leabra

type ActAvg struct {

	// running-average minus-phase activity -- used for adapting inhibition -- see ActAvgParams.Tau for time constant etc
	ActMAvg float32 `desc:"running-average minus-phase activity -- used for adapting inhibition -- see ActAvgParams.Tau for time constant etc"`

	// running-average plus-phase activity -- used for synaptic input scaling -- see ActAvgParams.Tau for time constant etc
	ActPAvg float32 `desc:"running-average plus-phase activity -- used for synaptic input scaling -- see ActAvgParams.Tau for time constant etc"`

	// ActPAvg * ActAvgParams.Adjust -- adjusted effective layer activity directly used in synaptic input scaling
	ActPAvgEff float32 `desc:"ActPAvg * ActAvgParams.Adjust -- adjusted effective layer activity directly used in synaptic input scaling"`
}

type ActAvgParams struct {

	// [min: 0] [typically 0.1 - 0.2] initial estimated average activity level in the layer (see also UseFirst option -- if that is off then it is used as a starting point for running average actual activity level, ActMAvg and ActPAvg) -- ActPAvg is used primarily for automatic netinput scaling, to balance out layers that have different activity levels -- thus it is important that init be relatively accurate -- good idea to update from recorded ActPAvg levels
	Init float32 `` /* 462-byte string literal not displayed */

	// [def: false] if true, then the Init value is used as a constant for ActPAvgEff (the effective value used for netinput rescaling), instead of using the actual running average activation
	Fixed bool `` /* 190-byte string literal not displayed */

	// [def: false] if true, then use the activation level computed from the external inputs to this layer (avg of targ or ext unit vars) -- this will only be applied to layers with Input or Target / Compare layer types, and falls back on the targ_init value if external inputs are not available or have a zero average -- implies fixed behavior
	UseExtAct bool `` /* 343-byte string literal not displayed */

	// [def: true] [viewif: Fixed=false] use the first actual average value to override targ_init value -- actual value is likely to be a better estimate than our guess
	UseFirst bool `` /* 166-byte string literal not displayed */

	// [def: 100] [viewif: Fixed=false] [min: 1] time constant in trials for integrating time-average values at the layer level -- used for computing Pool.ActAvg.ActsMAvg, ActsPAvg
	Tau float32 `` /* 177-byte string literal not displayed */

	// [def: 1] [viewif: Fixed=false] adjustment multiplier on the computed ActPAvg value that is used to compute ActPAvgEff, which is actually used for netinput rescaling -- if based on connectivity patterns or other factors the actual running-average value is resulting in netinputs that are too high or low, then this can be used to adjust the effective average activity value -- reducing the average activity with a factor < 1 will increase netinput scaling (stronger net inputs from layers that receive from this layer), and vice-versa for increasing (decreases net inputs)
	Adjust float32 `` /* 576-byte string literal not displayed */

	// [view: -] rate = 1 / tau
	Dt float32 `inactive:"+" view:"-" json:"-" xml:"-" desc:"rate = 1 / tau"`
}

type ActInitParams struct {

	// [def: 0,1] [min: 0] [max: 1] proportion to decay activation state toward initial values at start of every trial
	Decay float32 `def:"0,1" max:"1" min:"0" desc:"proportion to decay activation state toward initial values at start of every trial"`

	// [def: 0.4] initial membrane potential -- see e_rev.l for the resting potential (typically .3) -- often works better to have a somewhat elevated initial membrane potential relative to that
	Vm float32 `` /* 193-byte string literal not displayed */

	// [def: 0] initial activation value -- typically 0
	Act float32 `def:"0" desc:"initial activation value -- typically 0"`

	// [def: 0] baseline level of excitatory conductance (net input) -- Ge is initialized to this value, and it is added in as a constant background level of excitatory input -- captures all the other inputs not represented in the model, and intrinsic excitability, etc
	Ge float32 `` /* 268-byte string literal not displayed */
}

type ActNoiseParams struct {
	erand.RndParams

	// where and how to add processing noise
	Type ActNoiseType `desc:"where and how to add processing noise"`

	// keep the same noise value over the entire alpha cycle -- prevents noise from being washed out and produces a stable effect that can be better used for learning -- this is strongly recommended for most learning situations
	Fixed bool `` /* 227-byte string literal not displayed */
}

type ActNoiseType int

type ActParams struct {

	// [view: inline] Noisy X/X+1 rate code activation function parameters
	XX1 nxx1.Params `view:"inline" desc:"Noisy X/X+1 rate code activation function parameters"`

	// [view: inline] optimization thresholds for faster processing
	OptThresh OptThreshParams `view:"inline" desc:"optimization thresholds for faster processing"`

	// [view: inline] initial values for key network state variables -- initialized at start of trial with InitActs or DecayActs
	Init ActInitParams `` /* 127-byte string literal not displayed */

	// [view: inline] time and rate constants for temporal derivatives / updating of activation state
	Dt DtParams `view:"inline" desc:"time and rate constants for temporal derivatives / updating of activation state"`

	// [view: inline] [Defaults: 1, .1, 1, 1] maximal conductances levels for channels
	Gbar chans.Chans `view:"inline" desc:"[Defaults: 1, .1, 1, 1] maximal conductances levels for channels"`

	// [view: inline] [Defaults: 1, .3, .25, .1] reversal potentials for each channel
	Erev chans.Chans `view:"inline" desc:"[Defaults: 1, .3, .25, .1] reversal potentials for each channel"`

	// [view: inline] how external inputs drive neural activations
	Clamp ClampParams `view:"inline" desc:"how external inputs drive neural activations"`

	// [view: inline] how, where, when, and how much noise to add to activations
	Noise ActNoiseParams `view:"inline" desc:"how, where, when, and how much noise to add to activations"`

	// [view: inline] range for Vm membrane potential -- [0, 2.0] by default
	VmRange minmax.F32 `view:"inline" desc:"range for Vm membrane potential -- [0, 2.0] by default"`

	// [view: no-inline] sodium-gated potassium channel adaptation parameters -- activates an inhibitory leak-like current as a function of neural activity (firing = Na influx) at three different time-scales (M-type = fast, Slick = medium, Slack = slow)
	KNa knadapt.Params `` /* 252-byte string literal not displayed */

	// [view: -] Erev - Act.Thr for each channel -- used in computing GeThrFmG among others
	ErevSubThr chans.Chans `inactive:"+" view:"-" json:"-" xml:"-" desc:"Erev - Act.Thr for each channel -- used in computing GeThrFmG among others"`

	// [view: -] Act.Thr - Erev for each channel -- used in computing GeThrFmG among others
	ThrSubErev chans.Chans `inactive:"+" view:"-" json:"-" xml:"-" desc:"Act.Thr - Erev for each channel -- used in computing GeThrFmG among others"`
}

type AvgLParams struct {

	// [def: 0.4] [min: 0] [max: 1] initial AvgL value at start of training
	Init float32 `def:"0.4" min:"0" max:"1" desc:"initial AvgL value at start of training"`

	// [def: 1.5,2,2.5,3,4,5] [min: 0] gain multiplier on activation used in computing the running average AvgL value that is the key floating threshold in the BCM Hebbian learning rule -- when using the DELTA_FF_FB learning rule, it should generally be 2x what it was before with the old XCAL_CHL rule, i.e., default of 5 instead of 2.5 -- it is a good idea to experiment with this parameter a bit -- the default is on the high-side, so typically reducing a bit from initial default is a good direction
	Gain float32 `` /* 501-byte string literal not displayed */

	// [def: 0.2] [min: 0] miniumum AvgL value -- running average cannot go lower than this value even when it otherwise would due to inactivity -- default value is generally good and typically does not need to be changed
	Min float32 `` /* 219-byte string literal not displayed */

	// [def: 10] [min: 1] time constant for updating the running average AvgL -- AvgL moves toward gain*act with this time constant on every alpha-cycle - longer time constants can also work fine, but the default of 10 allows for quicker reaction to beneficial weight changes
	Tau float32 `` /* 273-byte string literal not displayed */

