# Symphony of high-dimensional brain

## TL;DR

This paper discusses the ongoing debate in neuroscience and AI about the role of high-dimensional brain structures, emphasizing the importance of real data distributions over theoretical models and exploring implications for machine learning and neuroscience.

## Contribution

It provides a comprehensive analysis of diverse opinions on high-dimensional brain research and discusses potential impacts on future machine learning and neuroscience methodologies.

## Key findings

- Highlighting the difference between theoretical and real data distributions.
- Identifying high-dimensional pitfalls in neuroscience.
- Proposing directions for the simplicity revolution in AI and neuroscience.

## Abstract

This paper is the final part of the scientific discussion organised by the Journal "Physics of Life Rviews" about the simplicity revolution in neuroscience and AI. This discussion was initiated by the review paper "The unreasonable effectiveness of small neural ensembles in high-dimensional brain". Phys Life Rev 2019, doi 10.1016/j.plrev.2018.09.005, arXiv:1809.07656. The topics of the discussion varied from the necessity to take into account the difference between the theoretical random distributions and "extremely non-random" real distributions and revise the common machine learning theory, to different forms of the curse of dimensionality and high-dimensional pitfalls in neuroscience. V. K{\r{u}}rkov{\'a}, A. Tozzi and J.F. Peters, R. Quian Quiroga, P. Varona, R. Barrio, G. Kreiman, L. Fortuna, C. van Leeuwen, R. Quian Quiroga, and V. Kreinovich, A.N. Gorban, V.A. Makarov, and I.Y. Tyukin participated in the discussion. In this paper we analyse the symphony of opinions and the possible outcomes of the simplicity revolution for machine learning and neuroscience.