# Energy-Efficient Information Representation in MNIST Classification Using Biologically Inspired Learning

arXiv: https://arxiv.org/abs/2603.00588 · HTML: https://mlsanity.com/paper/2603.00588/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 28 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [On Variational Bounds of Mutual Information](https://mlsanity.com/paper/1905.06922/) (2019)
- [Opening the Black Box of Deep Neural Networks via Information](https://mlsanity.com/paper/1703.00810/) (2017)
- [LLM Pruning and Distillation in Practice: The Minitron Approach](https://mlsanity.com/paper/2408.11796/) (2024)
- [To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review](https://mlsanity.com/paper/2304.09355/) (2023)
- [Survey on Deep Neural Networks in Speech and Vision Systems](https://mlsanity.com/paper/1908.07656/) (2019)
- [Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps](https://mlsanity.com/paper/2406.18808/) (2024)
- [Activation Learning by Local Competitions](https://mlsanity.com/paper/2209.13400/) (2022)
- [A theoretical framework for learning through structural plasticity](https://mlsanity.com/paper/2307.11735/) (2023)

## Built on by (most cited first, 2 of 2)

- [Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations](https://mlsanity.com/paper/2603.03234/) (2026)
- [Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints](https://mlsanity.com/paper/2607.16027/) (2026)

## Abstract (arXiv)

Efficient representation learning is essential for optimal information storage and classification. However, it is frequently overlooked in artificial neural networks (ANNs). This neglect results in networks that can become overparameterized by factors of up to 13, increasing redundancy and energy consumption. As the demand for large language models (LLMs) and their scale increase, these issues are further highlighted, raising significant ethical and environmental concerns. We analyze our previously developed biologically inspired learning rule using information-theoretic concepts, evaluating its efficiency on the MNIST classification task. The proposed rule, which emulates the brain's structural plasticity, naturally prevents overparameterization by optimizing synaptic usage and retaining only the essential number of synapses. Furthermore, it outperforms backpropagation (BP) in terms of efficiency and storage capacity. It also eliminates the need for pre-optimization of network architecture, enhances adaptability, and reflects the brain's ability to reserve 'space' for new memories. This approach advances scalable and energy-efficient AI and provides a promising framework for developing brain-inspired models that optimize resource allocation and adaptability.