	// [def: 0.5] [min: 0] maximum AvgLLrn value, which is amount of learning driven by AvgL factor -- when AvgL is at its maximum value (i.e., gain, as act does not exceed 1), then AvgLLrn will be at this maximum value -- by default, strong amounts of this homeostatic Hebbian form of learning can be used when the receiving unit is highly active -- this will then tend to bring down the average activity of units -- the default of 0.5, in combination with the err_mod flag, works well for most models -- use around 0.0004 for a single fixed value (with err_mod flag off)
	LrnMax float32 `` /* 570-byte string literal not displayed */

	// [def: 0.0001,0.0004] [min: 0] miniumum AvgLLrn value (amount of learning driven by AvgL factor) -- if AvgL is at its minimum value, then AvgLLrn will be at this minimum value -- neurons that are not overly active may not need to increase the contrast of their weights as much -- use around 0.0004 for a single fixed value (with err_mod flag off)
	LrnMin float32 `` /* 350-byte string literal not displayed */

	// [def: true] modulate amount learning by normalized level of error within layer
	ErrMod bool `def:"true" desc:"modulate amount learning by normalized level of error within layer"`

	// [def: 0.01] [viewif: ErrMod=true] minimum modulation value for ErrMod-- ensures a minimum amount of self-organizing learning even for network / layers that have a very small level of error signal
	ModMin float32 `` /* 200-byte string literal not displayed */

	// [view: -] rate = 1 / tau
	Dt float32 `view:"-" json:"-" xml:"-" inactive:"+" desc:"rate = 1 / tau"`

	// [view: -] (LrnMax - LrnMin) / (Gain - Min)
	LrnFact float32 `view:"-" json:"-" xml:"-" inactive:"+" desc:"(LrnMax - LrnMin) / (Gain - Min)"`
}

type ClampParams struct {

	// [def: true] whether to hard clamp inputs where activation is directly set to external input value (Act = Ext) or do soft clamping where Ext is added into Ge excitatory current (Ge += Gain * Ext)
	Hard bool `` /* 200-byte string literal not displayed */

	// [viewif: Hard] range of external input activation values allowed -- Max is .95 by default due to saturating nature of rate code activation function
	Range minmax.F32 `` /* 153-byte string literal not displayed */

	// [def: 0.02:0.5] [viewif: !Hard] soft clamp gain factor (Ge += Gain * Ext)
	Gain float32 `viewif:"!Hard" def:"0.02:0.5" desc:"soft clamp gain factor (Ge += Gain * Ext)"`

	// [viewif: !Hard] compute soft clamp as the average of current and target netins, not the sum -- prevents some of the main effect problems associated with adding external inputs
	Avg bool `` /* 181-byte string literal not displayed */

	// [def: 0.2] [viewif: !Hard && Avg] gain factor for averaging the Ge -- clamp value Ext contributes with AvgGain and current Ge as (1-AvgGain)
	AvgGain float32 `` /* 145-byte string literal not displayed */
}

type CosDiffParams struct {

	// [def: 100] [min: 1] time constant in alpha-cycles (roughly how long significant change takes, 1.4 x half-life) for computing running average CosDiff value for the layer, CosDiffAvg = cosine difference between ActM and ActP -- this is an important statistic for how much phase-based difference there is between phases in this layer -- it is used in standard X_COS_DIFF modulation of l_mix in LeabraConSpec, and for modulating learning rate as a function of predictability in the DeepLeabra predictive auto-encoder learning -- running average variance also computed with this: cos_diff_var
	Tau float32 `` /* 592-byte string literal not displayed */

	// [view: -] rate constant = 1 / Tau
	Dt float32 `inactive:"+" view:"-" json:"-" xml:"-" desc:"rate constant = 1 / Tau"`

	// [view: -] complement of rate constant = 1 - Dt
	DtC float32 `inactive:"+" view:"-" json:"-" xml:"-" desc:"complement of rate constant = 1 - Dt"`
}

type CosDiffStats struct {

	// cosine (normalized dot product) activation difference between ActP and ActM on this alpha-cycle for this layer -- computed by CosDiffFmActs at end of QuarterFinal for quarter = 3
	Cos float32 `` /* 185-byte string literal not displayed */

	// running average of cosine (normalized dot product) difference between ActP and ActM -- computed with CosDiff.Tau time constant in QuarterFinal, and used for modulating BCM Hebbian learning (see AvgLrn) and overall learning rate
	Avg float32 `` /* 234-byte string literal not displayed */

	// running variance of cosine (normalized dot product) difference between ActP and ActM -- computed with CosDiff.Tau time constant in QuarterFinal, used for modulating overall learning rate
	Var float32 `` /* 193-byte string literal not displayed */

	// 1 - Avg and 0 for non-Hidden layers
	AvgLrn float32 `desc:"1 - Avg and 0 for non-Hidden layers"`

	// 1 - AvgLrn and 0 for non-Hidden layers -- this is the value of Avg used for AvgLParams ErrMod modulation of the AvgLLrn factor if enabled
	ModAvgLLrn float32 `` /* 144-byte string literal not displayed */
}

type DWtNormParams struct {

	// [def: true] whether to use dwt normalization, only on error-driven dwt component, based on projection-level max_avg value -- slowly decays and instantly resets to any current max
	On bool `` /* 184-byte string literal not displayed */

	// [def: 1000,10000] [viewif: On] [min: 1] time constant for decay of dwnorm factor -- generally should be long-ish, between 1000-10000 -- integration rate factor is 1/tau
	DecayTau float32 `` /* 172-byte string literal not displayed */

	// [def: 0.001] [viewif: On] [min: 0] minimum effective value of the normalization factor -- provides a lower bound to how much normalization can be applied
	NormMin float32 `` /* 157-byte string literal not displayed */

	// [def: 0.15] [viewif: On] [min: 0] overall learning rate multiplier to compensate for changes due to use of normalization -- allows for a common master learning rate to be used between different conditions -- 0.1 for synapse-level, maybe higher for other levels
	LrComp float32 `` /* 264-byte string literal not displayed */

	// [def: false] [viewif: On] record the avg, max values of err, bcm hebbian, and overall dwt change per con group and per projection
	Stats bool `` /* 134-byte string literal not displayed */

	// [view: -] rate constant of decay = 1 / decay_tau
	DecayDt float32 `inactive:"+" view:"-" json:"-" xml:"-" desc:"rate constant of decay = 1 / decay_tau"`

	// [view: -] complement rate constant of decay = 1 - (1 / decay_tau)
	DecayDtC float32 `inactive:"+" view:"-" json:"-" xml:"-" desc:"complement rate constant of decay = 1 - (1 / decay_tau)"`
}

type DtParams struct {

	// [def: 1,0.5] [min: 0] overall rate constant for numerical integration, for all equations at the unit level -- all time constants are specified in millisecond units, with one cycle = 1 msec -- if you instead want to make one cycle = 2 msec, you can do this globally by setting this integ value to 2 (etc).  However, stability issues will likely arise if you go too high.  For improved numerical stability, you may even need to reduce this value to 0.5 or possibly even lower (typically however this is not necessary).  MUST also coordinate this with network.time_inc variable to ensure that global network.time reflects simulated time accurately
	Integ float32 `` /* 649-byte string literal not displayed */

	// [def: 3.3] [min: 1] membrane potential and rate-code activation time constant in cycles, which should be milliseconds typically (roughly, how long it takes for value to change significantly -- 1.4x the half-life) -- reflects the capacitance of the neuron in principle -- biological default for AdEx spiking model C = 281 pF = 2.81 normalized -- for rate-code activation, this also determines how fast to integrate computed activation values over time
	VmTau float32 `` /* 455-byte string literal not displayed */

