Learning and Memory: Computational Models

P. B. Sederberg; K. A. Norman

doi:10.1016/B978-0-08-045396-5.00140-8

Learning and Memory: Computational Models

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

The goal of learning and memory research is to understand how we store and retrieve information based on our experiences. Computational models provide formal implementations of memory theories that attempt to predict both behavior and neural data. This article describes computational models of declarative memory, including episodic memory (memory for specific events) and semantic memory (memory for meanings), with a particular focus on the role of context in supporting both types of memory.

Original language	English (US)
Title of host publication	Encyclopedia of Behavioral Neuroscience, Three-Volume Set, 1-3
Publisher	Elsevier
Pages	V2-145-V2-153
Volume	2
ISBN (Electronic)	9780080453965
ISBN (Print)	9780080914558
DOIs	https://doi.org/10.1016/B978-0-08-045396-5.00140-8
State	Published - Jan 1 2010

All Science Journal Classification (ASJC) codes

General Medicine
General Neuroscience

Keywords

Computational models
Context
Cortex
Episodic memory
Hippocampus
Learning
Memory
Neural networks
Recall
Recognition
Semantic memory
Synaptic plasticity

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10.1016/B978-0-08-045396-5.00140-8

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KW - Cortex

KW - Episodic memory

KW - Hippocampus

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KW - Memory

KW - Neural networks

KW - Recall

KW - Recognition

KW - Semantic memory

KW - Synaptic plasticity

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PB - Elsevier

ER -

Learning and Memory: Computational Models

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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