Cognitive algorithms and systems of episodic memory, semantic memory and their learnings

• URL: http://arxiv.org/abs/2602.07261v1
• Date: Fri, 06 Feb 2026 23:22:52 GMT
• Title: Cognitive algorithms and systems of episodic memory, semantic memory and their learnings
• Authors: Qi Zhang,
• Abstract summary: Declarative memory is made up of two dissociated parts: episodic memory and semantic memory.<n>Lesions in the hippocampus often result in various impairments of explicit memory.<n>This chapter reviews several cognitive systems that are centered to mimic explicit memory.
• Score: 8.156069657157342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Declarative memory, the memory that can be "declared" in words or languages, is made up of two dissociated parts: episodic memory and semantic memory. This dissociation has its neuroanatomical basis episodic memory is mostly associated with the hippocampus and semantic memory with the neocortex. The two memories, on the other hand, are closely related. Lesions in the hippocampus often result in various impairments of explicit memory, e.g., anterograde, retrograde and developmental amnesias, and semantic learning deficit. These impairments provide opportunities for us to understand how the two memories may be acquired, stored and organized. This chapter reviews several cognitive systems that are centered to mimic explicit memory, and other systems that are neuroanatomically based and are implemented to simulate those memory impairments mentioned above. This review includes: the structures of the computational systems, their learning rules, and their simulations of memory acquisition and impairments.

Related papers

• A consequence of failed sequential learning: A computational account of developmental amnesia [8.156069657157342]
  Developmental amnesia has been discovered to occur in children with hippocampal atrophy.<n>This unique combination of characteristics seems to challenge the understanding that early loss of episodic memory may impede cognitive development.<n>No computational model has been reported that is able to mimic the unique combination of characteristics.
  arXiv  Detail & Related papers  (2026-02-13T02:55:06Z)
• The AI Hippocampus: How Far are We From Human Memory? [77.04745635827278]
  Implicit memory refers to the knowledge embedded within the internal parameters of pre-trained transformers.<n>Explicit memory involves external storage and retrieval components designed to augment model outputs with dynamic, queryable knowledge representations.<n>Agentic memory introduces persistent, temporally extended memory structures within autonomous agents.
  arXiv  Detail & Related papers  (2026-01-14T03:24:08Z)
• AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents [69.39123054975218]
  Memory serves as the pivotal nexus bridging past and future.<n>Recent research on autonomous agents has increasingly focused on designing efficient memory by drawing on cognitive neuroscience.
  arXiv  Detail & Related papers  (2025-12-29T10:01:32Z)
• Agentic Learner with Grow-and-Refine Multimodal Semantic Memory [50.81667005063605]
  ViLoMem is a dual-stream memory framework that constructs compact, schema-based memory.<n>It encodes visual distraction patterns and logical reasoning errors, enabling MLLMs to learn from their successful and failed experiences.
  arXiv  Detail & Related papers  (2025-11-26T18:55:08Z)
• Evaluating Long-Term Memory for Long-Context Question Answering [100.1267054069757]
  We present a systematic evaluation of memory-augmented methods using LoCoMo, a benchmark of synthetic long-context dialogues annotated for question-answering tasks.<n>Our findings show that memory-augmented approaches reduce token usage by over 90% while maintaining competitive accuracy.
  arXiv  Detail & Related papers  (2025-10-27T18:03:50Z)
• MemGen: Weaving Generative Latent Memory for Self-Evolving Agents [57.1835920227202]
  We propose MemGen, a dynamic generative memory framework that equips agents with a human-esque cognitive faculty.<n>MemGen enables agents to recall and augment latent memory throughout reasoning, producing a tightly interwoven cycle of memory and cognition.
  arXiv  Detail & Related papers  (2025-09-29T12:33:13Z)
• Memorization and Knowledge Injection in Gated LLMs [8.305942415868042]
  Large Language Models (LLMs) currently struggle to sequentially add new memories and integrate new knowledge.<n>Memory Embedded in Gated LLMs (MEGa) injects event memories directly into the weights of LLMs.<n>During inference, a gating mechanism activates relevant memory weights by matching query embeddings to stored memory embeddings.
  arXiv  Detail & Related papers  (2025-04-30T00:28:32Z)
• MADial-Bench: Towards Real-world Evaluation of Memory-Augmented Dialogue Generation [15.64077949677469]
  We present a novel Memory-Augmented Dialogue Benchmark (MADail-Bench) to evaluate the effectiveness of memory-augmented dialogue systems (MADS) The benchmark assesses two tasks separately: memory retrieval and memory recognition with the incorporation of both passive and proactive memory recall data. Results from cutting-edge embedding models and large language models on this benchmark indicate the potential for further advancement.
  arXiv  Detail & Related papers  (2024-09-23T17:38:41Z)
• A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model [0.0]
  We propose a novel bio-inspired memory model based on the hippocampus. It can learn memories, recall them from a cue and even forget memories when trying to learn others with the same cue. This work presents the first hardware implementation of a fully functional bio-inspired spike-based hippocampus memory model.
  arXiv  Detail & Related papers  (2022-06-10T07:48:29Z)
• Assessment of Unconsciousness for Memory Consolidation Using EEG Signals [20.486281623777774]
  We assess the unconsciousness in terms of memory consolidation using electroencephalogram signals. spindle power in central, parietal, occipital regions during unconsciousness was positively correlated with the performance of location memory. There was also a negative correlation between delta connectivity and word-pairs memory, alpha connectivity and location memory, and spindle connectivity and word-pairs memory.
  arXiv  Detail & Related papers  (2020-05-15T06:49:42Z)
• Self-Attentive Associative Memory [69.40038844695917]
  We propose to separate the storage of individual experiences (item memory) and their occurring relationships (relational memory) We achieve competitive results with our proposed two-memory model in a diversity of machine learning tasks.
  arXiv  Detail & Related papers  (2020-02-10T03:27:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.