	// [def: 1.4,3,5] [min: 1] time constant for integrating synaptic conductances, in cycles, which should be milliseconds typically (roughly, how long it takes for value to change significantly -- 1.4x the half-life) -- this is important for damping oscillations -- generally reflects time constants associated with synaptic channels which are not modeled in the most abstract rate code models (set to 1 for detailed spiking models with more realistic synaptic currents) -- larger values (e.g., 3) can be important for models with higher conductances that otherwise might be more prone to oscillation.
	GTau float32 `` /* 601-byte string literal not displayed */

	// [def: 200] for integrating activation average (ActAvg), time constant in trials (roughly, how long it takes for value to change significantly) -- used mostly for visualization and tracking *hog* units
	AvgTau float32 `` /* 206-byte string literal not displayed */

	// [view: -] nominal rate = Integ / tau
	VmDt float32 `view:"-" json:"-" xml:"-" desc:"nominal rate = Integ / tau"`

	// [view: -] rate = Integ / tau
	GDt float32 `view:"-" json:"-" xml:"-" desc:"rate = Integ / tau"`

	// [view: -] rate = 1 / tau
	AvgDt float32 `view:"-" json:"-" xml:"-" desc:"rate = 1 / tau"`
}

type InhibParams struct {

	// [view: inline] inhibition across the entire layer
	Layer fffb.Params `view:"inline" desc:"inhibition across the entire layer"`

	// [view: inline] inhibition across sub-pools of units, for layers with 4D shape
	Pool fffb.Params `view:"inline" desc:"inhibition across sub-pools of units, for layers with 4D shape"`

	// [view: inline] neuron self-inhibition parameters -- can be beneficial for producing more graded, linear response -- not typically used in cortical networks
	Self SelfInhibParams `` /* 161-byte string literal not displayed */

	// [view: inline] running-average activation computation values -- for overall estimates of layer activation levels, used in netinput scaling
	ActAvg ActAvgParams `` /* 144-byte string literal not displayed */
}

type LayFunChan chan func(ly LeabraLayer)

type Layer struct {
	LayerStru

	// [view: add-fields] Activation parameters and methods for computing activations
	Act ActParams `view:"add-fields" desc:"Activation parameters and methods for computing activations"`

	// [view: add-fields] Inhibition parameters and methods for computing layer-level inhibition
	Inhib InhibParams `view:"add-fields" desc:"Inhibition parameters and methods for computing layer-level inhibition"`

	// [view: add-fields] Learning parameters and methods that operate at the neuron level
	Learn LearnNeurParams `view:"add-fields" desc:"Learning parameters and methods that operate at the neuron level"`

	// slice of neurons for this layer -- flat list of len = Shp.Len(). You must iterate over index and use pointer to modify values.
	Neurons []Neuron `` /* 133-byte string literal not displayed */

	// inhibition and other pooled, aggregate state variables -- flat list has at least of 1 for layer, and one for each sub-pool (unit group) if shape supports that (4D).  You must iterate over index and use pointer to modify values.
	Pools []Pool `` /* 234-byte string literal not displayed */

	// cosine difference between ActM, ActP stats
	CosDiff CosDiffStats `desc:"cosine difference between ActM, ActP stats"`
}

type LayerStru struct {

	// [view: -] we need a pointer to ourselves as an LeabraLayer (which subsumes emer.Layer), which can always be used to extract the true underlying type of object when layer is embedded in other structs -- function receivers do not have this ability so this is necessary.
	LeabraLay LeabraLayer `` /* 299-byte string literal not displayed */

	// [view: -] our parent network, in case we need to use it to find other layers etc -- set when added by network
	Network emer.Network `` /* 141-byte string literal not displayed */

	// Name of the layer -- this must be unique within the network, which has a map for quick lookup and layers are typically accessed directly by name
	Nm string `` /* 151-byte string literal not displayed */

	// Class is for applying parameter styles, can be space separated multple tags
	Cls string `desc:"Class is for applying parameter styles, can be space separated multple tags"`

	// inactivate this layer -- allows for easy experimentation
	Off bool `desc:"inactivate this layer -- allows for easy experimentation"`

	// shape of the layer -- can be 2D for basic layers and 4D for layers with sub-groups (hypercolumns) -- order is outer-to-inner (row major) so Y then X for 2D and for 4D: Y-X unit pools then Y-X neurons within pools
	Shp etensor.Shape `` /* 219-byte string literal not displayed */

	// type of layer -- Hidden, Input, Target, Compare, or extended type in specialized algorithms -- matches against .Class parameter styles (e.g., .Hidden etc)
	Typ emer.LayerType `` /* 161-byte string literal not displayed */

	// the thread number (go routine) to use in updating this layer. The user is responsible for allocating layers to threads, trying to maintain an even distribution across layers and establishing good break-points.
	Thr int `` /* 216-byte string literal not displayed */

	// [view: inline] Spatial relationship to other layer, determines positioning
	Rel relpos.Rel `view:"inline" desc:"Spatial relationship to other layer, determines positioning"`

	// position of lower-left-hand corner of layer in 3D space, computed from Rel.  Layers are in X-Y width - height planes, stacked vertically in Z axis.
	Ps mat32.Vec3 `` /* 154-byte string literal not displayed */

	// a 0..n-1 index of the position of the layer within list of layers in the network. For Leabra networks, it only has significance in determining who gets which weights for enforcing initial weight symmetry -- higher layers get weights from lower layers.
	Idx int `` /* 258-byte string literal not displayed */

	// indexes of representative units in the layer, for computationally expensive stats or displays
	RepIxs []int `desc:"indexes of representative units in the layer, for computationally expensive stats or displays"`

	// shape of representative units in the layer -- if RepIxs is empty or .Shp is nil, use overall layer shape
	RepShp etensor.Shape `desc:"shape of representative units in the layer -- if RepIxs is empty or .Shp is nil, use overall layer shape"`

	// list of receiving projections into this layer from other layers
	RcvPrjns LeabraPrjns `desc:"list of receiving projections into this layer from other layers"`

	// list of sending projections from this layer to other layers
	SndPrjns LeabraPrjns `desc:"list of sending projections from this layer to other layers"`
}

type LeabraLayer interface {
	emer.Layer

	// AsLeabra returns this layer as a leabra.Layer -- so that the LeabraLayer
	// interface does not need to include accessors to all the basic stuff
	AsLeabra() *Layer

	// SetThread sets the thread number for this layer to run on
	SetThread(thr int)

	// InitWts initializes the weight values in the network, i.e., resetting learning
	// Also calls InitActs
	InitWts()

	// InitActAvg initializes the running-average activation values that drive learning.
	InitActAvg()

	// InitActs fully initializes activation state -- only called automatically during InitWts
	InitActs()

	// InitWtsSym initializes the weight symmetry -- higher layers copy weights from lower layers
	InitWtSym()

	// InitExt initializes external input state -- called prior to apply ext
	InitExt()

	// ApplyExt applies external input in the form of an etensor.Tensor
	// If the layer is a Target or Compare layer type, then it goes in Targ
	// otherwise it goes in Ext.
	ApplyExt(ext etensor.Tensor)

	// ApplyExt1D applies external input in the form of a flat 1-dimensional slice of floats
	// If the layer is a Target or Compare layer type, then it goes in Targ
	// otherwise it goes in Ext
	ApplyExt1D(ext []float64)

	// UpdateExtFlags updates the neuron flags for external input based on current
	// layer Type field -- call this if the Type has changed since the last
	// ApplyExt* method call.
	UpdateExtFlags()

	// RecvPrjns returns the slice of receiving projections for this layer
	RecvPrjns() *LeabraPrjns

	// SendPrjns returns the slice of sending projections for this layer
	SendPrjns() *LeabraPrjns

	// IsTarget returns true if this layer is a Target layer.
	// By default, returns true for layers of Type == emer.Target
	// Other Target layers include the TRCLayer in deep predictive learning.
	// This is used for turning off BCM hebbian learning,
	// in CosDiffFmActs to set the CosDiff.ModAvgLLrn value
	// for error-modulated level of hebbian learning.
	// It is also used in WtBal to not apply it to target layers.
	// In both cases, Target layers are purely error-driven.
	IsTarget() bool

