Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2015

Abstract

Episodic memory is a significant part of cognition for reasoning and decision making. Retrieval in episodic memory depends on the order relationships of memory items which provides flexibility in reasoning and inferences regarding sequential relations for spatio-temporal domain. However, it is still unclear how they are encoded and how they differ from representations in other types of memory like semantic or procedural memory. This paper presents a neural model of sequential representation and inferences on episodic memory. It contrasts with the common views on sequential representation in neural networks that instead of maintaining transitions between events to represent sequences, they are represented as patterns of activation profiles wherein similarity matching operations support inferences and reasoning. Using an extension of multi-channel multi-layered adaptive resonance theory (ART) network, it is shown how episodic memory can be formed and learnt so that the memory performance becomes dependent on the order and the interchange of memory cues. We present experiments as a proof of concepts to show that the model contrasts sequential representations in semantic memory with those in episodic memory and the model can exhibit transitive inferences consistent with human and animals data.

Keywords

Episodic memory, Adaptive resonance theory, Transitive inference

Discipline

Computer Engineering | Databases and Information Systems | Programming Languages and Compilers

Research Areas

Data Science and Engineering

Publication

Neurocomputing

Volume

161

First Page

229

Last Page

242

ISSN

0925-2312

Identifier

10.1016/j.neucom.2015.02.038

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.neucom.2015.02.038

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