#include <modelSpec.h>

Public Types
typedef map< string, NeuronGroup >::value_type	NeuronGroupValueType

typedef map< string, SynapseGroup >::value_type	SynapseGroupValueType

typedef map< string, std::pair< unsigned int, unsigned int > >::value_type	SynapseGroupSubsetValueType

Public Member Functions
	NNmodel ()

	~NNmodel ()

void	setName (const std::string &)
	Method to set the neuronal network model name. More...

void	setPrecision (FloatType)
	Set numerical precision for floating point. More...

void	setTimePrecision (TimePrecision timePrecision)
	Set numerical precision for time. More...

void	setDT (double)
	Set the integration step size of the model. More...

void	setTiming (bool)
	Set whether timers and timing commands are to be included. More...

void	setSeed (unsigned int)
	Set the random seed (disables automatic seeding if argument not 0). More...

void	setRNType (const std::string &type)
	Sets the underlying type for random number generation (default: uint64_t) More...

void	setGPUDevice (int)
	Sets the underlying type for random number generation (default: uint64_t) More...

string	scalarExpr (const double) const
	Get the string literal that should be used to represent a value in the model's floating-point type. More...

void	setPopulationSums ()
	Set the accumulated sums of lowest multiple of kernel block size >= group sizes for all simulated groups. More...

void	finalize ()
	Declare that the model specification is finalised in modelDefinition(). More...

bool	zeroCopyInUse () const
	Are any variables in any populations in this model using zero-copy memory? More...

unsigned int	getNumPreSynapseResetRequiredGroups () const
	Return number of synapse groups which require a presynaptic reset kernel to be run. More...

bool	isPreSynapseResetRequired () const
	Is there reset logic to be run before the synapse kernel i.e. for dendritic delays. More...

bool	isHostRNGRequired () const
	Do any populations or initialisation code in this model require a host RNG? More...

bool	isDeviceRNGRequired () const
	Do any populations or initialisation code in this model require a device RNG? More...

bool	canRunOnCPU () const
	Can this model run on the CPU? More...

const std::string &	getName () const
	Gets the name of the neuronal network model. More...

const std::string &	getPrecision () const
	Gets the floating point numerical precision. More...

std::string	getTimePrecision () const
	Gets the floating point numerical precision used to represent time. More...

unsigned int	getResetKernel () const
	Which kernel should contain the reset logic? Specified in terms of GENN_FLAGS. More...

double	getDT () const
	Gets the model integration step size. More...

unsigned int	getSeed () const
	Get the random seed. More...

const std::string &	getRNType () const
	Gets the underlying type for random number generation (default: uint64_t) More...

bool	isFinalized () const
	Is the model specification finalized. More...

bool	isTimingEnabled () const
	Are timers and timing commands enabled. More...

std::string	getGeneratedCodePath (const std::string &path, const std::string &filename) const
	Generate path for generated code. More...

const map< string, string > &	getInitKernelParameters () const

bool	isDeviceInitRequired (int localHostID) const
	Does this model require device initialisation kernel. More...

bool	isDeviceSparseInitRequired () const
	Does this model require a device sparse initialisation kernel. More...

const map< string, NeuronGroup > &	getLocalNeuronGroups () const
	Get std::map containing local named NeuronGroup objects in model. More...

const map< string, NeuronGroup > &	getRemoteNeuronGroups () const
	Get std::map containing remote named NeuronGroup objects in model. More...

const map< string, string > &	getNeuronKernelParameters () const
	Gets std::map containing names and types of each parameter that should be passed through to the neuron kernel. More...

unsigned int	getNeuronGridSize () const
	Gets the size of the neuron kernel thread grid. More...

unsigned int	getNumLocalNeurons () const
	How many neurons are simulated locally in this model. More...

unsigned int	getNumRemoteNeurons () const
	How many neurons are simulated remotely in this model. More...

unsigned int	getNumNeurons () const
	How many neurons make up the entire model. More...

const NeuronGroup *	findNeuronGroup (const std::string &name) const
	Find a neuron group by name. More...

NeuronGroup *	findNeuronGroup (const std::string &name)
	Find a neuron group by name. More...

NeuronGroup *	addNeuronPopulation (const string &, unsigned int, unsigned int, const double , const double , int hostID=0, int deviceID=0)
	Method for adding a neuron population to a neuronal network model, using C++ string for the name of the population. More...

NeuronGroup *	addNeuronPopulation (const string &, unsigned int, unsigned int, const vector< double > &, const vector< double > &, int hostID=0, int deviceID=0)
	Method for adding a neuron population to a neuronal network model, using C++ string for the name of the population. More...

template<typename NeuronModel >
NeuronGroup *	addNeuronPopulation (const string &name, unsigned int size, const NeuronModel *model, const typename NeuronModel::ParamValues &paramValues, const typename NeuronModel::VarValues &varInitialisers, int hostID=0, int deviceID=0)
	Adds a new neuron group to the model using a neuron model managed by the user. More...

template<typename NeuronModel >
NeuronGroup *	addNeuronPopulation (const string &name, unsigned int size, const typename NeuronModel::ParamValues &paramValues, const typename NeuronModel::VarValues &varInitialisers, int hostID=0, int deviceID=0)
	Adds a new neuron group to the model using a singleton neuron model created using standard DECLARE_MODEL and IMPLEMENT_MODEL macros. More...

void	setNeuronClusterIndex (const string &neuronGroup, int hostID, int deviceID)
	This function has been deprecated in GeNN 3.1.0. More...

void	activateDirectInput (const string &, unsigned int type)
	This function defines the type of the explicit input to the neuron model. Current options are common constant input to all neurons, input from a file and input defines as a rule. More...

void	setConstInp (const string &, double)
	This function has been deprecated in GeNN 2.2. More...