	// AlphaCycInit handles all initialization at start of new input pattern.
	// Should already have presented the external input to the network at this point.
	// If updtActAvg is true, this includes updating the running-average
	// activations for each layer / pool, and the AvgL running average used
	// in BCM Hebbian learning.
	// The input scaling is updated  based on the layer-level running average acts,
	// and this can then change the behavior of the network,
	// so if you want 100% repeatable testing results, set this to false to
	// keep the existing scaling factors (e.g., can pass a train bool to
	// only update during training).  This flag also affects the AvgL learning
	// threshold
	AlphaCycInit(updtActAvg bool)

	// AvgLFmAvgM updates AvgL long-term running average activation that
	// drives BCM Hebbian learning
	AvgLFmAvgM()

	// GScaleFmAvgAct computes the scaling factor for synaptic conductance input
	// 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.
	GScaleFmAvgAct()

	// GenNoise generates random noise for all neurons
	GenNoise()

	// DecayState decays activation state by given proportion (default is on ly.Act.Init.Decay)
	DecayState(decay float32)

	// HardClamp hard-clamps the activations in the layer -- called during AlphaCycInit
	// for hard-clamped Input layers
	HardClamp()

	// InitGInc initializes synaptic conductance increments -- optional
	InitGInc()

	// SendGDelta sends change in activation since last sent, to increment recv
	// synaptic conductances G, if above thresholds
	SendGDelta(ltime *Time)

	// GFmInc integrates new synaptic conductances from increments sent during last SendGDelta
	GFmInc(ltime *Time)

	// AvgMaxGe computes the average and max Ge stats, used in inhibition
	AvgMaxGe(ltime *Time)

	// InhibiFmGeAct computes inhibition Gi from Ge and Act averages within relevant Pools
	InhibFmGeAct(ltime *Time)

	// ActFmG computes rate-code activation from Ge, Gi, Gl conductances
	// and updates learning running-average activations from that Act
	ActFmG(ltime *Time)

	// AvgMaxAct computes the average and max Act stats, used in inhibition
	AvgMaxAct(ltime *Time)

	// CyclePost is called after the standard Cycle update, as a separate
	// network layer loop.
	// This is reserved for any kind of special ad-hoc types that
	// need to do something special after Act is finally computed.
	// For example, sending a neuromodulatory signal such as dopamine.
	CyclePost(ltime *Time)

	// QuarterFinal does updating after end of a quarter
	QuarterFinal(ltime *Time)

	// CosDiffFmActs computes the cosine difference in activation state
	// between minus and plus phases.
	// This is also used for modulating the amount of BCM hebbian learning
	CosDiffFmActs()

	// DWt computes the weight change (learning) -- calls DWt method on sending projections
	DWt()

	// WtFmDWt updates the weights from delta-weight changes -- on the sending projections
	WtFmDWt()

	// WtBalFmWt computes the Weight Balance factors based on average recv weights
	WtBalFmWt()

	// LrateMult sets the new Lrate parameter for Prjns to LrateInit * mult.
	// Useful for implementing learning rate schedules.
	LrateMult(mult float32)
}

type LeabraNetwork interface {
	emer.Network

	// AsLeabra returns this network as a leabra.Network -- so that the
	// LeabraNetwork interface does not need to include accessors
	// to all the basic stuff
	AsLeabra() *Network

	// NewLayer creates a new concrete layer of appropriate type for this network
	NewLayer() emer.Layer

	// NewPrjn creates a new concrete projection of appropriate type for this network
	NewPrjn() emer.Prjn

	// AlphaCycInit handles all initialization at start of new input pattern.
	// Should already have presented the external input to the network at this point.
	// If updtActAvg is true, this includes updating the running-average
	// activations for each layer / pool, and the AvgL running average used
	// in BCM Hebbian learning.
	// The input scaling is updated  based on the layer-level running average acts,
	// and this can then change the behavior of the network,
	// so if you want 100% repeatable testing results, set this to false to
	// keep the existing scaling factors (e.g., can pass a train bool to
	// only update during training).  This flag also affects the AvgL learning
	// threshold
	AlphaCycInitImpl(updtActAvg bool)

	// CycleImpl runs one cycle of activation updating:
	// * Sends Ge increments from sending to receiving layers
	// * Average and Max Ge stats
	// * Inhibition based on Ge stats and Act Stats (computed at end of Cycle)
	// * Activation from Ge, Gi, and Gl
	// * Average and Max Act stats
	// This basic version doesn't use the time info, but more specialized types do, and we
	// want to keep a consistent API for end-user code.
	CycleImpl(ltime *Time)

	// CyclePostImpl is called after the standard Cycle update, and calls CyclePost
	// on Layers -- this is reserved for any kind of special ad-hoc types that
	// need to do something special after Act is finally computed.
	// For example, sending a neuromodulatory signal such as dopamine.
	CyclePostImpl(ltime *Time)

	// QuarterFinalImpl does updating after end of a quarter
	QuarterFinalImpl(ltime *Time)

	// DWtImpl computes the weight change (learning) based on current
	// running-average activation values
	DWtImpl()

	// WtFmDWtImpl updates the weights from delta-weight changes.
	// Also calls WtBalFmWt every WtBalInterval times
	WtFmDWtImpl()
}

type LeabraPrjn interface {
	emer.Prjn

	// AsLeabra returns this prjn as a leabra.Prjn -- so that the LeabraPrjn
	// interface does not need to include accessors to all the basic stuff.
	AsLeabra() *Prjn

	// InitWts initializes weight values according to Learn.WtInit params
	InitWts()

	// InitWtSym initializes weight symmetry -- is given the reciprocal projection where
	// the Send and Recv layers are reversed.
	InitWtSym(rpj LeabraPrjn)

	// InitGInc initializes the per-projection synaptic conductance threadsafe increments.
	// This is not typically needed (called during InitWts only) but can be called when needed
	InitGInc()

	// SendGDelta sends the delta-activation from sending neuron index si,
	// to integrate synaptic conductances on receivers
	SendGDelta(si int, delta float32)

	// RecvGInc increments the receiver's synaptic conductances from those of all the projections.
	RecvGInc()

	// DWt computes the weight change (learning) -- on sending projections
	DWt()

	// WtFmDWt updates the synaptic weight values from delta-weight changes -- on sending projections
	WtFmDWt()

	// WtBalFmWt computes the Weight Balance factors based on average recv weights
	WtBalFmWt()

	// LrateMult sets the new Lrate parameter for Prjns to LrateInit * mult.
	// Useful for implementing learning rate schedules.
	LrateMult(mult float32)
}

type LeabraPrjns []LeabraPrjn

type LearnNeurParams struct {

	// [view: inline] parameters for computing running average activations that drive learning
	ActAvg LrnActAvgParams `view:"inline" desc:"parameters for computing running average activations that drive learning"`

	// [view: inline] parameters for computing AvgL long-term running average
	AvgL AvgLParams `view:"inline" desc:"parameters for computing AvgL long-term running average"`

	// [view: inline] parameters for computing cosine diff between minus and plus phase
	CosDiff CosDiffParams `view:"inline" desc:"parameters for computing cosine diff between minus and plus phase"`
}

type LearnSynParams struct {

	// enable learning for this projection
	Learn bool `desc:"enable learning for this projection"`

	// [viewif: Learn] current effective learning rate (multiplies DWt values, determining rate of change of weights)
	Lrate float32 `viewif:"Learn" desc:"current effective learning rate (multiplies DWt values, determining rate of change of weights)"`

	// [viewif: Learn] initial learning rate -- this is set from Lrate in UpdateParams, which is called when Params are updated, and used in LrateMult to compute a new learning rate for learning rate schedules.
	LrateInit float32 `` /* 209-byte string literal not displayed */

	// [view: inline] [viewif: Learn] parameters for the XCal learning rule
	XCal XCalParams `viewif:"Learn" view:"inline" desc:"parameters for the XCal learning rule"`

