deep

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README

DeepLeabra

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Package deep provides the DeepLeabra variant of Leabra, which performs predictive learning by attempting to predict the activation states over the Pulvinar nucleus of the thalamus (in posterior sensory cortex), which are driven phasically every 100 msec by deep layer 5 intrinsic bursting (5IB) neurons that have strong focal (essentially 1-to-1) connections onto the Pulvinar Thalamic Relay Cell (TRC) neurons.

This package allows you to specify layer types as Deep or TRC (e.g., Pulvinar) which in turn drives specific forms of computation associated with each of those layer types. Standard leabra layer types are all effectively Super.

Wiring diagram:

  Super Layer --BurstTRC--> Pulv
   |       ^                 ^
BurstCtxt  |        - Back -/
   |    [DeepAttn] /
   v      |       /
 Deep Layer -----   (typically only for higher->lower)

DeepLeabra captures both the predictive learning and attentional modulation functions of the deep layer and thalamocortical circuitry.

  • Super layer neurons reflect the superficial layers of the neocortex, but they also are the basis for directly computing the Burst activation signal that reflects the deep layer 5 IB bursting activation, via thresholding of the superficial layer activations (Bursting is thought to have a higher threshold).

  • The alpha-cycle quarter(s) when Burst is updated and broadcast is set in BurstParams.BurstQtr (defaults to Q4, can also be e.g., Q2 and Q4 for beta frequency updating, used in pbwm). During this quarter(s), the Burst from Super layers is continuously sent via BurstTRC projections to TRC layers (using efficient delta-based computation) to drive plus-phase outcome states in those layers. At the end of the burst quarter(s), BurstCtxt projections convey the Burst signal to Deep layer neurons, where it is integrated into the Ctxt value representing the temporally-delayed context information. Note: Deep layers also compute a Burst value themselves, which can be sent via self projections to relfect the extensive deep-to-deep lateral connectivity that provides more extensive temporal context information.

  • Deep layer neurons reflect the layer 6 regular spiking CT corticothalamic neurons that project into the thalamus, and back up to all the other lamina within a microcolumn, where they drive a multiplicative attentional modulation signal. These neurons receive the Burst activation via a BurstCtxt projection type, typically once every 100 msec, and integrate that in the Ctxt value, which is added to other excitatory conductance inputs to drive the overall activation (Act) of these neurons. Due to the bursting nature of the Burst inputs, this causes these Deep layer neurons to reflect what the superficial layers encoded on the previous timestep -- thus they represent a temporally-delayed context state.

  • Deep layer neurons project to the TRC (Pulvinar) neurons via standard Act-driven projections that integrate into standard Ge excitatory input in TRC neurons, to drive the prediction aspect of predictive learning. They also can project back to the Super layer neurons via a DeepAttn projection to drive attentional modulation of activity there (in AttnGe, DeepAttn, and DeepLrn Neuron vars).

  • TRC layer neurons receive a BurstTRC projection from the Super layer (typically a prjn.OneToOne projection), which drives the plus-phase outcome activation state of these Pulvinar layers (Super actually computes the 5IB Burst activation). These layers also receive regular connections from Deep layers, which drive the prediction of this plus-phase outcome state, based on the temporally-delayed deep layer context information.

  • The attentional effects are implemented via DeepAttn projections from Deep to Super layers, which are typically fixed, non-learning, one-to-one projections, that drive the AttnGe excitatory condutance in Super layers. AttnGe then drives the computation of DeepAttn and DeepLrn values that modulate (i.e., multiply) the activation (DeepAttn) or learning rate (DeepLrn) of these superficial neurons.

All of the relevant parameters are in the params.go file, in the Deep*Params classes, which are then fields in the deep.Layer.

  • BurstParams (layer field: DeepBurst) has the BurstQtr when Burst is updated, and the thresholding parameters.

  • CtxtParams (layer field: DeepCtxt) has parameters for integrating DeepCtxt input

  • TRCParams (layer field: DeepTRC) has parameters for how to compute TRC plus phase activation states based on the TRCBurstGe excitatory input from the BurstTRC projections.

  • AttnParams (layer field: DeepAttn) has the parameters for computing DeepAttn and DeepLrn from AttnGe

See pbwm for info about Prefrontal-cortex Basal-ganglia Working Memory (PBWM) model that builds on this deep framework to support gated working memory.

Documentation

Overview

Package deep provides the DeepLeabra variant of Leabra, which performs predictive learning by attempting to predict the activation states over the Pulvinar nucleus of the thalamus (in posterior sensory cortex), which are driven phasically every 100 msec by deep layer 5 intrinsic bursting (5IB) neurons that have strong focal (essentially 1-to-1) connections onto the Pulvinar Thalamic Relay Cell (TRC) neurons.

