theory as well as statistical or machine learning) and neuroscience. Here we show that computation of complex Boolean functions arises spontaneously in threshold networks as a function of connectivity and antagonism (inhibition), computed by logic automata (motifs) in the form of computational cascades. We explain the emergent inverse relationship between the computational complexity of the motifs and their rank-ordering by function probabilities due to motifs, and its relationship to symmetry in function space. We also show that the optimal fraction of inhibition observed here supports results in computational neuroscience, relating to optimal information processing.