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Computational Model of Episodic Memory Formation, Recalling, and Forgetting

  • Rahul ShrivastavaEmail author
  • Sudhakar Tripathi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)

Abstract

Our motivation in this paper is to provide a computational model of episodic memory, which is agent and domain independent like human being. Proposed model is smart enough to encode experiences in response to continuous sensory input and able to store in the form of an episode of temporally correlated events based on reward and motivation of agent. In proposed mechanism, event (personal experience) is subdivided into its constituent’s coactive activities, where each constituent activity is shared among different events with certain participation strength in different events. Model dynamically allows forgetting of unimportant activities and events based on participation strength which is recalling and reward dependent. This model extracts the key event based on reward which further incorporates in episode formation by clustering of temporal and correlated events with the key event. Recalling is also supported on coming of noisy and erroneous cue or incomplete pattern. To validate the proposed model, an empirical study was conducted, where the proposed episodic memory model is evaluated based on the recall accuracy using partial and erroneous cues and deployed in a car race environment, where agent learns the episode with reward to play by itself. The analysis shows that the proposed model significantly associated with encoding and recalling of events and episodes even with incomplete and noisy cues.

Keywords

Episodic memory Encoding Recalling Forgetting Reward learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringNational Institute of TechnologyPatnaIndia

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