This package allows you to specify layer types as Deep or TRC (e.g., Pulvinar) which in turn drives specific forms of computation associated with each of those layer types. Standard leabra layer types are all effectively Super.

Wiring diagram: 

Super Layer --BurstTRC--> Pulv
 |       ^                 ^

BurstCtxt | - Back -/ 

  |    [DeepAttn] /
  v      |       /
Deep Layer -----   (typically only for higher->lower)

DeepLeabra captures both the predictive learning and attentional modulation functions of the deep layer and thalamocortical circuitry.

* Super layer neurons reflect the superficial layers of the neocortex, but they also are the basis for directly computing the Burst activation signal that reflects the deep layer 5 IB bursting activation, via thresholding of the superficial layer activations (Bursting is thought to have a higher threshold).

* The alpha-cycle quarter(s) when Burst is updated and broadcast is set in BurstParams.BurstQtr (defaults to Q4, can also be e.g., Q2 and Q4 for beta frequency updating). During this quarter(s), the Burst from Super layers is continuously sent via BurstTRC projections to TRC layers (using efficient delta-based computation) to drive plus-phase outcome states in those layers. At the end of the burst quarter(s), BurstCtxt projections convey the Burst signal to Deep layer neurons, where it is integrated into the Ctxt value representing the temporally-delayed context information. Note: Deep layers also compute a Burst value themselves, which can be sent via self projections to relfect the extensive deep-to-deep lateral connectivity that provides more extensive temporal context information.

* Deep layer neurons reflect the layer 6 regular spiking CT corticothalamic neurons that project into the thalamus, and back up to all the other lamina within a microcolumn, where they drive a multiplicative attentional modulation signal. These neurons receive the Burst activation via a BurstCtxt projection type, typically once every 100 msec, and integrate that in the Ctxt value, which is added to other excitatory conductance inputs to drive the overall activation (Act) of these neurons. Due to the bursting nature of the Burst inputs, this causes these Deep layer neurons to reflect what the superficial layers encoded on the *previous* timestep -- thus they represent a temporally-delayed context state.

* Deep layer neurons project to the TRC (Pulvinar) neurons via standard Act-driven projections that integrate into standard Ge excitatory input in TRC neurons, to drive the prediction aspect of predictive learning. They also can project back to the Super layer neurons via a DeepAttn projection to drive attentional modulation of activity there (in `AttnGe`, `DeepAttn`, and `DeepLrn` Neuron vars).

* TRC layer neurons receive a BurstTRC projection from the Super layer (typically a `prjn.OneToOne` projection), which drives the plus-phase "outcome" activation state of these Pulvinar layers (Super actually computes the 5IB Burst activation). These layers also receive regular connections from Deep layers, which drive the prediction of this plus-phase outcome state, based on the temporally-delayed deep layer context information.

* The attentional effects are implemented via DeepAttn projections from Deep to Super layers, which are typically fixed, non-learning, one-to-one projections, that drive the AttnGe excitatory condutance in Super layers. AttnGe then drives the computation of DeepAttn and DeepLrn values that modulate (i.e., multiply) the activation (DeepAttn) or learning rate (DeepLrn) of these superficial neurons.

All of the relevant parameters are in the params.go file, in the Deep*Params classes, which are then fields in the deep.Layer.

* BurstParams (layer DeepBurst) has the BurstQtr when Burst is updated, and the thresholding parameters.

* CtxtParams (layer DeepCtxt) has parameters for integrating DeepCtxt input

* TRCParams (layer DeepTRC) has parameters for how to compute TRC plus phase activation states based on the TRCBurstGe excitatory input from the BurstTRC projections.

* AttnParams (layer DeepAttn) has the parameters for computing DeepAttn and DeepLrn from AttnGe

Index

Constants

View Source 
const (
	// Deep are deep-layer neurons, reflecting activation of layer 6 regular spiking
	// CT corticothalamic neurons, which drive both attention in Super (via DeepAttn
	// projections) and  predictions in TRC (Pulvinar) via standard projections.
	Deep emer.LayerType = emer.LayerTypeN + iota