	// [view: inline] [viewif: Learn] parameters for the sigmoidal contrast weight enhancement
	WtSig WtSigParams `viewif:"Learn" view:"inline" desc:"parameters for the sigmoidal contrast weight enhancement"`

	// [view: inline] [viewif: Learn] parameters for normalizing weight changes by abs max dwt
	Norm DWtNormParams `viewif:"Learn" view:"inline" desc:"parameters for normalizing weight changes by abs max dwt"`

	// [view: inline] [viewif: Learn] parameters for momentum across weight changes
	Momentum MomentumParams `viewif:"Learn" view:"inline" desc:"parameters for momentum across weight changes"`

	// [view: inline] [viewif: Learn] parameters for balancing strength of weight increases vs. decreases
	WtBal WtBalParams `viewif:"Learn" view:"inline" desc:"parameters for balancing strength of weight increases vs. decreases"`
}

type LrnActAvgParams struct {

	// [def: 2,4,7] [min: 1] time constant in cycles, which should be milliseconds typically (roughly, how long it takes for value to change significantly -- 1.4x the half-life), for continuously updating the super-short time-scale avg_ss value -- this is provides a pre-integration step before integrating into the avg_s short time scale -- it is particularly important for spiking -- in general 4 is the largest value without starting to impair learning, but a value of 7 can be combined with m_in_s = 0 with somewhat worse results
	SSTau float32 `` /* 532-byte string literal not displayed */

	// [def: 2] [min: 1] time constant in cycles, which should be milliseconds typically (roughly, how long it takes for value to change significantly -- 1.4x the half-life), for continuously updating the short time-scale avg_s value from the super-short avg_ss value (cascade mode) -- avg_s represents the plus phase learning signal that reflects the most recent past information
	STau float32 `` /* 378-byte string literal not displayed */

	// [def: 10] [min: 1] time constant in cycles, which should be milliseconds typically (roughly, how long it takes for value to change significantly -- 1.4x the half-life), for continuously updating the medium time-scale avg_m value from the short avg_s value (cascade mode) -- avg_m represents the minus phase learning signal that reflects the expectation representation prior to experiencing the outcome (in addition to the outcome) -- the default value of 10 generally cannot be exceeded without impairing learning
	MTau float32 `` /* 518-byte string literal not displayed */

	// [def: 0.1,0] [min: 0] [max: 1] how much of the medium term average activation to mix in with the short (plus phase) to compute the Neuron AvgSLrn variable that is used for the unit's short-term average in learning. This is important to ensure that when unit turns off in plus phase (short time scale), enough medium-phase trace remains so that learning signal doesn't just go all the way to 0, at which point no learning would take place -- typically need faster time constant for updating S such that this trace of the M signal is lost -- can set SSTau=7 and set this to 0 but learning is generally somewhat worse
	LrnM float32 `` /* 618-byte string literal not displayed */

	// [def: 0.15] [min: 0] [max: 1] initial value for average
	Init float32 `def:"0.15" min:"0" max:"1" desc:"initial value for average"`

	// [view: -] rate = 1 / tau
	SSDt float32 `view:"-" json:"-" xml:"-" inactive:"+" desc:"rate = 1 / tau"`

	// [view: -] rate = 1 / tau
	SDt float32 `view:"-" json:"-" xml:"-" inactive:"+" desc:"rate = 1 / tau"`

	// [view: -] rate = 1 / tau
	MDt float32 `view:"-" json:"-" xml:"-" inactive:"+" desc:"rate = 1 / tau"`

	// [view: -] 1-LrnM
	LrnS float32 `view:"-" json:"-" xml:"-" inactive:"+" desc:"1-LrnM"`
}

type MomentumParams struct {

	// [def: true] whether to use standard simple momentum
	On bool `def:"true" desc:"whether to use standard simple momentum"`

	// [def: 10] [viewif: On] [min: 1] time constant factor for integration of momentum -- 1/tau is dt (e.g., .1), and 1-1/tau (e.g., .95 or .9) is traditional momentum time-integration factor
	MTau float32 `` /* 189-byte string literal not displayed */

	// [def: 0.1] [viewif: On] [min: 0] overall learning rate multiplier to compensate for changes due to JUST momentum without normalization -- allows for a common master learning rate to be used between different conditions -- generally should use .1 to compensate for just momentum itself
	LrComp float32 `` /* 288-byte string literal not displayed */

	// [view: -] rate constant of momentum integration = 1 / m_tau
	MDt float32 `inactive:"+" view:"-" json:"-" xml:"-" desc:"rate constant of momentum integration = 1 / m_tau"`

	// [view: -] complement rate constant of momentum integration = 1 - (1 / m_tau)
	MDtC float32 `inactive:"+" view:"-" json:"-" xml:"-" desc:"complement rate constant of momentum integration = 1 - (1 / m_tau)"`
}

type Network struct {
	NetworkStru

	// [def: 10] how frequently to update the weight balance average weight factor -- relatively expensive
	WtBalInterval int `def:"10" desc:"how frequently to update the weight balance average weight factor -- relatively expensive"`

	// counter for how long it has been since last WtBal
	WtBalCtr int `inactive:"+" desc:"counter for how long it has been since last WtBal"`
}

type NetworkStru struct {

	// [view: -] we need a pointer to ourselves as an emer.Network, which can always be used to extract the true underlying type of object when network is embedded in other structs -- function receivers do not have this ability so this is necessary.
	EmerNet emer.Network `` /* 274-byte string literal not displayed */

	// overall name of network -- helps discriminate if there are multiple
	Nm string `desc:"overall name of network -- helps discriminate if there are multiple"`

	// list of layers
	Layers emer.Layers `desc:"list of layers"`

	// filename of last weights file loaded or saved
	WtsFile string `desc:"filename of last weights file loaded or saved"`

	// [view: -] map of name to layers -- layer names must be unique
	LayMap map[string]emer.Layer `view:"-" desc:"map of name to layers -- layer names must be unique"`

	// [view: -] map of layer classes -- made during Build
	LayClassMap map[string][]string `view:"-" desc:"map of layer classes -- made during Build"`

	// [view: -] minimum display position in network
	MinPos mat32.Vec3 `view:"-" desc:"minimum display position in network"`

	// [view: -] maximum display position in network
	MaxPos mat32.Vec3 `view:"-" desc:"maximum display position in network"`

	// optional metadata that is saved in network weights files -- e.g., can indicate number of epochs that were trained, or any other information about this network that would be useful to save
	MetaData map[string]string `` /* 194-byte string literal not displayed */

	// number of parallel threads (go routines) to use -- this is computed directly from the Layers which you must explicitly allocate to different threads -- updated during Build of network
	NThreads int `` /* 203-byte string literal not displayed */

	// if set, runtime.LockOSThread() is called on the compute threads, which can be faster on large networks on some architectures -- experimentation is recommended
	LockThreads bool `` /* 165-byte string literal not displayed */

	// [view: -] layers per thread -- outer group is threads and inner is layers operated on by that thread -- based on user-assigned threads, initialized during Build
	ThrLay [][]emer.Layer `` /* 179-byte string literal not displayed */

	// [view: -] layer function channels, per thread
	ThrChans []LayFunChan `view:"-" desc:"layer function channels, per thread"`

	// [view: -] timers for each thread, so you can see how evenly the workload is being distributed
	ThrTimes []timer.Time `view:"-" desc:"timers for each thread, so you can see how evenly the workload is being distributed"`

	// [view: -] timers for each major function (step of processing)
	FunTimes map[string]*timer.Time `view:"-" desc:"timers for each major function (step of processing)"`

	// [view: -] network-level wait group for synchronizing threaded layer calls
	WaitGp sync.WaitGroup `view:"-" desc:"network-level wait group for synchronizing threaded layer calls"`
}

type NeurFlags int32

type Neuron struct {

	// bit flags for binary state variables
	Flags NeurFlags `desc:"bit flags for binary state variables"`

