[Paper Review] A computational account of dreaming: learning and memory consolidation

Zhang, Qi|arXiv (Cornell University)|Feb 4, 2026

Sleep and Wakefulness Research0 citations

TL;DR

The paper presents a cognitive and computational model in which random internal signals during dream sleep contribute to learning and memory consolidation, aligning dreaming with waking brain activity rather than functionless noise.

ABSTRACT

A number of studies have concluded that dreaming is mostly caused by randomly arriving internal signals because "dream contents are random impulses", and argued that dream sleep is unlikely to play an important part in our intellectual capacity. On the contrary, numerous functional studies have revealed that dream sleep does play an important role in our learning and other intellectual functions. Specifically, recent studies have suggested the importance of dream sleep in memory consolidation, following the findings of neural replaying of recent waking patterns in the hippocampus. The randomness has been the hurdle that divides dream theories into either functional or functionless. This study presents a cognitive and computational model of dream process. This model is simulated to perform the functions of learning and memory consolidation, which are two most popular dream functions that have been proposed. The simulations demonstrate that random signals may result in learning and memory consolidation. Thus, dreaming is proposed as a continuation of brain's waking activities that processes signals activated spontaneously and randomly from the hippocampus. The characteristics of the model are discussed and found in agreement with many characteristics concluded from various empirical studies.

Motivation & Objective

• Motivate the debate on whether dreaming is functionally important or random.
• Propose a cognitive and computational framework for dream processing.
• Demonstrate via simulations that random internal signals can support learning and memory consolidation.
• Relate model predictions to empirical findings on hippocampal replay and dream reports.

Proposed method

• Develop a computational model that treats dream processes as continuation of waking brain activity.
• Use random signals arriving from hippocampal activity to drive learning dynamics.
• Simulate the model to test learning and memory consolidation outcomes.
• Discuss the characteristics of the model and compare them with empirical dream research.
• Analyze how spontaneous signals can resemble neural replay during sleep.

Experimental results

Research questions

• RQ1 Can randomly generated signals during sleep drive learning and memory consolidation соответствии to waking activity?
• RQ2 Do dream-like processes contribute to cognitive functions such as memory consolidation rather than being mere random impulses?
• RQ3 How do simulated dream dynamics align with empirical findings on hippocampal replay and dream content?
• RQ4 What characteristics of the proposed model reproduce empirical observations from dream and sleep studies?

Key findings

• Simulations indicate that random signals can result in learning.
• The model supports memory consolidation as a function of dream-like processing.
• Dreaming is proposed as a continuation of waking brain activity driven by spontaneously activated hippocampal signals.
• Model characteristics align with several empirical findings from dream and sleep studies.