	// TRC are thalamic relay cell neurons, typically in the Pulvinar, which alternately reflect
	// predictions driven by Deep layer projections, and actual outcomes driven by BurstTRC
	// projections from corresponding Super layer neurons that provide strong driving inputs to
	// TRC neurons.
	TRC
)
View Source 
const (
	AddPulv    bool = true
	NoPulv          = false
	AttnPrjn        = true
	NoAttnPrjn      = false
)

bool args for greater clarity

View Source 
const (
	// BurstCtxt are projections from Superficial layers to Deep layers that
	// send Burst activations drive updating of DeepCtxt excitatory conductance,
	// at end of a DeepBurst quarter.  These projections also use a special learning
	// rule that takes into account the temporal delays in the activation states.
	BurstCtxt emer.PrjnType = emer.PrjnTypeN + iota

	// BurstTRC are projections from Superficial layers to TRC (thalamic relay cell)
	// neurons (e.g., in the Pulvinar) that send Burst activation continuously
	// during the DeepBurst quarter(s), driving the TRCBurstGe value, which then drives
	// the 	plus-phase activation state of the TRC representing the "outcome" against
	// which prior predictions are (implicitly) compared via the temporal difference
	// in TRC activation state.
	BurstTRC

	// DeepAttn are projections from Deep layers (representing layer 6 regular-spiking
	// CT corticothalamic neurons) up to corresponding Superficial layer neurons, that drive
	// the attentional modulation of activations there (i.e., DeepAttn and DeepLrn values).
	// This is sent continuously all the time from deep layers using the standard delta-based
	// Ge computation, and aggregated into the AttnGe variable on Super neurons.
	DeepAttn
)

The DeepLeabra prjn types

Variables

View Source 
var (
	NeuronVars    = []string{"ActNoAttn", "Burst", "BurstPrv", "CtxtGe", "TRCBurstGe", "BurstSent", "AttnGe", "DeepAttn", "DeepLrn"}
	NeuronVarsMap map[string]int
	NeuronVarsAll []string
)
View Source 
var KiT_Layer = kit.Types.AddType(&Layer{}, LayerProps)
View Source 
var KiT_LayerType = kit.Enums.AddEnumExt(emer.KiT_LayerType, LayerTypeN, kit.NotBitFlag, nil)
View Source 
var KiT_Network = kit.Types.AddType(&Network{}, NetworkProps)
View Source 
var KiT_Prjn = kit.Types.AddType(&Prjn{}, PrjnProps)
View Source 
var KiT_PrjnType = kit.Enums.AddEnumExt(emer.KiT_PrjnType, PrjnTypeN, kit.NotBitFlag, nil)
View Source 
var LayerProps = ki.Props{
	"EnumType:Typ": KiT_LayerType,
	"ToolBar": ki.PropSlice{
		{"Defaults", ki.Props{
			"icon": "reset",
			"desc": "return all parameters to their intial default values",
		}},
		{"InitWts", ki.Props{
			"icon": "update",
			"desc": "initialize the layer's weight values according to prjn parameters, for all *sending* projections out of this layer",
		}},
		{"InitActs", ki.Props{
			"icon": "update",
			"desc": "initialize the layer's activation values",
		}},
		{"sep-act", ki.BlankProp{}},
		{"LesionNeurons", ki.Props{
			"icon": "close",
			"desc": "Lesion (set the Off flag) for given proportion of neurons in the layer (number must be 0 -- 1, NOT percent!)",
			"Args": ki.PropSlice{
				{"Proportion", ki.Props{
					"desc": "proportion (0 -- 1) of neurons to lesion",
				}},
			},
		}},
		{"UnLesionNeurons", ki.Props{
			"icon": "reset",
			"desc": "Un-Lesion (reset the Off flag) for all neurons in the layer",
		}},
	},
}
View Source 
var NetworkProps = leabra.NetworkProps
View Source 
var PrjnProps = ki.Props{
	"EnumType:Typ": KiT_PrjnType,
}

Functions

func NeuronVarByName 

func NeuronVarByName(varNm string) (int, error)

NeuronVarByName returns the index of the variable in the Neuron, or error

Types

type AttnParams added in v0.5.5 

type AttnParams struct {
	On  bool    `desc:"Enable the computation of DeepAttn, DeepLrn from AttnGe (otherwise, DeepAttn and DeepLrn = 1)"`
	Min float32 `` /* 239-byte string literal not displayed */
	Thr float32 `` /* 256-byte string literal not displayed */

	Range float32 `` /* 128-byte string literal not displayed */
}

AttnParams are parameters determining how the DeepAttn and DeepLrn attentional modulation is computed from the AttnGe inputs received via DeepAttn projections

func (*AttnParams) DeepAttnFmG added in v0.5.5 

func (db *AttnParams) DeepAttnFmG(lrn float32) float32

DeepAttnFmG returns the DeepAttn value computed from DeepLrn value

func (*AttnParams) DeepLrnFmG added in v0.5.5 

func (db *AttnParams) DeepLrnFmG(attnG, attnMax float32) float32

DeepLrnFmG returns the DeepLrn value computed from AttnGe and MAX(AttnGe) across layer. As simply the max-normalized value.