	// index of the sub-level inhibitory pool that this neuron is in (only for 4D shapes, the pool (unit-group / hypercolumn) structure level) -- indicies start at 1 -- 0 is layer-level pool (is 0 if no sub-pools).
	SubPool int32 `` /* 214-byte string literal not displayed */

	// rate-coded activation value reflecting final output of neuron communicated to other neurons, typically in range 0-1.  This value includes adaptation and synaptic depression / facilitation effects which produce temporal contrast (see ActLrn for version without this).  For rate-code activation, this is noisy-x-over-x-plus-one (NXX1) function; for discrete spiking it is computed from the inverse of the inter-spike interval (ISI), and Spike reflects the discrete spikes.
	Act float32 `` /* 477-byte string literal not displayed */

	// learning activation value, reflecting *dendritic* activity that is not affected by synaptic depression or adapdation channels which are located near the axon hillock.  This is the what drives the Avg* values that drive learning. Computationally, neurons strongly discount the signals sent to other neurons to provide temporal contrast, but need to learn based on a more stable reflection of their overall inputs in the dendrites.
	ActLrn float32 `` /* 436-byte string literal not displayed */

	// total excitatory synaptic conductance -- the net excitatory input to the neuron -- does *not* include Gbar.E
	Ge float32 `desc:"total excitatory synaptic conductance -- the net excitatory input to the neuron -- does *not* include Gbar.E"`

	// total inhibitory synaptic conductance -- the net inhibitory input to the neuron -- does *not* include Gbar.I
	Gi float32 `desc:"total inhibitory synaptic conductance -- the net inhibitory input to the neuron -- does *not* include Gbar.I"`

	// total potassium conductance, typically reflecting sodium-gated potassium currents involved in adaptation effects -- does *not* include Gbar.K
	Gk float32 `` /* 148-byte string literal not displayed */

	// net current produced by all channels -- drives update of Vm
	Inet float32 `desc:"net current produced by all channels -- drives update of Vm"`

	// membrane potential -- integrates Inet current over time
	Vm float32 `desc:"membrane potential -- integrates Inet current over time"`

	// target value: drives learning to produce this activation value
	Targ float32 `desc:"target value: drives learning to produce this activation value"`

	// external input: drives activation of unit from outside influences (e.g., sensory input)
	Ext float32 `desc:"external input: drives activation of unit from outside influences (e.g., sensory input)"`

	// super-short time-scale average of ActLrn activation -- provides the lowest-level time integration -- for spiking this integrates over spikes before subsequent averaging, and it is also useful for rate-code to provide a longer time integral overall
	AvgSS float32 `` /* 254-byte string literal not displayed */

	// short time-scale average of ActLrn activation -- tracks the most recent activation states (integrates over AvgSS values), and represents the plus phase for learning in XCAL algorithms
	AvgS float32 `` /* 190-byte string literal not displayed */

	// medium time-scale average of ActLrn activation -- integrates over AvgS values, and represents the minus phase for learning in XCAL algorithms
	AvgM float32 `` /* 148-byte string literal not displayed */

	// long time-scale average of medium-time scale (trial level) activation, used for the BCM-style floating threshold in XCAL
	AvgL float32 `` /* 127-byte string literal not displayed */

	// how much to learn based on the long-term floating threshold (AvgL) for BCM-style Hebbian learning -- is modulated by level of AvgL itself (stronger Hebbian as average activation goes higher) and optionally the average amount of error experienced in the layer (to retain a common proportionality with the level of error-driven learning across layers)
	AvgLLrn float32 `` /* 356-byte string literal not displayed */

	// short time-scale activation average that is actually used for learning -- typically includes a small contribution from AvgM in addition to mostly AvgS, as determined by LrnActAvgParams.LrnM -- important to ensure that when unit turns off in plus phase (short time scale), enough medium-phase trace remains so that learning signal doesn't just go all the way to 0, at which point no learning would take place
	AvgSLrn float32 `` /* 414-byte string literal not displayed */

	// the activation state at start of current alpha cycle (same as the state at end of previous cycle)
	ActQ0 float32 `desc:"the activation state at start of current alpha cycle (same as the state at end of previous cycle)"`

	// the activation state at end of first quarter of current alpha cycle
	ActQ1 float32 `desc:"the activation state at end of first quarter of current alpha cycle"`

	// the activation state at end of second quarter of current alpha cycle
	ActQ2 float32 `desc:"the activation state at end of second quarter of current alpha cycle"`

	// the activation state at end of third quarter, which is the traditional posterior-cortical minus phase activation
	ActM float32 `desc:"the activation state at end of third quarter, which is the traditional posterior-cortical minus phase activation"`

	// the activation state at end of fourth quarter, which is the traditional posterior-cortical plus_phase activation
	ActP float32 `desc:"the activation state at end of fourth quarter, which is the traditional posterior-cortical plus_phase activation"`

	// ActP - ActM -- difference between plus and minus phase acts -- reflects the individual error gradient for this neuron in standard error-driven learning terms
	ActDif float32 `` /* 164-byte string literal not displayed */

	// delta activation: change in Act from one cycle to next -- can be useful to track where changes are taking place
	ActDel float32 `desc:"delta activation: change in Act from one cycle to next -- can be useful to track where changes are taking place"`

	// average activation (of final plus phase activation state) over long time intervals (time constant = DtPars.AvgTau -- typically 200) -- useful for finding hog units and seeing overall distribution of activation
	ActAvg float32 `` /* 216-byte string literal not displayed */

	// noise value added to unit (ActNoiseParams determines distribution, and when / where it is added)
	Noise float32 `desc:"noise value added to unit (ActNoiseParams determines distribution, and when / where it is added)"`

	// aggregated synaptic inhibition (from Inhib projections) -- time integral of GiRaw -- this is added with computed FFFB inhibition to get the full inhibition in Gi
	GiSyn float32 `` /* 168-byte string literal not displayed */

	// total amount of self-inhibition -- time-integrated to avoid oscillations
	GiSelf float32 `desc:"total amount of self-inhibition -- time-integrated to avoid oscillations"`

	// last activation value sent (only send when diff is over threshold)
	ActSent float32 `desc:"last activation value sent (only send when diff is over threshold)"`

	// raw excitatory conductance (net input) received from sending units (send delta's are added to this value)
	GeRaw float32 `desc:"raw excitatory conductance (net input) received from sending units (send delta's are added to this value)"`

	// raw inhibitory conductance (net input) received from sending units (send delta's are added to this value)
	GiRaw float32 `desc:"raw inhibitory conductance (net input) received from sending units (send delta's are added to this value)"`

	// conductance of sodium-gated potassium channel (KNa) fast dynamics (M-type) -- produces accommodation / adaptation of firing
	GknaFast float32 `` /* 130-byte string literal not displayed */

	// conductance of sodium-gated potassium channel (KNa) medium dynamics (Slick) -- produces accommodation / adaptation of firing
	GknaMed float32 `` /* 131-byte string literal not displayed */

	// conductance of sodium-gated potassium channel (KNa) slow dynamics (Slack) -- produces accommodation / adaptation of firing
	GknaSlow float32 `` /* 129-byte string literal not displayed */

	// whether neuron has spiked or not (0 or 1), for discrete spiking neurons.
	Spike float32 `desc:"whether neuron has spiked or not (0 or 1), for discrete spiking neurons."`

	// current inter-spike-interval -- counts up since last spike.  Starts at -1 when initialized.
	ISI float32 `desc:"current inter-spike-interval -- counts up since last spike.  Starts at -1 when initialized."`

	// average inter-spike-interval -- average time interval between spikes.  Starts at -1 when initialized, and goes to -2 after first spike, and is only valid after the second spike post-initialization.
	ISIAvg float32 `` /* 204-byte string literal not displayed */
}

type OptThreshParams struct {

	// [def: 0.1] don't send activation when act <= send -- greatly speeds processing
	Send float32 `def:"0.1" desc:"don't send activation when act <= send -- greatly speeds processing"`

