# Engram Memory Encoding and Retrieval: A Neurocomputational Perspective

arXiv: https://arxiv.org/abs/2506.01659 · HTML: https://mlsanity.com/paper/2506.01659/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [Learning Sparse Neural Networks via Sensitivity-Driven Regularization](https://mlsanity.com/paper/1810.11764/) (2018)
- [Sparse Distributed Memory is a Continual Learner](https://mlsanity.com/paper/2303.11934/) (2023)
- [The Efficacy of L1 Regularization in Two-Layer Neural Networks](https://mlsanity.com/paper/2010.01048/) (2020)
- [From Neuronal Packets to Thoughtseeds: A Hierarchical Model of Embodied Cognition in the Global Workspace](https://mlsanity.com/paper/2408.15982/) (2024)
- [Sparsifying Parametric Models with L0 Regularization](https://mlsanity.com/paper/2409.03489/) (2024)
- [Computational models of learning and synaptic plasticity](https://mlsanity.com/paper/2412.05501/) (2024)
- [Stochastic Engrams for Efficient Continual Learning](https://mlsanity.com/paper/2503.21436/) (2025)

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

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Hebbian Memory-Augmented Recurrent Networks: Engram Neurons in Deep Learning](https://mlsanity.com/paper/2507.21474/) (2025)

## Abstract (arXiv)

Despite substantial research into the biological basis of memory, the precise mechanisms by which experiences are encoded, stored, and retrieved in the brain remain incompletely understood. A growing body of evidence supports the engram theory, which posits that sparse populations of neurons undergo lasting physical and biochemical changes to support long-term memory. Yet, a comprehensive computational framework that integrates biological findings with mechanistic models remains elusive. This work synthesizes insights from cellular neuroscience and computational modeling to address key challenges in engram research: how engram neurons are identified and manipulated; how synaptic plasticity mechanisms contribute to stable memory traces; and how sparsity promotes efficient, interference-resistant representations. Relevant computational approaches -- such as sparse regularization, engram gating, and biologically inspired architectures like Sparse Distributed Memory and spiking neural networks -- are also examined. Together, these findings suggest that memory efficiency, capacity, and stability emerge from the interaction of plasticity and sparsity constraints. By integrating neurobiological and computational perspectives, this paper provides a comprehensive theoretical foundation for engram research and proposes a roadmap for future inquiry into the mechanisms underlying memory, with implications for the diagnosis and treatment of memory-related disorders.