Categorygithub.com/PrincetonCompMemLab/neurodiff_leabrahip
package
0.0.0-20240905221516-6df987c31e31
Repository: https://github.com/princetoncompmemlab/neurodiff_leabra.git
Documentation: pkg.go.dev
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# README

Hippocampus

GoDoc

Package hip provides special hippocampus algorithms for implementing the Theta-phase hippocampus model from Ketz, Morkonda, & O'Reilly (2013).

timing of ThetaPhase dynamics -- based on quarter structure:

  • Q1: ECin -> CA1 -> ECout (CA3 -> CA1 off) : ActQ1 = minus phase for auto-encoder
  • Q2,3: CA3 -> CA1 -> ECout (ECin -> CA1 off) : ActM = minus phase for recall
  • Q4: ECin -> CA1, ECin -> ECout (CA3 -> CA1 off, ECin -> CA1 on): ActP = plus phase for everything
[  q1      ][  q2  q3  ][     q4     ]
[ ------ minus ------- ][ -- plus -- ]
[   auto-  ][ recall-  ][ -- plus -- ]

  DG -> CA3 -> CA1
 /    /      /    \
[----ECin---] -> [ ECout ]

minus phase: ECout unclamped, driven by CA1
auto-   CA3 -> CA1 = 0, ECin -> CA1 = 1
recall- CA3 -> CA1 = 1, ECin -> CA1 = 0

plus phase: ECin -> ECout auto clamped
CA3 -> CA1 = 0, ECin -> CA1 = 1
(same as auto- -- training signal for CA3 -> CA1 is what EC would produce!
  • ActQ1 = auto encoder minus phase state (in both CA1 and ECout used in EcCa1Prjn as minus phase relative to ActP plus phase in CHL)
  • ActM = recall minus phase (normal minus phase dynamics for CA3 recall learning)
  • ActP = plus (serves as plus phase for both auto and recall)

learning just happens at end of trial as usual, but encoder projections use the ActQ1, ActM, ActP variables to learn on the right signals

TODO

  • try error-driven CA3 learning based on DG -> CA3 plus phase per https://arxiv.org/abs/1909.10340

  • implement a two-trial version of the code to produce a true theta rhythm integrating over two adjacent alpha trials..

# Structs

Contrastive Hebbian Learning (CHL) parameters.
hip.CHLPrjn is a Contrastive Hebbian Learning (CHL) projection, based on basic rate-coded leabra.Prjn, that implements a pure CHL learning rule, which works better in the hippocampus.
hip.EcCa1Prjn is for EC <-> CA1 projections, to perform error-driven learning of this encoder pathway according to the ThetaPhase algorithm uses Contrastive Hebbian Learning (CHL) on ActP - ActQ1 Q1: ECin -> CA1 -> ECout : ActQ1 = minus phase for auto-encoder Q2, 3: CA3 -> CA1 -> ECout : ActM = minus phase for recall Q4: ECin -> CA1, ECin -> ECout : ActP = plus phase for everything.