	// [def: 0.005] don't send activation changes until they exceed this threshold: only for when LeabraNetwork::send_delta is on!
	Delta float32 `` /* 129-byte string literal not displayed */
}

type Pool struct {

	// starting and ending (exlusive) indexes for the list of neurons in this pool
	StIdx, EdIdx int `desc:"starting and ending (exlusive) indexes for the list of neurons in this pool"`

	// FFFB inhibition computed values, including Ge and Act AvgMax which drive inhibition
	Inhib fffb.Inhib `desc:"FFFB inhibition computed values, including Ge and Act AvgMax which drive inhibition"`

	// minus phase average and max Act activation values, for ActAvg updt
	ActM minmax.AvgMax32 `desc:"minus phase average and max Act activation values, for ActAvg updt"`

	// plus phase average and max Act activation values, for ActAvg updt
	ActP minmax.AvgMax32 `desc:"plus phase average and max Act activation values, for ActAvg updt"`

	// running-average activation levels used for netinput scaling and adaptive inhibition
	ActAvg ActAvg `desc:"running-average activation levels used for netinput scaling and adaptive inhibition"`
}

type Prjn struct {
	PrjnStru

	// [view: inline] initial random weight distribution
	WtInit WtInitParams `view:"inline" desc:"initial random weight distribution"`

	// [view: inline] weight scaling parameters: modulates overall strength of projection, using both absolute and relative factors
	WtScale WtScaleParams `` /* 130-byte string literal not displayed */

	// [view: add-fields] synaptic-level learning parameters
	Learn LearnSynParams `view:"add-fields" desc:"synaptic-level learning parameters"`

	// synaptic state values, ordered by the sending layer units which owns them -- one-to-one with SConIdx array
	Syns []Synapse `desc:"synaptic state values, ordered by the sending layer units which owns them -- one-to-one with SConIdx array"`

	// scaling factor for integrating synaptic input conductances (G's) -- computed in AlphaCycInit, incorporates running-average activity levels
	GScale float32 `` /* 145-byte string literal not displayed */

	// local per-recv unit increment accumulator for synaptic conductance from sending units -- goes to either GeRaw or GiRaw on neuron depending on projection type -- this will be thread-safe
	GInc []float32 `` /* 192-byte string literal not displayed */

	// weight balance state variables for this projection, one per recv neuron
	WbRecv []WtBalRecvPrjn `desc:"weight balance state variables for this projection, one per recv neuron"`
}

type PrjnStru struct {

	// [view: -] we need a pointer to ourselves as an LeabraPrjn, which can always be used to extract the true underlying type of object when prjn is embedded in other structs -- function receivers do not have this ability so this is necessary.
	LeabraPrj LeabraPrjn `` /* 269-byte string literal not displayed */

	// inactivate this projection -- allows for easy experimentation
	Off bool `desc:"inactivate this projection -- allows for easy experimentation"`

	// Class is for applying parameter styles, can be space separated multple tags
	Cls string `desc:"Class is for applying parameter styles, can be space separated multple tags"`

	// can record notes about this projection here
	Notes string `desc:"can record notes about this projection here"`

	// sending layer for this projection
	Send emer.Layer `desc:"sending layer for this projection"`

	// receiving layer for this projection -- the emer.Layer interface can be converted to the specific Layer type you are using, e.g., rlay := prjn.Recv.(*leabra.Layer)
	Recv emer.Layer `` /* 169-byte string literal not displayed */

	// pattern of connectivity
	Pat prjn.Pattern `desc:"pattern of connectivity"`

	// type of projection -- Forward, Back, Lateral, or extended type in specialized algorithms -- matches against .Cls parameter styles (e.g., .Back etc)
	Typ emer.PrjnType `` /* 154-byte string literal not displayed */

	// [view: -] number of recv connections for each neuron in the receiving layer, as a flat list
	RConN []int32 `view:"-" desc:"number of recv connections for each neuron in the receiving layer, as a flat list"`

	// [view: inline] average and maximum number of recv connections in the receiving layer
	RConNAvgMax minmax.AvgMax32 `inactive:"+" view:"inline" desc:"average and maximum number of recv connections in the receiving layer"`

	// [view: -] starting index into ConIdx list for each neuron in receiving layer -- just a list incremented by ConN
	RConIdxSt []int32 `view:"-" desc:"starting index into ConIdx list for each neuron in receiving layer -- just a list incremented by ConN"`

	// [view: -] index of other neuron on sending side of projection, ordered by the receiving layer's order of units as the outer loop (each start is in ConIdxSt), and then by the sending layer's units within that
	RConIdx []int32 `` /* 213-byte string literal not displayed */

	// [view: -] index of synaptic state values for each recv unit x connection, for the receiver projection which does not own the synapses, and instead indexes into sender-ordered list
	RSynIdx []int32 `` /* 185-byte string literal not displayed */

	// [view: -] number of sending connections for each neuron in the sending layer, as a flat list
	SConN []int32 `view:"-" desc:"number of sending connections for each neuron in the sending layer, as a flat list"`

	// [view: inline] average and maximum number of sending connections in the sending layer
	SConNAvgMax minmax.AvgMax32 `inactive:"+" view:"inline" desc:"average and maximum number of sending connections in the sending layer"`

	// [view: -] starting index into ConIdx list for each neuron in sending layer -- just a list incremented by ConN
	SConIdxSt []int32 `view:"-" desc:"starting index into ConIdx list for each neuron in sending layer -- just a list incremented by ConN"`

	// [view: -] index of other neuron on receiving side of projection, ordered by the sending layer's order of units as the outer loop (each start is in ConIdxSt), and then by the sending layer's units within that
	SConIdx []int32 `` /* 213-byte string literal not displayed */
}

type Quarters int32

type SelfInhibParams struct {

	// enable neuron self-inhibition
	On bool `desc:"enable neuron self-inhibition"`

	// [def: 0.4] [viewif: On] strength of individual neuron self feedback inhibition -- can produce proportional activation behavior in individual units for specialized cases (e.g., scalar val or BG units), but not so good for typical hidden layers
	Gi float32 `` /* 247-byte string literal not displayed */

	// [def: 1.4] [viewif: On] time constant in cycles, which should be milliseconds typically (roughly, how long it takes for value to change significantly -- 1.4x the half-life) for integrating unit self feedback inhibitory values -- prevents oscillations that otherwise occur -- relatively rapid 1.4 typically works, but may need to go longer if oscillations are a problem
	Tau float32 `` /* 373-byte string literal not displayed */

	// [view: -] rate = 1 / tau
	Dt float32 `inactive:"+" view:"-" json:"-" xml:"-" desc:"rate = 1 / tau"`
}

type Synapse struct {

	// synaptic weight value -- sigmoid contrast-enhanced
	Wt float32 `desc:"synaptic weight value -- sigmoid contrast-enhanced"`

	// linear (underlying) weight value -- learns according to the lrate specified in the connection spec -- this is converted into the effective weight value, Wt, via sigmoidal contrast enhancement (see WtSigParams)
	LWt float32 `` /* 216-byte string literal not displayed */

	// change in synaptic weight, from learning
	DWt float32 `desc:"change in synaptic weight, from learning"`

	// DWt normalization factor -- reset to max of abs value of DWt, decays slowly down over time -- serves as an estimate of variance in weight changes over time
	Norm float32 `` /* 162-byte string literal not displayed */

	// momentum -- time-integrated DWt changes, to accumulate a consistent direction of weight change and cancel out dithering contradictory changes
	Moment float32 `` /* 148-byte string literal not displayed */

	// scaling parameter for this connection: effective weight value is scaled by this factor -- useful for topographic connectivity patterns e.g., to enforce more distant connections to always be lower in magnitude than closer connections.  Value defaults to 1 (cannot be exactly 0 -- otherwise is automatically reset to 1 -- use a very small number to approximate 0).  Typically set by using the prjn.Pattern Weights() values where appropriate
	Scale float32 `` /* 445-byte string literal not displayed */
}

type Time struct {

	// accumulated amount of time the network has been running, in simulation-time (not real world time), in seconds
	Time float32 `desc:"accumulated amount of time the network has been running, in simulation-time (not real world time), in seconds"`