func (*AttnParams) Defaults added in v0.5.5 

func (db *AttnParams) Defaults()

func (*AttnParams) Update added in v0.5.5 

func (db *AttnParams) Update()

type BurstParams added in v0.5.5 

type BurstParams struct {
	On          bool            `` /* 149-byte string literal not displayed */
	BurstQtr    leabra.Quarters `` /* 222-byte string literal not displayed */
	FmActNoAttn bool            `` /* 281-byte string literal not displayed */
	ThrRel      float32         `` /* 365-byte string literal not displayed */
	ThrAbs      float32         `` /* 258-byte string literal not displayed */
}

BurstParams are parameters determining how the DeepBurst activation is computed from the superficial layer activation values.

func (*BurstParams) Defaults added in v0.5.5 

func (db *BurstParams) Defaults()

func (*BurstParams) IsBurstQtr added in v0.5.5 

func (db *BurstParams) IsBurstQtr(qtr int) bool

IsBurstQtr returns true if the given quarter (0-3) is set as a Bursting quarter.

func (*BurstParams) NextIsBurstQtr added in v0.5.5 

func (db *BurstParams) NextIsBurstQtr(qtr int) bool

NextIsBurstQtr returns true if the quarter after given quarter (0-3) is set as a Bursting quarter according to BurstQtr settings. wraps around -- if qtr=3 and qtr=0 is a burst qtr, then it is true

func (*BurstParams) PrevIsBurstQtr added in v0.5.5 

func (db *BurstParams) PrevIsBurstQtr(qtr int) bool

PrevIsBurstQtr returns true if the quarter before given quarter (0-3) is set as a Bursting quarter according to BurstQtr settings. wraps around -- if qtr=0 and qtr=3 is a burst qtr, then it is true

func (*BurstParams) SetBurstQtr added in v0.5.5 

func (db *BurstParams) SetBurstQtr(qtr leabra.Quarters)

SetBurstQtr sets given burst quarter (adds to any existing) -- Q4 by default

func (*BurstParams) Update added in v0.5.5 

func (db *BurstParams) Update()

type DeepLayer 

type DeepLayer interface {
	leabra.LeabraLayer

	// AsDeep returns this layer as a deep.Layer -- provides direct access to variables
	AsDeep() *Layer

	// AttnGeInc integrates new AttnGe from increments sent during last SendGDelta.
	AttnGeInc(ltime *leabra.Time)

	// AvgMaxAttnGe computes the average and max AttnGe stats
	AvgMaxAttnGe(ltime *leabra.Time)

	// DeepAttnFmG computes DeepAttn and DeepLrn from AttnGe input,
	// and then applies the DeepAttn modulation to the Act activation value.
	DeepAttnFmG(ltime *leabra.Time)

	// AvgMaxActNoAttn computes the average and max ActNoAttn stats
	AvgMaxActNoAttn(ltime *leabra.Time)

	// BurstFmAct updates Burst layer 5 IB bursting value from current Act (superficial activation)
	// Subject to thresholding.
	BurstFmAct(ltime *leabra.Time)

	// SendTRCBurstGeDelta sends change in Burst activation since last sent, over BurstTRC
	// projections.
	SendTRCBurstGeDelta(ltime *leabra.Time)

	// TRCBurstGeFmInc computes the TRCBurstGe input from sent values
	TRCBurstGeFmInc(ltime *leabra.Time)

	// AvgMaxTRCBurstGe computes the average and max TRCBurstGe stats
	AvgMaxTRCBurstGe(ltime *leabra.Time)

	// SendCtxtGe sends full Burst activation over BurstCtxt projections to integrate
	// CtxtGe excitatory conductance on deep layers.
	// This must be called at the end of the Burst quarter for this layer.
	SendCtxtGe(ltime *leabra.Time)

	// CtxtFmGe integrates new CtxtGe excitatory conductance from projections, and computes
	// overall Ctxt value.  This must be called at the end of the Burst quarter for this layer,
	// after SendCtxtGe.
	CtxtFmGe(ltime *leabra.Time)

	// BurstPrv saves Burst as BurstPrv
	BurstPrv(ltime *leabra.Time)
}

DeepLayer defines the essential algorithmic API for DeepLeabra at the layer level.