const map< string, SynapseGroup > &	getLocalSynapseGroups () const
	Get std::map containing local named SynapseGroup objects in model. More...

const map< string, SynapseGroup > &	getRemoteSynapseGroups () const
	Get std::map containing remote named SynapseGroup objects in model. More...

const map< string, std::pair< unsigned int, unsigned int > > &	getSynapsePostLearnGroups () const

const map< string, std::pair< unsigned int, unsigned int > > &	getSynapseDynamicsGroups () const

const map< string, string > &	getSynapseKernelParameters () const
	Gets std::map containing names and types of each parameter that should be passed through to the synapse kernel. More...

const map< string, string > &	getSimLearnPostKernelParameters () const
	Gets std::map containing names and types of each parameter that should be passed through to the postsynaptic learning kernel. More...

const map< string, string > &	getSynapseDynamicsKernelParameters () const
	Gets std::map containing names and types of each parameter that should be passed through to the synapse dynamics kernel. More...

unsigned int	getSynapseKernelGridSize () const
	Gets the size of the synapse kernel thread grid. More...

unsigned int	getSynapsePostLearnGridSize () const
	Gets the size of the post-synaptic learning kernel thread grid. More...

unsigned int	getSynapseDynamicsGridSize () const
	Gets the size of the synapse dynamics kernel thread grid. More...

const SynapseGroup *	findSynapseGroup (const std::string &name) const
	Find a synapse group by name. More...

SynapseGroup *	findSynapseGroup (const std::string &name)
	Find a synapse group by name. More...

bool	isSynapseGroupDynamicsRequired (const std::string &name) const
	Does named synapse group have synapse dynamics. More...

bool	isSynapseGroupPostLearningRequired (const std::string &name) const
	Does named synapse group have post-synaptic learning. More...

SynapseGroup *	addSynapsePopulation (const string &name, unsigned int syntype, SynapseConnType conntype, SynapseGType gtype, const string &src, const string &trg, const double *p)
	This function has been depreciated as of GeNN 2.2. More...

SynapseGroup *	addSynapsePopulation (const string &, unsigned int, SynapseConnType, SynapseGType, unsigned int, unsigned int, const string &, const string &, const double , const double , const double *)
	Overloaded version without initial variables for synapses. More...

SynapseGroup *	addSynapsePopulation (const string &, unsigned int, SynapseConnType, SynapseGType, unsigned int, unsigned int, const string &, const string &, const double , const double , const double , const double )
	Method for adding a synapse population to a neuronal network model, using C++ string for the name of the population. More...

SynapseGroup *	addSynapsePopulation (const string &, unsigned int, SynapseConnType, SynapseGType, unsigned int, unsigned int, const string &, const string &, const vector< double > &, const vector< double > &, const vector< double > &, const vector< double > &)
	Method for adding a synapse population to a neuronal network model, using C++ string for the name of the population. More...

template<typename WeightUpdateModel , typename PostsynapticModel >
SynapseGroup *	addSynapsePopulation (const string &name, SynapseMatrixType mtype, unsigned int delaySteps, const string &src, const string &trg, const WeightUpdateModel wum, const typename WeightUpdateModel::ParamValues &weightParamValues, const typename WeightUpdateModel::VarValues &weightVarInitialisers, const typename WeightUpdateModel::PreVarValues &weightPreVarInitialisers, const typename WeightUpdateModel::PostVarValues &weightPostVarInitialisers, const PostsynapticModel psm, const typename PostsynapticModel::ParamValues &postsynapticParamValues, const typename PostsynapticModel::VarValues &postsynapticVarInitialisers, const InitSparseConnectivitySnippet::Init &connectivityInitialiser=uninitialisedConnectivity())
	Adds a synapse population to the model using weight update and postsynaptic models managed by the user. More...

template<typename WeightUpdateModel , typename PostsynapticModel >
SynapseGroup *	addSynapsePopulation (const string &name, SynapseMatrixType mtype, unsigned int delaySteps, const string &src, const string &trg, const typename WeightUpdateModel::ParamValues &weightParamValues, const typename WeightUpdateModel::VarValues &weightVarInitialisers, const typename PostsynapticModel::ParamValues &postsynapticParamValues, const typename PostsynapticModel::VarValues &postsynapticVarInitialisers, const InitSparseConnectivitySnippet::Init &connectivityInitialiser=uninitialisedConnectivity())
	Adds a synapse population to the model using singleton weight update and postsynaptic models created using standard DECLARE_MODEL and IMPLEMENT_MODEL macros. More...

template<typename WeightUpdateModel , typename PostsynapticModel >
SynapseGroup *	addSynapsePopulation (const string &name, SynapseMatrixType mtype, unsigned int delaySteps, const string &src, const string &trg, const typename WeightUpdateModel::ParamValues &weightParamValues, const typename WeightUpdateModel::VarValues &weightVarInitialisers, const typename WeightUpdateModel::PreVarValues &weightPreVarInitialisers, const typename WeightUpdateModel::PostVarValues &weightPostVarInitialisers, const typename PostsynapticModel::ParamValues &postsynapticParamValues, const typename PostsynapticModel::VarValues &postsynapticVarInitialisers, const InitSparseConnectivitySnippet::Init &connectivityInitialiser=uninitialisedConnectivity())
	Adds a synapse population to the model using singleton weight update and postsynaptic models created using standard DECLARE_MODEL and IMPLEMENT_MODEL macros. More...

void	setSynapseG (const string &, double)
	This function has been depreciated as of GeNN 2.2. More...

void	setMaxConn (const string &, unsigned int)
	This function defines the maximum number of connections for a neuron in the population. More...

void	setSpanTypeToPre (const string &)
	Method for switching the execution order of synapses to pre-to-post. More...

const map< string, CurrentSource > &	getLocalCurrentSources () const
	Get std::map containing local named CurrentSource objects in model. More...

const map< string, CurrentSource > &	getRemoteCurrentSources () const
	Get std::map containing remote named CurrentSource objects in model. More...

const map< string, string > &	getCurrentSourceKernelParameters () const
	Gets std::map containing names and types of each parameter that should be passed through to the current source kernel. More...

const CurrentSource *	findCurrentSource (const std::string &name) const
	Find a current source by name. More...