	// cycle counter: number of iterations of activation updating (settling) on the current alpha-cycle (100 msec / 10 Hz) trial -- this counts time sequentially through the entire trial, typically from 0 to 99 cycles
	Cycle int `` /* 217-byte string literal not displayed */

	// total cycle count -- this increments continuously from whenever it was last reset -- typically this is number of milliseconds in simulation time
	CycleTot int `` /* 151-byte string literal not displayed */

	// [0-3] current gamma-frequency (25 msec / 40 Hz) quarter of alpha-cycle (100 msec / 10 Hz) trial being processed.  Due to 0-based indexing, the first quarter is 0, second is 1, etc -- the plus phase final quarter is 3.
	Quarter int `` /* 224-byte string literal not displayed */

	// true if this is the plus phase (final quarter = 3) -- else minus phase
	PlusPhase bool `desc:"true if this is the plus phase (final quarter = 3) -- else minus phase"`

	// [def: 0.001] amount of time to increment per cycle
	TimePerCyc float32 `def:"0.001" desc:"amount of time to increment per cycle"`

	// [def: 25] number of cycles per quarter to run -- 25 = standard 100 msec alpha-cycle
	CycPerQtr int `def:"25" desc:"number of cycles per quarter to run -- 25 = standard 100 msec alpha-cycle"`
}

type TimeScales int32

type WtBalParams struct {

	// perform weight balance soft normalization?  if so, maintains overall weight balance across units by progressively penalizing weight increases as a function of amount of averaged receiver weight above a high threshold (hi_thr) and long time-average activation above an act_thr -- this is generally very beneficial for larger models where hog units are a problem, but not as much for smaller models where the additional constraints are not beneficial -- uses a sigmoidal function: WbInc = 1 / (1 + HiGain*(WbAvg - HiThr) + ActGain * (nrn.ActAvg - ActThr)))
	On bool `` /* 561-byte string literal not displayed */

	// apply soft bounding to target layers -- appears to be beneficial but still testing
	Targs bool `desc:"apply soft bounding to target layers -- appears to be beneficial but still testing"`

	// [def: 0.25] [viewif: On] threshold on weight value for inclusion into the weight average that is then subject to the further HiThr threshold for then driving a change in weight balance -- this AvgThr allows only stronger weights to contribute so that weakening of lower weights does not dilute sensitivity to number and strength of strong weights
	AvgThr float32 `` /* 351-byte string literal not displayed */

	// [def: 0.4] [viewif: On] high threshold on weight average (subject to AvgThr) before it drives changes in weight increase vs. decrease factors
	HiThr float32 `` /* 146-byte string literal not displayed */

	// [def: 4] [viewif: On] gain multiplier applied to above-HiThr thresholded weight averages -- higher values turn weight increases down more rapidly as the weights become more imbalanced
	HiGain float32 `` /* 188-byte string literal not displayed */

	// [def: 0.4] [viewif: On] low threshold on weight average (subject to AvgThr) before it drives changes in weight increase vs. decrease factors
	LoThr float32 `` /* 145-byte string literal not displayed */

	// [def: 6,0] [viewif: On] gain multiplier applied to below-lo_thr thresholded weight averages -- higher values turn weight increases up more rapidly as the weights become more imbalanced -- generally beneficial but sometimes not -- worth experimenting with either 6 or 0
	LoGain float32 `` /* 273-byte string literal not displayed */
}

type WtBalRecvPrjn struct {

	// average of effective weight values that exceed WtBal.AvgThr across given Recv Neuron's connections for given Prjn
	Avg float32 `desc:"average of effective weight values that exceed WtBal.AvgThr across given Recv Neuron's connections for given Prjn"`

	// overall weight balance factor that drives changes in WbInc vs. WbDec via a sigmoidal function -- this is the net strength of weight balance changes
	Fact float32 `` /* 154-byte string literal not displayed */

	// weight balance increment factor -- extra multiplier to add to weight increases to maintain overall weight balance
	Inc float32 `desc:"weight balance increment factor -- extra multiplier to add to weight increases to maintain overall weight balance"`

	// weight balance decrement factor -- extra multiplier to add to weight decreases to maintain overall weight balance
	Dec float32 `desc:"weight balance decrement factor -- extra multiplier to add to weight decreases to maintain overall weight balance"`
}

type WtInitParams struct {
	erand.RndParams

	// symmetrize the weight values with those in reciprocal projection -- typically true for bidirectional excitatory connections
	Sym bool `` /* 130-byte string literal not displayed */
}

type WtScaleParams struct {

	// [def: 1] [min: 0] absolute scaling, which is not subject to normalization: directly multiplies weight values
	Abs float32 `def:"1" min:"0" desc:"absolute scaling, which is not subject to normalization: directly multiplies weight values"`

	// [min: 0] [Default: 1] relative scaling that shifts balance between different projections -- this is subject to normalization across all other projections into unit
	Rel float32 `` /* 169-byte string literal not displayed */
}

type WtSigParams struct {

	// [def: 1,6] [min: 0] gain (contrast, sharpness) of the weight contrast function (1 = linear)
	Gain float32 `def:"1,6" min:"0" desc:"gain (contrast, sharpness) of the weight contrast function (1 = linear)"`

	// [def: 1] [min: 0] offset of the function (1=centered at .5, >1=higher, <1=lower) -- 1 is standard for XCAL
	Off float32 `def:"1" min:"0" desc:"offset of the function (1=centered at .5, >1=higher, <1=lower) -- 1 is standard for XCAL"`

	// [def: true] apply exponential soft bounding to the weight changes
	SoftBound bool `def:"true" desc:"apply exponential soft bounding to the weight changes"`
}

type XCalParams struct {

	// [def: 1] [min: 0] multiplier on learning based on the medium-term floating average threshold which produces error-driven learning -- this is typically 1 when error-driven learning is being used, and 0 when pure Hebbian learning is used. The long-term floating average threshold is provided by the receiving unit
	MLrn float32 `` /* 316-byte string literal not displayed */

	// [def: false] if true, set a fixed AvgLLrn weighting factor that determines how much of the long-term floating average threshold (i.e., BCM, Hebbian) component of learning is used -- this is useful for setting a fully Hebbian learning connection, e.g., by setting MLrn = 0 and LLrn = 1. If false, then the receiving unit's AvgLLrn factor is used, which dynamically modulates the amount of the long-term component as a function of how active overall it is
	SetLLrn bool `` /* 459-byte string literal not displayed */

	// [viewif: SetLLrn] fixed l_lrn weighting factor that determines how much of the long-term floating average threshold (i.e., BCM, Hebbian) component of learning is used -- this is useful for setting a fully Hebbian learning connection, e.g., by setting MLrn = 0 and LLrn = 1.
	LLrn float32 `` /* 279-byte string literal not displayed */

	// [def: 0.1] [min: 0] [max: 0.99] proportional point within LTD range where magnitude reverses to go back down to zero at zero -- err-driven svm component does better with smaller values, and BCM-like mvl component does better with larger values -- 0.1 is a compromise
	DRev float32 `` /* 270-byte string literal not displayed */

	// [def: 0.0001,0.01] [min: 0] minimum LTD threshold value below which no weight change occurs -- this is now *relative* to the threshold
	DThr float32 `` /* 139-byte string literal not displayed */

	// [def: 0.01] xcal learning threshold -- don't learn when sending unit activation is below this value in both phases -- due to the nature of the learning function being 0 when the sr coproduct is 0, it should not affect learning in any substantial way -- nonstandard learning algorithms that have different properties should ignore it
	LrnThr float32 `` /* 338-byte string literal not displayed */

	// [view: -] -(1-DRev)/DRev -- multiplication factor in learning rule -- builds in the minus sign!
	DRevRatio float32 `` /* 131-byte string literal not displayed */
}