# Exponential scaling of neural algorithms - a future beyond Moore's Law?

## TL;DR

This paper explores how advances in neurotechnologies and neural computation could lead to a new era of exponential scaling in computing, surpassing traditional transistor miniaturization limits.

## Contribution

It highlights the potential for neural computation and neurotechnologies to drive future exponential growth in computing beyond Moore's Law.

## Key findings

- Neurotechnologies are revitalizing neural computation applications.
- Deep learning and neuromorphic hardware exemplify new computing paradigms.
- Understanding the brain can catalyze future exponential growth in computing capabilities.

## Abstract

Although the brain has long been considered a potential inspiration for future computing, Moore's Law - the scaling property that has seen revolutions in technologies ranging from supercomputers to smart phones - has largely been driven by advances in materials science. As the ability to miniaturize transistors is coming to an end, there is increasing attention on new approaches to computation, including renewed enthusiasm around the potential of neural computation. This paper describes how recent advances in neurotechnologies, many of which have been aided by computing's rapid progression over recent decades, are now reigniting this opportunity to bring neural computation insights into broader computing applications. As we understand more about the brain, our ability to motivate new computing paradigms with continue to progress. These new approaches to computing, which we are already seeing in techniques such as deep learning and neuromorphic hardware, will themselves improve our ability to learn about the brain and accordingly can be projected to give rise to even further insights. This paper will describe how this positive feedback has the potential to change the complexion of how computing sciences and neurosciences interact, and suggests that the next form of exponential scaling in computing may emerge from our progressive understanding of the brain.

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Source: https://tomesphere.com/paper/1705.02042