type DeepPrjn 

type DeepPrjn interface {
	leabra.LeabraPrjn

	// SendCtxtGe sends the full Burst activation from sending neuron index si,
	// to integrate CtxtGe excitatory conductance on receivers
	SendCtxtGe(si int, dburst float32)

	// SendTRCBurstGeDelta sends the delta-Burst activation from sending neuron index si,
	// to integrate TRCBurstGe excitatory conductance on receivers
	SendTRCBurstGeDelta(si int, delta float32)

	// SendAttnGeDelta sends the delta-activation from sending neuron index si,
	// to integrate into AttnGeInc excitatory conductance on receivers
	SendAttnGeDelta(si int, delta float32)

	// RecvCtxtGeInc increments the receiver's CtxtGe from that of all the projections
	RecvCtxtGeInc()

	// RecvTRCBurstGeInc increments the receiver's TRCBurstGe from that of all the projections
	RecvTRCBurstGeInc()

	// RecvAttnGeInc increments the receiver's AttnGe from that of all the projections
	RecvAttnGeInc()

	// DWtDeepCtxt computes the weight change (learning) -- for DeepCtxt projections
	DWtDeepCtxt()
}

DeepPrjn defines the essential algorithmic API for DeepLeabra at the projection level.

type Layer 

type Layer struct {
	leabra.Layer             // access as .Layer
	DeepBurst    BurstParams `` /* 142-byte string literal not displayed */
	DeepTRC      TRCParams   `` /* 145-byte string literal not displayed */
	DeepAttn     AttnParams  `` /* 180-byte string literal not displayed */
	DeepNeurs    []Neuron    `` /* 151-byte string literal not displayed */
	DeepPools    []Pool      `` /* 247-byte string literal not displayed */
}

deep.Layer is the DeepLeabra layer, based on basic rate-coded leabra.Layer

func (*Layer) ActFmG 

func (ly *Layer) ActFmG(ltime *leabra.Time)

ActFmG computes rate-code activation from Ge, Gi, Gl conductances and updates learning running-average activations from that Act

func (*Layer) AsDeep 

func (ly *Layer) AsDeep() *Layer

AsDeep returns this layer as a deep.Layer

func (*Layer) AttnGeInc added in v0.5.5 

func (ly *Layer) AttnGeInc(ltime *leabra.Time)

AttnGeInc integrates new AttnGe from increments sent during last SendGDelta. Very low overhead if no DeepAttn prjns.

func (*Layer) AvgMaxAct 

func (ly *Layer) AvgMaxAct(ltime *leabra.Time)

AvgMaxAct computes the average and max Act stats, used in inhibition Deep version also computes AvgMaxActNoAttn

func (*Layer) AvgMaxActNoAttn 

func (ly *Layer) AvgMaxActNoAttn(ltime *leabra.Time)

AvgMaxActNoAttn computes the average and max ActNoAttn stats

func (*Layer) AvgMaxAttnGe 

func (ly *Layer) AvgMaxAttnGe(ltime *leabra.Time)

AvgMaxAttnGe computes the average and max AttnGe stats

func (*Layer) AvgMaxGe 

func (ly *Layer) AvgMaxGe(ltime *leabra.Time)

AvgMaxGe computes the average and max Ge stats, used in inhibition Deep version also computes AttnGe stats

func (*Layer) AvgMaxTRCBurstGe 

func (ly *Layer) AvgMaxTRCBurstGe(ltime *leabra.Time)

AvgMaxTRCBurstGe computes the average and max TRCBurstGe stats

func (*Layer) Build 

func (ly *Layer) Build() error

Build constructs the layer state, including calling Build on the projections.

func (*Layer) BurstFmAct added in v0.5.5 

func (ly *Layer) BurstFmAct(ltime *leabra.Time)

BurstFmAct updates Burst layer 5 IB bursting value from current Act (superficial activation) Subject to thresholding.

func (*Layer) BurstPrv added in v0.5.5 

func (ly *Layer) BurstPrv(ltime *leabra.Time)

BurstPrv saves Burst as BurstPrv

func (*Layer) Class added in v0.5.5 

func (ly *Layer) Class() string

func (*Layer) CtxtFmGe added in v0.5.5 

func (ly *Layer) CtxtFmGe(ltime *leabra.Time)

CtxtFmGe integrates new CtxtGe excitatory conductance from projections, and computes overall Ctxt value, only on Deep layers. This must be called at the end of the DeepBurst quarter for this layer, after SendCtxtGe.

func (*Layer) DecayState 

func (ly *Layer) DecayState(decay float32)

func (*Layer) DeepAttnFmG 

func (ly *Layer) DeepAttnFmG(ltime *leabra.Time)

