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README
¶
Hippocampus
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..
Documentation
¶
Overview ¶
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: implement a two-trial version of the code to produce a true theta rhythm integrating over two adjacent alpha trials..
Index ¶
- type CHLParams
- func (ch *CHLParams) DWt(hebb, err float32) float32
- func (ch *CHLParams) Defaults()
- func (ch *CHLParams) ErrDWt(sactP, sactM, ractP, ractM, linWt float32) float32
- func (ch *CHLParams) HebbDWt(sact, ract, savgCor, linWt float32) float32
- func (ch *CHLParams) MinusAct(actM, actQ1 float32) float32
- func (ch *CHLParams) Update()
- type CHLPrjn
- type EcCa1Prjn
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type CHLParams ¶
type CHLParams struct {
On bool `desc:"if true, use CHL learning instead of standard XCAL learning -- allows easy exploration of CHL vs. XCAL"`
Hebb float32 `def:"0.001" min:"0" max:"1" desc:"amount of hebbian learning (should be relatively small, can be effective at .0001)"`
Err float32 `def:"0.999" min:"0" max:"1" inactive:"+" desc:"amount of error driven learning, automatically computed to be 1-Hebb"`
MinusQ1 bool `desc:"if true, use ActQ1 as the minus phase -- otherwise ActM"`
SAvgCor float32 `` /* 161-byte string literal not displayed */
SAvgThr float32 `` /* 145-byte string literal not displayed */
}
Contrastive Hebbian Learning (CHL) parameters
func (*CHLParams) ErrDWt ¶
ErrDWt computes the error-driven DWt value from sending, recv acts in both phases, and linear Wt, which is used for soft weight bounding (always applied here, separate from hebbian which has its own soft weight bounding dynamic).
func (*CHLParams) HebbDWt ¶
HebbDWt computes the hebbian DWt value from sending, recv acts, savgCor, and linear Wt
type CHLPrjn ¶
type CHLPrjn struct {
leabra.Prjn // access as .Prjn
CHL CHLParams `` /* 129-byte string literal not displayed */
}
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.
func (*CHLPrjn) DWt ¶
func (pj *CHLPrjn) DWt()
DWt computes the weight change (learning) -- on sending projections CHL version supported if On
func (*CHLPrjn) DWtCHL ¶
func (pj *CHLPrjn) DWtCHL()
DWtCHL computes the weight change (learning) for CHL
func (*CHLPrjn) SAvgCor ¶
SAvgCor computes the sending average activation, corrected according to the SAvgCor correction factor (typically makes layer appear more sparse than it is)
func (*CHLPrjn) UpdateParams ¶
func (pj *CHLPrjn) UpdateParams()
type EcCa1Prjn ¶
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
func (*EcCa1Prjn) DWt ¶
func (pj *EcCa1Prjn) DWt()
DWt computes the weight change (learning) -- on sending projections Delta version
func (*EcCa1Prjn) UpdateParams ¶
func (pj *EcCa1Prjn) UpdateParams()
Source Files