GenericFroemkeDanStdpSynapse< BaseSyn > Class Template Reference

STDP due to Froemke and Dan Nature 416 (3/2002). More...

#include <GenericFroemkeDanStdpSynapse.h>

Inheritance diagram for GenericFroemkeDanStdpSynapse< BaseSyn >:

Collaboration diagram for GenericFroemkeDanStdpSynapse< BaseSyn >:

List of all members.

Public Member Functions

virtual double stdpLearning (const double &delta, const double &t_post, const double &t_pre, const double &t_prev_post, const double &t_prev_pre)

virtual double maxRelevantSpikeTimeDiff ()

Public Attributes

bool useFroemkeDanSTDP

activate extended rule by Froemke and Dan (default=1)

float tauspost

Used for extended rule by Froemke and Dan. See Froemke and Dan (2002). Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416 (3/2002).

float tauspre

Used for extended rule by Froemke and Dan.

float taupos

Timeconstant of exponential decay of positive learning window for STDP.

float tauneg

Timeconstant of exponential decay of negative learning window for STDP.

float STDPgap

No learning is performed if $|Delta| = |t_{post}-t_{pre}| < STDPgap$ .

float Wex

The maximal/minimal weight of the synapse [readwrite; units=;].

float Aneg

Defines the peak of the negative exponential learning window.

float Apos

Defines the peak of the positive exponential learning window.

float mupos

Extended multiplicative positive update: $dw = (Wex-W)^{mupos} * Apos * exp(-Delta/taupos)$ . Set to 0 for basic update. See Guetig, Aharonov, Rotter and Sompolinsky (2003). Learning input correlations through non-linear asymmetric Hebbian plasticity. Journal of Neuroscience 23. pp.3697-3714.

float muneg

Extended multiplicative negative update: $dw = W^{muneg} * Aneg * exp(Delta/tauneg)$ . Set to 0 for basic update.

Detailed Description

template<class BaseSyn>
class GenericFroemkeDanStdpSynapse< BaseSyn >

STDP due to Froemke and Dan Nature 416 (3/2002).

Implements the basic weight update for a time difference $Delta = t_{post}-t_{pre}$ with presynaptic spike at time $t_{pre}$ and postsynaptic spike at time $t_{post}$ . Then, the weight update is given by $dw = Apos * exp(-Delta/taupos)$ for $Delta > 0$ , and $dw = Aneg * exp(-Delta/tauneg)$ for $Delta < 0$ . (set $useFroemkeDanSTDP=0$ and $mupos=muneg=0$ for this basic update rule).

It is also possible to use an extended multiplicative update by changing mupos and muneg. Then $dw = (Wex-W)^{mupos} * Apos * exp(-Delta/taupos)$ for $Delta > 0$ and $dw = W^{mupos} * Aneg * exp(Delta/tauneg)$ for $Delta < 0$ . (see Guetig, Aharonov, Rotter and Sompolinsky (2003). Learning input correlations through non-linear asymmetric Hebbian plasticity. Journal of Neuroscience 23. pp.3697-3714.)

Set $useFroemkeDanSTDP=1$ (this is the default value) and use $tauspost$ and $tauspre$ for the rule given in Froemke and Dan (2002). Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416 (3/2002).

Definition at line 38 of file GenericFroemkeDanStdpSynapse.h.

Member Function Documentation

template<class BaseSyn>

double GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning	(	const double &	delta,
		const double &	t_post,
		const double &	t_pre,
		const double &	t_prev_post,
		const double &	t_prev_pre
	)			`[inline, virtual]`

Reimplemented in GenericDAModulatedSTDPSynapse< GenericFroemkeDanStdpSynapse< GenericEachPairStdpSynapse< GenericStaticSpikingSynapse< GenericCurrentBasedSpikingSynapse< ExponentialDecaySpikeResponse > > > > >, GenericDAModulatedSTDPSynapse< GenericFroemkeDanStdpSynapse< GenericEachPairStdpSynapse< GenericDynamicSpikingSynapse< GenericCurrentBasedSpikingSynapse< ExponentialDecaySpikeResponse > > > > >, GenericDAModulatedSTDPSynapse< GenericFroemkeDanStdpSynapse< GenericEachPairStdpSynapse< GenericDynamicSpikingSynapse< GenericConductanceBasedSpikingSynapse< ExponentialDecaySpikeResponse > > > > >, GenericDAModulatedSTDPSynapse< GenericFroemkeDanStdpSynapse< GenericEachPairStdpSynapse< GenericStaticSpikingSynapse< GenericConductanceBasedSpikingSynapse< ExponentialDecaySpikeResponse > > > > >, and GenericDAModulatedSTDPSynapse< GenericFroemkeDanStdpSynapse< GenericEachPairStdpSynapse< GenericStaticSpikingSynapse< GenericCurrentBasedSpikingSynapse< SquarePulseSpikeResponse > > > > >.