DeepAttnFmG computes DeepAttn and DeepLrn from AttnGe input, and then applies the DeepAttn modulation to the Act activation value.

func (*Layer) Defaults 

func (ly *Layer) Defaults()

func (*Layer) GFmInc 

func (ly *Layer) GFmInc(ltime *leabra.Time)

GFmInc integrates new synaptic conductances from increments sent during last SendGDelta.

func (*Layer) GScaleFmAvgAct 

func (ly *Layer) GScaleFmAvgAct()

GScaleFmAvgAct computes the scaling factor for synaptic input conductances G, based on sending layer average activation. This attempts to automatically adjust for overall differences in raw activity coming into the units to achieve a general target of around .5 to 1 for the integrated G values. DeepLeabra version separately normalizes the Deep projection types.

func (*Layer) InitActs 

func (ly *Layer) InitActs()

func (*Layer) InitGInc added in v0.5.5 

func (ly *Layer) InitGInc()

InitGinc initializes the Ge excitatory and Gi inhibitory conductance accumulation states including ActSent and G*Raw values. called at start of trial always, and can be called optionally when delta-based Ge computation needs to be updated (e.g., weights might have changed strength)

func (*Layer) IsSuper added in v0.5.5 

func (ly *Layer) IsSuper() bool

IsSuper returns true if layer is not a TRC or Deep type -- all others are Super

func (*Layer) QuarterFinal 

func (ly *Layer) QuarterFinal(ltime *leabra.Time)

QuarterFinal does updating after end of a quarter

func (*Layer) SendCtxtGe added in v0.5.5 

func (ly *Layer) SendCtxtGe(ltime *leabra.Time)

SendCtxtGe sends full Burst activation over BurstCtxt projections to integrate CtxtGe excitatory conductance on deep layers. This must be called at the end of the DeepBurst quarter for this layer.

func (*Layer) SendGDelta 

func (ly *Layer) SendGDelta(ltime *leabra.Time)

SendGDelta sends change in activation since last sent, if above thresholds. Deep version sends either to standard Ge or AttnGe for DeepAttn projections.

func (*Layer) SendTRCBurstGeDelta 

func (ly *Layer) SendTRCBurstGeDelta(ltime *leabra.Time)

SendTRCBurstGeDelta sends change in Burst activation since last sent, over BurstTRC projections.

func (*Layer) TRCBurstGeFmInc 

func (ly *Layer) TRCBurstGeFmInc(ltime *leabra.Time)

TRCBurstGeFmInc computes the TRCBurstGe input from sent values

func (*Layer) UnitVal1D added in v0.5.5 

func (ly *Layer) UnitVal1D(varIdx int, idx int) float32

UnitVal1D returns value of given variable index on given unit, using 1-dimensional index. returns NaN on invalid index. This is the core unit var access method used by other methods, so it is the only one that needs to be updated for derived layer types.

func (*Layer) UnitVarIdx added in v0.5.5 

func (ly *Layer) UnitVarIdx(varNm string) (int, error)

UnitVarIdx returns the index of given variable within the Neuron, according to UnitVarNames() list (using a map to lookup index), or -1 and error message if not found.

func (*Layer) UnitVarNames 

func (ly *Layer) UnitVarNames() []string

UnitVarNames returns a list of variable names available on the units in this layer

func (*Layer) UpdateParams 

func (ly *Layer) UpdateParams()

UpdateParams updates all params given any changes that might have been made to individual values including those in the receiving projections of this layer

type LayerType added in v0.5.5 

type LayerType emer.LayerType

LayerType has the DeepLeabra extensions to the emer.LayerType types, for gui 

const (
	Deep_ LayerType = LayerType(emer.LayerTypeN) + iota
	TRC_
	LayerTypeN
)

gui versions

func StringToLayerType added in v0.5.5 

func StringToLayerType(s string) (LayerType, error)

func (LayerType) String added in v0.5.5 

func (i LayerType) String() string

type Network 

type Network struct {
	leabra.Network
}

deep.Network has parameters for running a DeepLeabra network

func (*Network) AddInputPulv2D added in v0.5.5 

func (nt *Network) AddInputPulv2D(name string, shapeY, shapeX int) (input, pulv emer.Layer)

AddInputPulv2D adds an input and corresponding Pulvinar (P suffix) layer with BurstTRC one-to-one projection from Input to Pulvinar. Pulvinar is placed Behind Input.

func (*Network) AddInputPulv4D added in v0.5.5 

func (nt *Network) AddInputPulv4D(name string, nPoolsY, nPoolsX, nNeurY, nNeurX int) (input, pulv emer.Layer)

