Green model to adapt classical conditioning learning in the hippocampus

Mustafa Khalid, Jun Wu*, Taghreed M. Ali, Ahmed A. Moustafa, Qiuguo Zhu, Rong Xiong

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

Compared with the biological paradigms of classical conditioning, non-adaptive computational models are not capable of realistically simulating the biological behavioural functions of the hippocampal regions, because of their implausible requirement for a large number of learning trials, which can be on the order of hundreds. Additionally, these models did not attain a unified, final stable state even after hundreds of learning trials. Conversely, the output response has a different threshold for similar tasks in various models with prolonged transient response of unspecified status via the training or even testing phases. Accordingly, a green model is a combination of adaptive neuro-computational hippocampal and cortical models that is proposed by adaptively updating the whole weights in all layers for both intact networks and lesion networks using instar and outstar learning rules with adaptive resonance theory (ART). The green model sustains and expands the classical conditioning biological paradigms of the non-adaptive models. The model also overcomes the irregular output response behaviour by using the proposed feature of adaptivity. Further, the model successfully simulates the hippocampal regions without passing the final output response back to the whole network, which is considered to be biologically implausible. The results of the Green model showed a significant improvement confirmed by empirical studies of different tasks. In addition, the results indicated that the model outperforms the previously published models. All the obtained results successfully and quickly attained a stable, desired final state (with a unified concluding state of either “1” or “0”) with a significantly shorter transient duration.

Original languageEnglish
Pages (from-to)201-219
Number of pages19
JournalNeuroscience
Volume426
DOIs
Publication statusPublished - 1 Feb 2020
Externally publishedYes

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