CurrentSource *	findCurrentSource (const std::string &name)
	Find a current source by name. More...

template<typename CurrentSourceModel >
CurrentSource *	addCurrentSource (const string &currentSourceName, const CurrentSourceModel *model, const string &targetNeuronGroupName, const typename CurrentSourceModel::ParamValues &paramValues, const typename CurrentSourceModel::VarValues &varInitialisers)
	Adds a new current source to the model using a current source model managed by the user. More...

template<typename CurrentSourceModel >
CurrentSource *	addCurrentSource (const string &currentSourceName, const string &targetNeuronGroupName, const typename CurrentSourceModel::ParamValues &paramValues, const typename CurrentSourceModel::VarValues &varInitialisers)
	Adds a new current source to the model using a singleton current source model created using standard DECLARE_MODEL and IMPLEMENT_MODEL macros. More...

Member Typedef Documentation

◆ NeuronGroupValueType

typedef map<string, NeuronGroup>::value_type NNmodel::NeuronGroupValueType

◆ SynapseGroupSubsetValueType

typedef map<string, std::pair<unsigned int, unsigned int> >::value_type NNmodel::SynapseGroupSubsetValueType

◆ SynapseGroupValueType

typedef map<string, SynapseGroup>::value_type NNmodel::SynapseGroupValueType

Constructor & Destructor Documentation

◆ NNmodel()

NNmodel::NNmodel ( )

◆ ~NNmodel()

NNmodel::~NNmodel ( )

Member Function Documentation

◆ activateDirectInput()

void NNmodel::activateDirectInput	(	const string &	,
		unsigned int	type
	)

This function defines the type of the explicit input to the neuron model. Current options are common constant input to all neurons, input from a file and input defines as a rule.

Parameters

type	Type of input: 1 if common input, 2 if custom input from file, 3 if custom input as a rule

◆ addCurrentSource() [1/2]

template<typename CurrentSourceModel >

CurrentSource* NNmodel::addCurrentSource	(	const string &	currentSourceName,
		const CurrentSourceModel *	model,
		const string &	targetNeuronGroupName,
		const typename CurrentSourceModel::ParamValues &	paramValues,
		const typename CurrentSourceModel::VarValues &	varInitialisers
	)

inline

Adds a new current source to the model using a current source model managed by the user.

Template Parameters

CurrentSourceModel type of current source model (derived from CurrentSourceModels::Base).

Parameters

name	string containing unique name of current source.
model	current source model to use for current source.
targetNeuronGroupName	string name of the target neuron group
paramValues	parameters for model wrapped in CurrentSourceModel::ParamValues object.
varInitialisers	state variable initialiser snippets and parameters wrapped in CurrentSource::VarValues object.

Returns: pointer to newly created CurrentSource

◆ addCurrentSource() [2/2]

template<typename CurrentSourceModel >

CurrentSource* NNmodel::addCurrentSource	(	const string &	currentSourceName,
		const string &	targetNeuronGroupName,
		const typename CurrentSourceModel::ParamValues &	paramValues,
		const typename CurrentSourceModel::VarValues &	varInitialisers
	)

inline

Adds a new current source to the model using a singleton current source model created using standard DECLARE_MODEL and IMPLEMENT_MODEL macros.

Template Parameters

CurrentSourceModel type of neuron model (derived from CurrentSourceModel::Base).

Parameters

currentSourceName	string containing unique name of current source.
targetNeuronGroupName	string name of the target neuron group
paramValues	parameters for model wrapped in CurrentSourceModel::ParamValues object.
varInitialisers	state variable initialiser snippets and parameters wrapped in CurrentSourceModel::VarValues object.

Returns: pointer to newly created CurrentSource

◆ addNeuronPopulation() [1/4]

NeuronGroup * NNmodel::addNeuronPopulation	(	const string &	name,
		unsigned int	nNo,
		unsigned int	type,
		const double *	p,
		const double *	ini,
		int	hostID = `0`,
		int	deviceID = `0`
	)

Method for adding a neuron population to a neuronal network model, using C++ string for the name of the population.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

This function adds a neuron population to a neuronal network models, assigning the name, the number of neurons in the group, the neuron type, parameters and initial values, the latter two defined as double *

Parameters

name	The name of the neuron population
nNo	Number of neurons in the population
type	Type of the neurons, refers to either a standard type or user-defined type
p	Parameters of this neuron type
ini	Initial values for variables of this neuron type
hostID	host ID for neuron group

◆ addNeuronPopulation() [2/4]

NeuronGroup * NNmodel::addNeuronPopulation	(	const string &	name,
		unsigned int	nNo,
		unsigned int	type,
		const vector< double > &	p,
		const vector< double > &	ini,
		int	hostID = `0`,
		int	deviceID = `0`
	)

Method for adding a neuron population to a neuronal network model, using C++ string for the name of the population.

This function adds a neuron population to a neuronal network models, assigning the name, the number of neurons in the group, the neuron type, parameters and initial values. The latter two defined as STL vectors of double.

Parameters

name	The name of the neuron population
nNo	Number of neurons in the population
type	Type of the neurons, refers to either a standard type or user-defined type
p	Parameters of this neuron type
ini	Initial values for variables of this neuron type

◆ addNeuronPopulation() [3/4]

template<typename NeuronModel >

NeuronGroup* NNmodel::addNeuronPopulation	(	const string &	name,
		unsigned int	size,
		const NeuronModel *	model,
		const typename NeuronModel::ParamValues &	paramValues,
		const typename NeuronModel::VarValues &	varInitialisers,
		int	hostID = `0`,
		int	deviceID = `0`
	)

inline

Adds a new neuron group to the model using a neuron model managed by the user.

