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README
¶
Kinase Learning Implementation
This implements the Kinase learning rule, with multiple different variants being explored.
See https://github.com/emer/axon/tree/master/examples/kinaseq for exploration of the implemented equations, and https://github.com/ccnlab/kinase/tree/main/sims/kinase for biophysical basis of the equations.
Documentation
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Index ¶
- Variables
- type CaParams
- func (kp *CaParams) CurCa(ctime, utime int32, caM, caP, caD float32) (cCaM, cCaP, cCaD float32)
- func (kp *CaParams) Defaults()
- func (kp *CaParams) FmCa(ca float32, caM, caP, caD *float32)
- func (kp *CaParams) FmSpike(spike float32, caM, caP, caD *float32)
- func (kp *CaParams) IntFmTime(ctime, utime int32) int
- func (kp *CaParams) SynNMDACa(snmdao, rca float32) float32
- func (kp *CaParams) Update()
- type DWtParams
- type Rules
Constants ¶
This section is empty.
Variables ¶
var KiT_Rules = kit.Enums.AddEnum(RulesN, kit.NotBitFlag, nil)
Functions ¶
This section is empty.
Types ¶
type CaParams ¶ added in v1.3.18
type CaParams struct {
Rule Rules `desc:"which form of synaptic calcium and subsequent learning rule to use"`
SpikeG float32 `` /* 364-byte string literal not displayed */
MTau float32 `` /* 203-byte string literal not displayed */
PTau float32 `` /* 314-byte string literal not displayed */
DTau float32 `` /* 299-byte string literal not displayed */
UpdtThr float32 `` /* 183-byte string literal not displayed */
OptInteg bool `` /* 198-byte string literal not displayed */
MaxISI int `def:"100" desc:"maximum ISI for integrating in Opt mode -- above that just set to 0"`
MDt float32 `view:"-" json:"-" xml:"-" inactive:"+" desc:"rate = 1 / tau"`
PDt float32 `view:"-" json:"-" xml:"-" inactive:"+" desc:"rate = 1 / tau"`
DDt float32 `view:"-" json:"-" xml:"-" inactive:"+" desc:"rate = 1 / tau"`
}
CaParams has rate constants for integrating spike-driven Ca calcium at different time scales, including final CaP = CaMKII and CaD = DAPK1 timescales for LTP potentiation vs. LTD depression factors.
func (*CaParams) CurCa ¶ added in v1.3.18
CurCa returns the current Ca* values, dealing with updating for optimized spike-time update versions. ctime is current time in msec, and utime is last update time (-1 if never)
func (*CaParams) FmCa ¶ added in v1.3.18
FmCa computes updates to CaM, CaP, CaD from current calcium level. The SpikeG factor is NOT applied to Ca and should be pre-applied as appropriate.
func (*CaParams) FmSpike ¶ added in v1.3.18
FmSpike computes updates to CaM, CaP, CaD from current spike value. The SpikeG factor determines strength of increase to CaM.
func (*CaParams) IntFmTime ¶ added in v1.3.18
IntFmTime returns the interval from current time and last update time, which is -1 if never updated (in which case return is -1)
type DWtParams ¶ added in v1.3.18
type DWtParams struct {
TDWtISI int `` /* 209-byte string literal not displayed */
CaDMaxThr float32 `desc:"threshold on CaDMax peak level for whether the synapse had enough activity to drive learning"`
CaDMaxPct float32 `` /* 272-byte string literal not displayed */
TrlDecay float32 `` /* 247-byte string literal not displayed */
DScale float32 `` /* 186-byte string literal not displayed */
}
DWtParams has parameters controlling Kinase-based learning rules
type Rules ¶
type Rules int32
Rules are different options for Kinase-based learning rules
const ( // NeurSpkCa uses neuron-level spike-driven calcium signals // integrated at P vs. D time scales -- this is the original // Leabra and Axon XCAL / CHL learning rule. // It exhibits strong sensitivity to final spikes and thus // high levels of variance. NeurSpkCa Rules = iota // SynSpkCa integrates synapse-level spike-driven calcium signals // starting with a product of pre and post CaM values at the point // of either spike (using neuron level SpkCa params), // which is then integrated at P vs. D time scales. // Basically a synapse version of original learning rule. SynSpkCa // SynNMDACa uses synapse-level NMDA-driven calcium signals // computed according to the very close approximation to the // Urakubo et al (2008) allosteric NMDA dynamics, then // integrated at P vs. D time scales. // This is an abstract version of a biologically realistic model, // very close in many details to a fully biophysically-grounded one. SynNMDACa RulesN )
The time scales
Source Files