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hip-SL

Adaptation of the emer/leabra (github.com/emer/leabra) hippocampus model for statistical learning

Description

This repository contains an updated version of the Schapiro et al. (2017) statistical learning hippocampus model, originally written in C++ emergent (www.grey.colorado.edu/emergent/index.php/Main_Page). The model has been re-written for the Golang emergent (www.github.com/emer/emergent) framework and is based on the emer/leabra/examples/hip hip.go example.

Usage instructions

  1. Install Golang.
  2. Install emergent by following the instructions at www.github.com/emer/emergent/wiki/Install.
  3. Download/Clone this repository.
  4. cd to the repo folder and run the command go build && hip-sl.

This should open the GUI view for the model. Please refer to emergent documentation for information on the GUI view.

Parameter differences between the hip.go example and hip-SL.go

All parameter changes are listed in the hip.go_vs_hip-SL.go_param_changes.xlsx file.

Architecture differences between the C++ emergent version and hip-SL.go

  1. KWTA inhition has been replaced by FFFB inhibition. For more information see: www.grey.colorado.edu/CompCogNeuro/index.php/CCNBook/Networks
  2. Learning rates for MSP and TSP have been changed for better performance:
Learning Rate C++ model Golang model
MSP 0.02 0.05
TSP 0.2 0.4
  1. Golang emergent divides trials into four quarters of 25 cycles each, with each quarter serving as a different learning phase. The ActMid, ActM and ActP learning variables from C++ emergent are therefore recorded at different timepoints in a Golang emergent training trial vs a C++ emergent training trial. Cycle at which each variable is recorded for the two models:
ActMid ActM ActP
C++ model 40 80 100
Golang model 25 75 100

Please see https://github.com/schapirolab/hip-sl/wiki/Results for full results.

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Adaptation of the emer/leabra (github.com/emer/leabra) hippocampus model for statistical learning

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