Template Parameters

NeuronModel type of neuron model (derived from NeuronModels::Base).

Parameters

name	string containing unique name of neuron population.
size	integer specifying how many neurons are in the population.
model	neuron model to use for neuron group.
paramValues	parameters for model wrapped in NeuronModel::ParamValues object.
varInitialisers	state variable initialiser snippets and parameters wrapped in NeuronModel::VarValues object.

Returns: pointer to newly created NeuronGroup

◆ addNeuronPopulation() [4/4]

template<typename NeuronModel >

NeuronGroup* NNmodel::addNeuronPopulation	(	const string &	name,
		unsigned int	size,
		const typename NeuronModel::ParamValues &	paramValues,
		const typename NeuronModel::VarValues &	varInitialisers,
		int	hostID = `0`,
		int	deviceID = `0`
	)

inline

Adds a new neuron group to the model using a singleton neuron model created using standard DECLARE_MODEL and IMPLEMENT_MODEL macros.

Template Parameters

NeuronModel type of neuron model (derived from NeuronModels::Base).

Parameters

name	string containing unique name of neuron population.
size	integer specifying how many neurons are in the population.
paramValues	parameters for model wrapped in NeuronModel::ParamValues object.
varInitialisers	state variable initialiser snippets and parameters wrapped in NeuronModel::VarValues object.

Returns: pointer to newly created NeuronGroup

◆ addSynapsePopulation() [1/7]

SynapseGroup * NNmodel::addSynapsePopulation	(	const string &	name,
		unsigned int	syntype,
		SynapseConnType	conntype,
		SynapseGType	gtype,
		const string &	src,
		const string &	trg,
		const double *	p
	)

This function has been depreciated as of GeNN 2.2.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

This deprecated function is provided for compatibility with the previous release of GeNN. Default values are provide for new parameters, it is strongly recommended these be selected explicity via the new version othe function

Parameters

name	The name of the synapse population
syntype	The type of synapse to be added (i.e. learning mode)
conntype	The type of synaptic connectivity
gtype	The way how the synaptic conductivity g will be defined
src	Name of the (existing!) pre-synaptic neuron population
trg	Name of the (existing!) post-synaptic neuron population
p	A C-type array of doubles that contains synapse parameter values (common to all synapses of the population) which will be used for the defined synapses.

◆ addSynapsePopulation() [2/7]

SynapseGroup * NNmodel::addSynapsePopulation	(	const string &	name,
		unsigned int	syntype,
		SynapseConnType	conntype,
		SynapseGType	gtype,
		unsigned int	delaySteps,
		unsigned int	postsyn,
		const string &	src,
		const string &	trg,
		const double *	p,
		const double *	PSVini,
		const double *	ps
	)

Overloaded version without initial variables for synapses.

Overloaded old version (deprecated)

Parameters

name	The name of the synapse population
syntype	The type of synapse to be added (i.e. learning mode)
conntype	The type of synaptic connectivity
gtype	The way how the synaptic conductivity g will be defined
delaySteps	Number of delay slots
postsyn	Postsynaptic integration method
src	Name of the (existing!) pre-synaptic neuron population
trg	Name of the (existing!) post-synaptic neuron population
p	A C-type array of doubles that contains synapse parameter values (common to all synapses of the population) which will be used for the defined synapses.
PSVini	A C-type array of doubles that contains the initial values for postsynaptic mechanism variables (common to all synapses of the population) which will be used for the defined synapses.
ps	A C-type array of doubles that contains postsynaptic mechanism parameter values (common to all synapses of the population) which will be used for the defined synapses.

◆ addSynapsePopulation() [3/7]

SynapseGroup * NNmodel::addSynapsePopulation	(	const string &	name,
		unsigned int	syntype,
		SynapseConnType	conntype,
		SynapseGType	gtype,
		unsigned int	delaySteps,
		unsigned int	postsyn,
		const string &	src,
		const string &	trg,
		const double *	synini,
		const double *	p,
		const double *	PSVini,
		const double *	ps
	)

Method for adding a synapse population to a neuronal network model, using C++ string for the name of the population.

This function adds a synapse population to a neuronal network model, assigning the name, the synapse type, the connectivity type, the type of conductance specification, the source and destination neuron populations, and the synaptic parameters.

Parameters

name	The name of the synapse population
syntype	The type of synapse to be added (i.e. learning mode)
conntype	The type of synaptic connectivity
gtype	The way how the synaptic conductivity g will be defined
delaySteps	Number of delay slots
postsyn	Postsynaptic integration method
src	Name of the (existing!) pre-synaptic neuron population
trg	Name of the (existing!) post-synaptic neuron population
synini	A C-type array of doubles that contains the initial values for synapse variables (common to all synapses of the population) which will be used for the defined synapses.
p	A C-type array of doubles that contains synapse parameter values (common to all synapses of the population) which will be used for the defined synapses.
PSVini	A C-type array of doubles that contains the initial values for postsynaptic mechanism variables (common to all synapses of the population) which will be used for the defined synapses.
ps	A C-type array of doubles that contains postsynaptic mechanism parameter values (common to all synapses of the population) which will be used for the defined synapses.

◆ addSynapsePopulation() [4/7]

SynapseGroup * NNmodel::addSynapsePopulation	(	const string &	name,
		unsigned int	syntype,
		SynapseConnType	conntype,
		SynapseGType	gtype,
		unsigned int	delaySteps,
		unsigned int	postsyn,
		const string &	src,
		const string &	trg,
		const vector< double > &	synini,
		const vector< double > &	p,
		const vector< double > &	PSVini,
		const vector< double > &	ps
	)

Method for adding a synapse population to a neuronal network model, using C++ string for the name of the population.