AddInputPulv4D adds an input and corresponding Pulvinar (P suffix) layer with BurstTRC one-to-one projection from Input to Pulvinar. Pulvinar is placed Behind Input.

func (*Network) AddSuperDeep2D added in v0.5.5 

func (nt *Network) AddSuperDeep2D(name string, shapeY, shapeX int, pulvLay, attn bool) (super, deep, pulv emer.Layer)

AddSuperDeep2D adds a superficial (hidden) and corresponding Deep (D suffix) layer with BurstCtxt Full projection from Hidden to Deep. Optionally creates a Pulvinar for Hidden with One-to-One BurstTRC to Pulvinar, and optionally a DeepAttn projection back from Deep to Super (OneToOne). Deep is placed Behind Super, and Pulvinar behind Deep if created.

func (*Network) AddSuperDeep4D added in v0.5.5 

func (nt *Network) AddSuperDeep4D(name string, nPoolsY, nPoolsX, nNeurY, nNeurX int, pulvLay, attn bool) (super, deep, pulv emer.Layer)

AddSuperDeep4D adds a superficial (hidden) and corresponding Deep (D suffix) layer with BurstCtxt Full projection from Hidden to Deep. Optionally creates a Pulvinar for Hidden with One-to-One BurstTRC to Pulvinar, and optionally a DeepAttn projection back from Deep to Super (OneToOne) Deep is placed Behind Super, and Pulvinar behind Deep if created.

func (*Network) CycleImpl added in v0.5.5 

func (nt *Network) CycleImpl(ltime *leabra.Time)

CycleImpl runs one cycle of activation updating Deep version adds call to update DeepBurst at end

func (*Network) DeepBurst 

func (nt *Network) DeepBurst(ltime *leabra.Time)

DeepBurst is called at end of Cycle, computes Burst and sends it to other layers

func (*Network) DeepCtxt 

func (nt *Network) DeepCtxt(ltime *leabra.Time)

DeepCtxt sends DeepBurst to Deep layers and integrates DeepCtxt on Deep layers

func (*Network) Defaults 

func (nt *Network) Defaults()

Defaults sets all the default parameters for all layers and projections

func (*Network) NewLayer 

func (nt *Network) NewLayer() emer.Layer

NewLayer returns new layer of proper type

func (*Network) NewPrjn 

func (nt *Network) NewPrjn() emer.Prjn

NewPrjn returns new prjn of proper type

func (*Network) QuarterFinal 

func (nt *Network) QuarterFinal(ltime *leabra.Time)

QuarterFinal does updating after end of a quarter

func (*Network) UnitVarNames added in v0.5.5 

func (nt *Network) UnitVarNames() []string

UnitVarNames returns a list of variable names available on the units in this layer

func (*Network) UpdateParams 

func (nt *Network) UpdateParams()

UpdateParams updates all the derived parameters if any have changed, for all layers and projections

type Neuron 

type Neuron struct {
	ActNoAttn  float32 `` /* 316-byte string literal not displayed */
	Burst      float32 `` /* 686-byte string literal not displayed */
	BurstPrv   float32 `desc:"Burst from the previous alpha trial -- this is typically used for learning in the BurstCtxt projection."`
	CtxtGe     float32 `` /* 351-byte string literal not displayed */
	TRCBurstGe float32 `` /* 198-byte string literal not displayed */
	BurstSent  float32 `desc:"Last Burst activation value sent, for computing TRCBurstGe using efficient delta mechanism."`
	AttnGe     float32 `` /* 314-byte string literal not displayed */
	DeepAttn   float32 `` /* 493-byte string literal not displayed */
	DeepLrn    float32 `` /* 246-byte string literal not displayed */
}

deep.Neuron holds the extra neuron (unit) level variables for DeepLeabra computation. DeepLeabra includes both attentional and predictive learning functions of the deep layers and thalamocortical circuitry. These are maintained in a separate parallel slice from the leabra.Neuron variables.

func (*Neuron) VarByIndex 

func (nrn *Neuron) VarByIndex(idx int) float32

VarByIndex returns variable using index (0 = first variable in NeuronVars list)

func (*Neuron) VarByName 

func (nrn *Neuron) VarByName(varNm string) (float32, error)

