# Neural Networks, Artificial Intelligence and the Computational Brain

arXiv: https://arxiv.org/abs/2101.08635 · HTML: https://mlsanity.com/paper/2101.08635/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2020.

## Built on by (most cited first, 3 of 3)

- [A Deep Transfer Learning Approach on Identifying Glitch Wave-form in Gravitational Wave Data](https://mlsanity.com/paper/2107.01863/) (2021)
- [DynaTab: Dynamic Feature Ordering as Neural Rewiring for High-Dimensional Tabular Data](https://mlsanity.com/paper/2605.03430/) (2026)
- [Unsupervised anomaly detection in large-scale estuarine acoustic telemetry data](https://mlsanity.com/paper/2502.01543/) (2025)

## Abstract (arXiv)

In recent years, several studies have provided insight on the functioning of the brain which consists of neurons and form networks via interconnection among them by synapses. Neural networks are formed by interconnected systems of neurons, and are of two types, namely, the Artificial Neural Network (ANNs) and Biological Neural Network (interconnected nerve cells). The ANNs are computationally influenced by human neurons and are used in modelling neural systems. The reasoning foundations of ANNs have been useful in anomaly detection, in areas of medicine such as instant physician, electronic noses, pattern recognition, and modelling biological systems. Advancing research in artificial intelligence using the architecture of the human brain seeks to model systems by studying the brain rather than looking to technology for brain models. This study explores the concept of ANNs as a simulator of the biological neuron, and its area of applications. It also explores why brain-like intelligence is needed and how it differs from computational framework by comparing neural networks to contemporary computers and their modern day implementation.