This function adds a synapse population to a neuronal network model, assigning the name, the synapse type, the connectivity type, the type of conductance specification, the source and destination neuron populations, and the synaptic parameters.

Parameters

name	The name of the synapse population
syntype	The type of synapse to be added (i.e. learning mode)
conntype	The type of synaptic connectivity
gtype	The way how the synaptic conductivity g will be defined
delaySteps	Number of delay slots
postsyn	Postsynaptic integration method
src	Name of the (existing!) pre-synaptic neuron population
trg	Name of the (existing!) post-synaptic neuron population
synini	A C-type array of doubles that contains the initial values for synapse variables (common to all synapses of the population) which will be used for the defined synapses.
p	A C-type array of doubles that contains synapse parameter values (common to all synapses of the population) which will be used for the defined synapses.
PSVini	A C-type array of doubles that contains the initial values for postsynaptic mechanism variables (common to all synapses of the population) which will be used for the defined synapses.
ps	A C-type array of doubles that contains postsynaptic mechanism parameter values (common to all synapses of the population) which will be used for the defined synapses.

◆ addSynapsePopulation() [5/7]

template<typename WeightUpdateModel , typename PostsynapticModel >

SynapseGroup* NNmodel::addSynapsePopulation	(	const string &	name,
		SynapseMatrixType	mtype,
		unsigned int	delaySteps,
		const string &	src,
		const string &	trg,
		const WeightUpdateModel *	wum,
		const typename WeightUpdateModel::ParamValues &	weightParamValues,
		const typename WeightUpdateModel::VarValues &	weightVarInitialisers,
		const typename WeightUpdateModel::PreVarValues &	weightPreVarInitialisers,
		const typename WeightUpdateModel::PostVarValues &	weightPostVarInitialisers,
		const PostsynapticModel *	psm,
		const typename PostsynapticModel::ParamValues &	postsynapticParamValues,
		const typename PostsynapticModel::VarValues &	postsynapticVarInitialisers,
		const InitSparseConnectivitySnippet::Init &	connectivityInitialiser = `uninitialisedConnectivity()`
	)

inline

Adds a synapse population to the model using weight update and postsynaptic models managed by the user.

Template Parameters

WeightUpdateModel	type of weight update model (derived from WeightUpdateModels::Base).
PostsynapticModel	type of postsynaptic model (derived from PostsynapticModels::Base).

Parameters

name	string containing unique name of neuron population.
mtype	how the synaptic matrix associated with this synapse population should be represented.
delaySteps	integer specifying number of timesteps delay this synaptic connection should incur (or NO_DELAY for none)
src	string specifying name of presynaptic (source) population
trg	string specifying name of postsynaptic (target) population
wum	weight update model to use for synapse group.
weightParamValues	parameters for weight update model wrapped in WeightUpdateModel::ParamValues object.
weightVarInitialisers	weight update model state variable initialiser snippets and parameters wrapped in WeightUpdateModel::VarValues object.
weightPreVarInitialisers	weight update model presynaptic state variable initialiser snippets and parameters wrapped in WeightUpdateModel::VarValues object.
weightPostVarInitialisers	weight update model postsynaptic state variable initialiser snippets and parameters wrapped in WeightUpdateModel::VarValues object.
psm	postsynaptic model to use for synapse group.
postsynapticParamValues	parameters for postsynaptic model wrapped in PostsynapticModel::ParamValues object.
postsynapticVarInitialisers	postsynaptic model state variable initialiser snippets and parameters wrapped in NeuronModel::VarValues object.

Returns: pointer to newly created SynapseGroup

◆ addSynapsePopulation() [6/7]

template<typename WeightUpdateModel , typename PostsynapticModel >

SynapseGroup* NNmodel::addSynapsePopulation	(	const string &	name,
		SynapseMatrixType	mtype,
		unsigned int	delaySteps,
		const string &	src,
		const string &	trg,
		const typename WeightUpdateModel::ParamValues &	weightParamValues,
		const typename WeightUpdateModel::VarValues &	weightVarInitialisers,
		const typename PostsynapticModel::ParamValues &	postsynapticParamValues,
		const typename PostsynapticModel::VarValues &	postsynapticVarInitialisers,
		const InitSparseConnectivitySnippet::Init &	connectivityInitialiser = `uninitialisedConnectivity()`
	)

inline

Adds a synapse population to the model using singleton weight update and postsynaptic models created using standard DECLARE_MODEL and IMPLEMENT_MODEL macros.

Template Parameters

WeightUpdateModel	type of weight update model (derived from WeightUpdateModels::Base).
PostsynapticModel	type of postsynaptic model (derived from PostsynapticModels::Base).

Parameters

name	string containing unique name of neuron population.
mtype	how the synaptic matrix associated with this synapse population should be represented.
delaySteps	integer specifying number of timesteps delay this synaptic connection should incur (or NO_DELAY for none)
src	string specifying name of presynaptic (source) population
trg	string specifying name of postsynaptic (target) population
weightParamValues	parameters for weight update model wrapped in WeightUpdateModel::ParamValues object.
weightVarInitialisers	weight update model state variable initialiser snippets and parameters wrapped in WeightUpdateModel::VarValues object.
postsynapticParamValues	parameters for postsynaptic model wrapped in PostsynapticModel::ParamValues object.
postsynapticVarInitialisers	postsynaptic model state variable initialiser snippets and parameters wrapped in NeuronModel::VarValues object.

