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Adaptation of the emer/leabra ( hippocampus model for statistical learning
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Adaptation of the emer/leabra ( hippocampus model for statistical learning


This repository contains an updated version of the Schapiro et al. (2017) statistical learning hippocampus model, originally written in C++ emergent ( The model has been re-written for the Golang 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
  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 KWTA inhition has been replaced by FFFB inhibition.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:
  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

Differences in results between the C++ emergent version and hip-SL.go

The only observed qualitative change in results is the lack of a checkerboard pattern (see Schapiro et al. 2017) in the 'Initial Response' heatmap for CA1.

Please see for full results.

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