Definition at line 88 of file GenericFroemkeDanStdpSynapse.h.

template<class BaseSyn>

virtual double GenericFroemkeDanStdpSynapse< BaseSyn >::maxRelevantSpikeTimeDiff ( ) [inline, virtual]

Definition at line 76 of file GenericFroemkeDanStdpSynapse.h.

Member Data Documentation

template<class BaseSyn>

bool GenericFroemkeDanStdpSynapse< BaseSyn >::useFroemkeDanSTDP

activate extended rule by Froemke and Dan (default=1)

Definition at line 42 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::tauspost

Used for extended rule by Froemke and Dan. See Froemke and Dan (2002). Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416 (3/2002).

Definition at line 45 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::tauspre

Used for extended rule by Froemke and Dan.

Definition at line 48 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::taupos

Timeconstant of exponential decay of positive learning window for STDP.

Definition at line 51 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< GenericEachPairStdpSynapse< GenericStaticSpikingSynapse< GenericCurrentBasedSpikingSynapse< SquarePulseSpikeResponse > > > >::maxRelevantSpikeTimeDiff(), and GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::tauneg

Timeconstant of exponential decay of negative learning window for STDP.

Definition at line 54 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< GenericEachPairStdpSynapse< GenericStaticSpikingSynapse< GenericCurrentBasedSpikingSynapse< SquarePulseSpikeResponse > > > >::maxRelevantSpikeTimeDiff(), and GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::STDPgap

No learning is performed if $|Delta| = |t_{post}-t_{pre}| < STDPgap$ .

Definition at line 57 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::Wex

The maximal/minimal weight of the synapse [readwrite; units=;].

Definition at line 60 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::Aneg

Defines the peak of the negative exponential learning window.

Definition at line 63 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::Apos

Defines the peak of the positive exponential learning window.

Definition at line 66 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::mupos

Extended multiplicative positive update: $dw = (Wex-W)^{mupos} * Apos * exp(-Delta/taupos)$ . Set to 0 for basic update. See Guetig, Aharonov, Rotter and Sompolinsky (2003). Learning input correlations through non-linear asymmetric Hebbian plasticity. Journal of Neuroscience 23. pp.3697-3714.

Definition at line 69 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

template<class BaseSyn>

float GenericFroemkeDanStdpSynapse< BaseSyn >::muneg

Extended multiplicative negative update: $dw = W^{muneg} * Aneg * exp(Delta/tauneg)$ . Set to 0 for basic update.

Definition at line 72 of file GenericFroemkeDanStdpSynapse.h.

Referenced by GenericFroemkeDanStdpSynapse< BaseSyn >::stdpLearning().

The documentation for this class was generated from the following file:

/home/tnatschl/PCSIM-Sandbox/pcsim/pcsim/simobjects/GenericFroemkeDanStdpSynapse.h


Public Member Functions
virtual double	stdpLearning (const double &delta, const double &t_post, const double &t_pre, const double &t_prev_post, const double &t_prev_pre)
virtual double	maxRelevantSpikeTimeDiff ()
Public Attributes
bool	useFroemkeDanSTDP
	activate extended rule by Froemke and Dan (default=1)
float	tauspost
	Used for extended rule by Froemke and Dan. See Froemke and Dan (2002). Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416 (3/2002).
float	tauspre
	Used for extended rule by Froemke and Dan.
float	taupos
	Timeconstant of exponential decay of positive learning window for STDP.
float	tauneg
	Timeconstant of exponential decay of negative learning window for STDP.
float	STDPgap
	No learning is performed if $\|Delta\| = \|t_{post}-t_{pre}\| < STDPgap$ .
float	Wex
	The maximal/minimal weight of the synapse [readwrite; units=;].
float	Aneg
	Defines the peak of the negative exponential learning window.
float	Apos
	Defines the peak of the positive exponential learning window.
float	mupos
	Extended multiplicative positive update: $dw = (Wex-W)^{mupos} * Apos * exp(-Delta/taupos)$ . Set to 0 for basic update. See Guetig, Aharonov, Rotter and Sompolinsky (2003). Learning input correlations through non-linear asymmetric Hebbian plasticity. Journal of Neuroscience 23. pp.3697-3714.
float	muneg
	Extended multiplicative negative update: $dw = W^{muneg} * Aneg * exp(Delta/tauneg)$ . Set to 0 for basic update.

GenericFroemkeDanStdpSynapse< BaseSyn > Class Template Reference

Public Member Functions

Public Attributes

Detailed Description

template<class BaseSyn> class GenericFroemkeDanStdpSynapse< BaseSyn >

Member Function Documentation

Member Data Documentation

template<class BaseSyn>
class GenericFroemkeDanStdpSynapse< BaseSyn >