Returns: pointer to newly created SynapseGroup

◆ addSynapsePopulation() [7/7]

template<typename WeightUpdateModel , typename PostsynapticModel >

SynapseGroup* NNmodel::addSynapsePopulation	(	const string &	name,
		SynapseMatrixType	mtype,
		unsigned int	delaySteps,
		const string &	src,
		const string &	trg,
		const typename WeightUpdateModel::ParamValues &	weightParamValues,
		const typename WeightUpdateModel::VarValues &	weightVarInitialisers,
		const typename WeightUpdateModel::PreVarValues &	weightPreVarInitialisers,
		const typename WeightUpdateModel::PostVarValues &	weightPostVarInitialisers,
		const typename PostsynapticModel::ParamValues &	postsynapticParamValues,
		const typename PostsynapticModel::VarValues &	postsynapticVarInitialisers,
		const InitSparseConnectivitySnippet::Init &	connectivityInitialiser = `uninitialisedConnectivity()`
	)

inline

Adds a synapse population to the model using singleton weight update and postsynaptic models created using standard DECLARE_MODEL and IMPLEMENT_MODEL macros.

Template Parameters

WeightUpdateModel	type of weight update model (derived from WeightUpdateModels::Base).
PostsynapticModel	type of postsynaptic model (derived from PostsynapticModels::Base).

Parameters

name	string containing unique name of neuron population.
mtype	how the synaptic matrix associated with this synapse population should be represented.
delaySteps	integer specifying number of timesteps delay this synaptic connection should incur (or NO_DELAY for none)
src	string specifying name of presynaptic (source) population
trg	string specifying name of postsynaptic (target) population
weightParamValues	parameters for weight update model wrapped in WeightUpdateModel::ParamValues object.
weightVarInitialisers	weight update model per-synapse state variable initialiser snippets and parameters wrapped in WeightUpdateModel::VarValues object.
weightPreVarInitialisers	weight update model presynaptic state variable initialiser snippets and parameters wrapped in WeightUpdateModel::VarValues object.
weightPostVarInitialisers	weight update model postsynaptic state variable initialiser snippets and parameters wrapped in WeightUpdateModel::VarValues object.
postsynapticParamValues	parameters for postsynaptic model wrapped in PostsynapticModel::ParamValues object.
postsynapticVarInitialisers	postsynaptic model state variable initialiser snippets and parameters wrapped in NeuronModel::VarValues object.

Returns: pointer to newly created SynapseGroup

◆ canRunOnCPU()

bool NNmodel::canRunOnCPU ( ) const

Can this model run on the CPU?

If we are running in CPU_ONLY mode this is always true, but some GPU functionality will prevent models being run on both CPU and GPU.

◆ finalize()

void NNmodel::finalize ( )

Declare that the model specification is finalised in modelDefinition().

◆ findCurrentSource() [1/2]

const CurrentSource * NNmodel::findCurrentSource ( const std::string & name ) const

Find a current source by name.

This function attempts to find an existing current source.

◆ findCurrentSource() [2/2]

CurrentSource * NNmodel::findCurrentSource ( const std::string & name )

Find a current source by name.

◆ findNeuronGroup() [1/2]

const NeuronGroup * NNmodel::findNeuronGroup ( const std::string & name ) const

Find a neuron group by name.

◆ findNeuronGroup() [2/2]

NeuronGroup * NNmodel::findNeuronGroup ( const std::string & name )

Find a neuron group by name.

◆ findSynapseGroup() [1/2]

const SynapseGroup * NNmodel::findSynapseGroup ( const std::string & name ) const

Find a synapse group by name.

◆ findSynapseGroup() [2/2]

SynapseGroup * NNmodel::findSynapseGroup ( const std::string & name )

Find a synapse group by name.

◆ getCurrentSourceKernelParameters()

const map<string, string>& NNmodel::getCurrentSourceKernelParameters ( ) const

inline

Gets std::map containing names and types of each parameter that should be passed through to the current source kernel.

◆ getDT()

double NNmodel::getDT ( ) const

inline

Gets the model integration step size.

◆ getGeneratedCodePath()

std::string NNmodel::getGeneratedCodePath	(	const std::string &	path,
		const std::string &	filename
	)		const

Generate path for generated code.

◆ getInitKernelParameters()

const map<string, string>& NNmodel::getInitKernelParameters ( ) const

inline

◆ getLocalCurrentSources()

const map<string, CurrentSource>& NNmodel::getLocalCurrentSources ( ) const

inline

Get std::map containing local named CurrentSource objects in model.

◆ getLocalNeuronGroups()

const map<string, NeuronGroup>& NNmodel::getLocalNeuronGroups ( ) const

inline

Get std::map containing local named NeuronGroup objects in model.

◆ getLocalSynapseGroups()

const map<string, SynapseGroup>& NNmodel::getLocalSynapseGroups ( ) const

inline

Get std::map containing local named SynapseGroup objects in model.

◆ getName()

const std::string& NNmodel::getName ( ) const

inline

Gets the name of the neuronal network model.

◆ getNeuronGridSize()

unsigned int NNmodel::getNeuronGridSize ( ) const

Gets the size of the neuron kernel thread grid.

This is calculated by adding together the number of threads required by each neuron population, padded to be a multiple of GPU's thread block size.

◆ getNeuronKernelParameters()

const map<string, string>& NNmodel::getNeuronKernelParameters ( ) const

inline

Gets std::map containing names and types of each parameter that should be passed through to the neuron kernel.

◆ getNumLocalNeurons()

unsigned int NNmodel::getNumLocalNeurons ( ) const

How many neurons are simulated locally in this model.

◆ getNumNeurons()

unsigned int NNmodel::getNumNeurons ( ) const

inline

How many neurons make up the entire model.

◆ getNumPreSynapseResetRequiredGroups()

unsigned int NNmodel::getNumPreSynapseResetRequiredGroups ( ) const

Return number of synapse groups which require a presynaptic reset kernel to be run.

◆ getNumRemoteNeurons()

unsigned int NNmodel::getNumRemoteNeurons ( ) const

How many neurons are simulated remotely in this model.

◆ getPrecision()

const std::string& NNmodel::getPrecision ( ) const

inline

Gets the floating point numerical precision.

