kinasex

README

KinasEx

This package contains experimental Kinase-based learning rules.

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.

In the initially-implemented nomenclature (early 2022), the space of algorithms was enumerated in kinase/rules.go as follows:

const (
	// SynSpkCont implements synaptic-level Ca signals at an abstract level,
	// purely driven by spikes, not NMDA channel Ca, as a product of
	// sender and recv CaSyn values that capture the decaying Ca trace
	// from spiking, qualitatively as in the NMDA dynamics.  These spike-driven
	// Ca signals are integrated in a cascaded manner via CaM,
	// then CaP (reflecting CaMKII) and finally CaD (reflecting DAPK1).
	// It uses continuous learning based on temporary DWt (TDWt) values
	// based on the TWindow around spikes, which convert into DWt after
	// a pause in synaptic activity (no arbitrary ThetaCycle boundaries).
	// There is an option to compare with SynSpkTheta by only doing DWt updates
	// at the theta cycle level, in which case the key difference is the use of
	// TDWt, which can remove some variability associated with the arbitrary
	// timing of the end of trials.
	SynSpkCont Rules = iota

	// SynNMDACont is the same as SynSpkCont with 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 the most biologically realistic yet computationally
	// tractable verseion of the Kinase learning algorithm.
	SynNMDACont

	// SynSpkTheta abstracts the SynSpkCont algorithm by only computing the
	// DWt change at the end of the ThetaCycle, instead of continuous updating.
	// This allows an optimized implementation that is roughly 1/3 slower than
	// the fastest NeurSpkTheta version, while still capturing much of the
	// learning dynamics by virtue of synaptic-level integration.
	SynSpkTheta

	// NeurSpkTheta 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.
	NeurSpkTheta
)

This package contains implementations of SynSpkCont and SynNMDACont.

Documentation

Index

• Variables
• type ContSyn
  • func (sy *ContSyn) VarByIndex(varIdx int) float32
  • func (sy *ContSyn) VarByName(varNm string) float32

Variables

var ContSynVars = []string{"TDWt", "CaDMax"}

Types

type ContSyn

type ContSyn struct {

	// transitional, temporary DWt value, which is updated in a window after synaptic activity when Ca levels are still elevated, and added to the DWt value after a longer break of spiking where there is enough time for CaMKII driven AMPA receptor trafficking to take place
	TDWt float32 `` /* 273-byte string literal not displayed */

	// maximum CaD value since last DWt change -- DWt occurs when current CaD has decreased by a given proportion from this recent peak
	CaDMax float32 `` /* 135-byte string literal not displayed */
}

ContSyn holds extra synaptic state for continuous learning

func (*ContSyn) VarByIndex

func (sy *ContSyn) VarByIndex(varIdx int) float32

VarByIndex returns synapse variable by index

func (*ContSyn) VarByName

func (sy *ContSyn) VarByName(varNm string) float32

VarByName returns synapse variable by name