VarByName returns variable by name, or error

func (*Neuron) VarNames 

func (nrn *Neuron) VarNames() []string

type Pool 

type Pool struct {
	ActNoAttn  minmax.AvgMax32
	TRCBurstGe minmax.AvgMax32
	AttnGe     minmax.AvgMax32
}

deep.Pool contains extra statistics used in DeepLeabra

type Prjn 

type Prjn struct {
	leabra.Prjn             // access as .Prjn
	CtxtGeInc     []float32 `desc:"local per-recv unit accumulator for Ctxt excitatory conductance from sending units -- not a delta -- the full value"`
	TRCBurstGeInc []float32 `` /* 133-byte string literal not displayed */
	AttnGeInc     []float32 `` /* 129-byte string literal not displayed */
}

deep.Prjn is the DeepLeabra projection, based on basic rate-coded leabra.Prjn

func (*Prjn) Build 

func (pj *Prjn) Build() error

func (*Prjn) DWt 

func (pj *Prjn) DWt()

DWt computes the weight change (learning) -- on sending projections Deep version supports DeepCtxt temporal learning option

func (*Prjn) DWtDeepCtxt 

func (pj *Prjn) DWtDeepCtxt()

DWtDeepCtxt computes the weight change (learning) -- for DeepCtxt projections

func (*Prjn) Defaults 

func (pj *Prjn) Defaults()

func (*Prjn) InitGInc 

func (pj *Prjn) InitGInc()

func (*Prjn) PrjnTypeName added in v0.5.5 

func (pj *Prjn) PrjnTypeName() string

func (*Prjn) RecvAttnGeInc 

func (pj *Prjn) RecvAttnGeInc()

RecvAttnGeInc increments the receiver's AttnGe from that of all the projections

func (*Prjn) RecvCtxtGeInc added in v0.5.5 

func (pj *Prjn) RecvCtxtGeInc()

RecvCtxtGeInc increments the receiver's CtxtGe from that of all the projections

func (*Prjn) RecvTRCBurstGeInc 

func (pj *Prjn) RecvTRCBurstGeInc()

RecvTRCBurstGeInc increments the receiver's TRCBurstGe from that of all the projections

func (*Prjn) SendAttnGeDelta 

func (pj *Prjn) SendAttnGeDelta(si int, delta float32)

SendAttnGeDelta sends the delta-activation from sending neuron index si, to integrate into AttnGeInc excitatory conductance on receivers

func (*Prjn) SendCtxtGe added in v0.5.5 

func (pj *Prjn) SendCtxtGe(si int, dburst float32)

SendCtxtGe sends the full Burst activation from sending neuron index si, to integrate CtxtGe excitatory conductance on receivers

func (*Prjn) SendTRCBurstGeDelta 

func (pj *Prjn) SendTRCBurstGeDelta(si int, delta float32)

SendTRCBurstGeDelta sends the delta-Burst activation from sending neuron index si, to integrate TRCBurstGe excitatory conductance on receivers

func (*Prjn) UpdateParams 

func (pj *Prjn) UpdateParams()

type PrjnType added in v0.5.5 

type PrjnType emer.PrjnType

PrjnType has the DeepLeabra extensions to the emer.PrjnType types, for gui 

const (
	BurstCtxt_ PrjnType = PrjnType(emer.PrjnTypeN) + iota
	BurstTRC_
	DeepAttn_
	PrjnTypeN
)

gui versions

func StringToPrjnType added in v0.5.5 

func StringToPrjnType(s string) (PrjnType, error)

func (PrjnType) String added in v0.5.5 

func (i PrjnType) String() string

type TRCParams added in v0.5.5 

type TRCParams struct {
	MaxInhib  float32 `` /* 725-byte string literal not displayed */
	InhibPool bool    `` /* 184-byte string literal not displayed */
	Binarize  bool    `` /* 346-byte string literal not displayed */
	BinThr    float32 `` /* 164-byte string literal not displayed */
	BinOn     float32 `def:"0.3" viewif:"Binarize" desc:"Effective value for units above threshold -- lower value around 0.3 or so seems best."`
	BinOff    float32 `def:"0" viewif:"Binarize" desc:"Effective value for units below threshold -- typically 0."`
}

TRCParams provides parameters for how the plus-phase (outcome) state of thalamic relay cell (e.g., Pulvinar) neurons is computed from the BurstTRC projections that drive TRCBurstGe excitatory conductance.

func (*TRCParams) BurstGe added in v0.5.5 

func (tp *TRCParams) BurstGe(burstGe float32) float32

BurstGe returns effective excitatory conductance to use for burst-quarter time in TRC layer.

func (*TRCParams) Defaults added in v0.5.5 

func (tp *TRCParams) Defaults()

func (*TRCParams) Update added in v0.5.5 

func (tp *TRCParams) Update()

Source Files

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