◆ getRemoteCurrentSources()

const map<string, CurrentSource>& NNmodel::getRemoteCurrentSources ( ) const

inline

Get std::map containing remote named CurrentSource objects in model.

◆ getRemoteNeuronGroups()

const map<string, NeuronGroup>& NNmodel::getRemoteNeuronGroups ( ) const

inline

Get std::map containing remote named NeuronGroup objects in model.

◆ getRemoteSynapseGroups()

const map<string, SynapseGroup>& NNmodel::getRemoteSynapseGroups ( ) const

inline

Get std::map containing remote named SynapseGroup objects in model.

◆ getResetKernel()

unsigned int NNmodel::getResetKernel ( ) const

inline

Which kernel should contain the reset logic? Specified in terms of GENN_FLAGS.

◆ getRNType()

const std::string& NNmodel::getRNType ( ) const

inline

Gets the underlying type for random number generation (default: uint64_t)

◆ getSeed()

unsigned int NNmodel::getSeed ( ) const

inline

Get the random seed.

◆ getSimLearnPostKernelParameters()

const map<string, string>& NNmodel::getSimLearnPostKernelParameters ( ) const

inline

Gets std::map containing names and types of each parameter that should be passed through to the postsynaptic learning kernel.

◆ getSynapseDynamicsGridSize()

unsigned int NNmodel::getSynapseDynamicsGridSize ( ) const

Gets the size of the synapse dynamics kernel thread grid.

This is calculated by adding together the number of threads required by each synapse population's synapse dynamics kernel, padded to be a multiple of GPU's thread block size.

◆ getSynapseDynamicsGroups()

const map<string, std::pair<unsigned int, unsigned int> >& NNmodel::getSynapseDynamicsGroups ( ) const

inline

Get std::map containing names of synapse groups which require synapse dynamics and their thread IDs within the synapse dynamics kernel (padded to multiples of the GPU thread block size)

◆ getSynapseDynamicsKernelParameters()

const map<string, string>& NNmodel::getSynapseDynamicsKernelParameters ( ) const

inline

Gets std::map containing names and types of each parameter that should be passed through to the synapse dynamics kernel.

◆ getSynapseKernelGridSize()

unsigned int NNmodel::getSynapseKernelGridSize ( ) const

Gets the size of the synapse kernel thread grid.

This is calculated by adding together the number of threads required by each synapse population's synapse kernel, padded to be a multiple of GPU's thread block size.

◆ getSynapseKernelParameters()

const map<string, string>& NNmodel::getSynapseKernelParameters ( ) const

inline

Gets std::map containing names and types of each parameter that should be passed through to the synapse kernel.

◆ getSynapsePostLearnGridSize()

unsigned int NNmodel::getSynapsePostLearnGridSize ( ) const

Gets the size of the post-synaptic learning kernel thread grid.

This is calculated by adding together the number of threads required by each synapse population's postsynaptic learning kernel, padded to be a multiple of GPU's thread block size.

◆ getSynapsePostLearnGroups()

const map<string, std::pair<unsigned int, unsigned int> >& NNmodel::getSynapsePostLearnGroups ( ) const

inline

Get std::map containing names of synapse groups which require postsynaptic learning and their thread IDs within the postsynaptic learning kernel (padded to multiples of the GPU thread block size)

◆ getTimePrecision()

std::string NNmodel::getTimePrecision ( ) const

Gets the floating point numerical precision used to represent time.

◆ isDeviceInitRequired()

bool NNmodel::isDeviceInitRequired ( int localHostID ) const

Does this model require device initialisation kernel.

NOTE this is for neuron groups and densely connected synapse groups only

◆ isDeviceRNGRequired()

bool NNmodel::isDeviceRNGRequired ( ) const

Do any populations or initialisation code in this model require a device RNG?

NOTE some model code will use per-neuron RNGs instead

◆ isDeviceSparseInitRequired()

bool NNmodel::isDeviceSparseInitRequired ( ) const

Does this model require a device sparse initialisation kernel.

NOTE this is for sparsely connected synapse groups only

◆ isFinalized()

bool NNmodel::isFinalized ( ) const

inline

Is the model specification finalized.

◆ isHostRNGRequired()

bool NNmodel::isHostRNGRequired ( ) const

Do any populations or initialisation code in this model require a host RNG?

◆ isPreSynapseResetRequired()

bool NNmodel::isPreSynapseResetRequired ( ) const

inline

Is there reset logic to be run before the synapse kernel i.e. for dendritic delays.

◆ isSynapseGroupDynamicsRequired()

bool NNmodel::isSynapseGroupDynamicsRequired ( const std::string & name ) const

Does named synapse group have synapse dynamics.

◆ isSynapseGroupPostLearningRequired()

bool NNmodel::isSynapseGroupPostLearningRequired ( const std::string & name ) const

Does named synapse group have post-synaptic learning.

◆ isTimingEnabled()

bool NNmodel::isTimingEnabled ( ) const

inline

Are timers and timing commands enabled.

◆ scalarExpr()

string NNmodel::scalarExpr ( const double val ) const

Get the string literal that should be used to represent a value in the model's floating-point type.

◆ setConstInp()

void NNmodel::setConstInp	(	const string &	,
		double
	)

This function has been deprecated in GeNN 2.2.

This function sets a global input value to the specified neuron group.

◆ setDT()

void NNmodel::setDT ( double newDT )

Set the integration step size of the model.

This function sets the integration time step DT of the model.

◆ setGPUDevice()

void NNmodel::setGPUDevice ( int device )

Sets the underlying type for random number generation (default: uint64_t)

This function defines the way how the GPU is chosen. If "AUTODEVICE" (-1) is given as the argument, GeNN will use internal heuristics to choose the device. Otherwise the argument is the device number and the indicated device will be used.

Method to choose the GPU to be used for the model. If "AUTODEVICE' (-1), GeNN will choose the device based on a heuristic rule.

◆ setMaxConn()

void NNmodel::setMaxConn	(	const string &	sname,
		unsigned int	maxConnP
	)

This function defines the maximum number of connections for a neuron in the population.

◆ setName()

void NNmodel::setName ( const std::string & )

Method to set the neuronal network model name.

◆ setNeuronClusterIndex()

void NNmodel::setNeuronClusterIndex	(	const string &	neuronGroup,
		int	hostID,
		int	deviceID
	)

This function has been deprecated in GeNN 3.1.0.

This function is for setting which host and which device a neuron group will be simulated on.

◆ setPopulationSums()

void NNmodel::setPopulationSums ( )

Set the accumulated sums of lowest multiple of kernel block size >= group sizes for all simulated groups.

Accumulate the sums and block-size-padded sums of all simulation groups.

This method saves the neuron numbers of the populations rounded to the next multiple of the block size as well as the sums s(i) = sum_{1...i} n_i of the rounded population sizes. These are later used to determine the branching structure for the generated neuron kernel code.

◆ setPrecision()

void NNmodel::setPrecision ( FloatType floattype )

Set numerical precision for floating point.

This function sets the numerical precision of floating type variables. By default, it is GENN_GENN_FLOAT.

◆ setRNType()

void NNmodel::setRNType ( const std::string & type )

Sets the underlying type for random number generation (default: uint64_t)

◆ setSeed()

void NNmodel::setSeed ( unsigned int inseed )

Set the random seed (disables automatic seeding if argument not 0).

This function sets the random seed. If the passed argument is > 0, automatic seeding is disabled. If the argument is 0, the underlying seed is obtained from the time() function.

Parameters

inseed the new seed

◆ setSpanTypeToPre()

void NNmodel::setSpanTypeToPre ( const string & sname )

Method for switching the execution order of synapses to pre-to-post.

This function defines the execution order of the synapses in the kernels (0 : execute for every postsynaptic neuron 1: execute for every presynaptic neuron)

Parameters

sname name of the synapse group to which to apply the pre-synaptic span type

◆ setSynapseG()

void NNmodel::setSynapseG	(	const string &	,
		double
	)

This function has been depreciated as of GeNN 2.2.

This functions sets the global value of the maximal synaptic conductance for a synapse population that was idfentified as conductance specifcation method "GLOBALG".

◆ setTimePrecision()

void NNmodel::setTimePrecision ( TimePrecision timePrecision )

Set numerical precision for time.

◆ setTiming()

void NNmodel::setTiming ( bool theTiming )

Set whether timers and timing commands are to be included.

This function sets a flag to determine whether timers and timing commands are to be included in generated code.

◆ zeroCopyInUse()

bool NNmodel::zeroCopyInUse ( ) const

Are any variables in any populations in this model using zero-copy memory?

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

Public Types

Public Member Functions

Member Typedef Documentation

◆ NeuronGroupValueType

◆ SynapseGroupSubsetValueType

◆ SynapseGroupValueType

Constructor & Destructor Documentation

◆ NNmodel()

◆ ~NNmodel()

Member Function Documentation

◆ activateDirectInput()

◆ addCurrentSource() [1/2]

◆ addCurrentSource() [2/2]

◆ addNeuronPopulation() [1/4]

◆ addNeuronPopulation() [2/4]

◆ addNeuronPopulation() [3/4]

◆ addNeuronPopulation() [4/4]

◆ addSynapsePopulation() [1/7]

◆ addSynapsePopulation() [2/7]

◆ addSynapsePopulation() [3/7]

◆ addSynapsePopulation() [4/7]

◆ addSynapsePopulation() [5/7]

◆ addSynapsePopulation() [6/7]

◆ addSynapsePopulation() [7/7]

◆ canRunOnCPU()

◆ finalize()

◆ findCurrentSource() [1/2]

◆ findCurrentSource() [2/2]

◆ findNeuronGroup() [1/2]

◆ findNeuronGroup() [2/2]

◆ findSynapseGroup() [1/2]

◆ findSynapseGroup() [2/2]

◆ getCurrentSourceKernelParameters()

◆ getDT()

◆ getGeneratedCodePath()

◆ getInitKernelParameters()

◆ getLocalCurrentSources()

◆ getLocalNeuronGroups()

◆ getLocalSynapseGroups()

◆ getName()

◆ getNeuronGridSize()

◆ getNeuronKernelParameters()

◆ getNumLocalNeurons()

◆ getNumNeurons()

◆ getNumPreSynapseResetRequiredGroups()

◆ getNumRemoteNeurons()

◆ getPrecision()

◆ getRemoteCurrentSources()

◆ getRemoteNeuronGroups()

◆ getRemoteSynapseGroups()

◆ getResetKernel()

◆ getRNType()

◆ getSeed()

◆ getSimLearnPostKernelParameters()

◆ getSynapseDynamicsGridSize()

◆ getSynapseDynamicsGroups()

◆ getSynapseDynamicsKernelParameters()

◆ getSynapseKernelGridSize()

◆ getSynapseKernelParameters()

◆ getSynapsePostLearnGridSize()

◆ getSynapsePostLearnGroups()

◆ getTimePrecision()

◆ isDeviceInitRequired()

◆ isDeviceRNGRequired()

◆ isDeviceSparseInitRequired()

◆ isFinalized()

◆ isHostRNGRequired()

◆ isPreSynapseResetRequired()

◆ isSynapseGroupDynamicsRequired()

◆ isSynapseGroupPostLearningRequired()

◆ isTimingEnabled()

◆ scalarExpr()

◆ setConstInp()

◆ setDT()

◆ setGPUDevice()

◆ setMaxConn()

◆ setName()

◆ setNeuronClusterIndex()

◆ setPopulationSums()

◆ setPrecision()