Improve documentation of rate-based models

models/gauss_rate.h

@@ -50,16 +50,25 @@ Name: gauss_rate - rate model with Gaussian gain function
 gauss_rate is an implementation of a nonlinear rate model with input
 function
 @f[ input(h) = g * \exp( -( x - \mu )^2 / ( 2 * \sigma^2 ) ) @f].
+It either models a rate neuron with input noise (see rate_neuron_ipn)
+or a rate transformer (see rate_transformer_node).
 Input transformation can either be applied to individual inputs
 or to the sum of all inputs.
 
 The model supports connections to other rate models with either zero or
 non-zero delay, and uses the secondary_event concept introduced with
 the gap-junction framework.
 
+Nonlinear rate neurons can be created by typing
+nest.Create('gauss_rate_ipn'). Nonlinear rate transformers can be
+created by typing nest.Create('rate_transformer_gauss').
+
+
 Parameters:
 
-The following parameters can be set in the status dictionary.
+The following parameters can be set in the status dictionary. Note
+that some of the parameters only apply to rate neurons and not to rate
+transformers.
 
 \verbatim embed:rst
 =================  ======= ==============================================

models/lin_rate.h

@@ -42,23 +42,34 @@ Name: lin_rate - Linear rate model
 
 Description:
 
-lin_rate is an implementation of a linear rate model with
-input function \f$ input(h) = g * h \f$.
-The model supports multiplicative coupling which can
-be switched on and off via the boolean parameter mult_coupling
-(default=false). In case multiplicative coupling is actived
-the excitatory input of the model is multiplied with the function
-\f$ mult\_coupling\_ex(rate) = g_{ex} * ( \theta_{ex} - rate ) \f$
-and the inhibitory input is multiplied with the function
-\f$ mult\_coupling\_in(rate) = g_{in} * ( \theta_{in} + rate ) \f$.
-
-The model supports connections to other rate models with either zero or
-non-zero delay, and uses the secondary_event concept introduced with
-the gap-junction framework.
+lin_rate is an implementation of a rate model with linear input
+function \f$ input(h) = g * h \f$. It either models a rate neuron with
+input noise (see rate_neuron_ipn), a rate neuron with output noise
+(see rate_neuron_opn) or a rate transformer (see
+rate_transformer_node).
+
+Linear rate neurons support multiplicative coupling which can be
+switched on and off via the boolean parameter mult_coupling
+(default=false). In case multiplicative coupling is actived the
+excitatory input of the model is multiplied with the function \f$
+mult\_coupling\_ex(rate) = g_{ex} * ( \theta_{ex} - rate ) \f$ and the
+inhibitory input is multiplied with the function \f$
+mult\_coupling\_in(rate) = g_{in} * ( \theta_{in} + rate ) \f$.
+
+The model supports connections to other rate models with either zero
+or non-zero delay, and uses the secondary_event concept introduced
+with the gap-junction framework.
+
+Linear rate neurons can be created by typing
+nest.Create('lin_rate_ipn') or nest.Create('lin_rate_opn') for input
+noise or output noise, respectively. Linear rate transformers can be
+created by typing nest.Create('rate_transformer_lin').
 
 Parameters:
 
-The following parameters can be set in the status dictionary.
+The following parameters can be set in the status dictionary. Note
+that some of the parameters only apply to rate neurons and not to rate
+transformers.
 \verbatim embed:rst
 ===============  ======= ==================================================
  rate            real    Rate (unitless)
@@ -97,7 +108,8 @@ DataLoggingRequest
 
 Author: David Dahmen, Jan Hahne, Jannis Schuecker
 
-SeeAlso: rate_connection_instantaneous, rate_connection_delayed
+SeeAlso: rate_connection_instantaneous, rate_connection_delayed,
+rate_neuron_ipn, rate_neuron_opn
 */
 
 class nonlinearities_lin_rate

models/rate_transformer_node.h

@@ -53,11 +53,17 @@ Name: rate_transformer_node - Rate neuron that sums up incoming rates
 
 Description:
 
+Base class for rate transformer model of the form
+@f[
+  X_i(t) = \phi( \sum w_{ij} \cdot \psi( X_j(t-d_{ij}) ) )
+
+@f]
 The rate transformer node simply applies the nonlinearity specified in the
 input-function of the template class to all incoming inputs. The boolean
 parameter linear_summation determines whether the input function is applied to
-the summed up incoming connections (True, default value) or to each input
-individually (False).
+the summed up incoming connections (True, default value, input
+represents phi) or to each input individually (False, input represents psi).
+
 An important application is to provide the possibility to
 apply different nonlinearities to different incoming connections of the
 same rate neuron by connecting the sending rate neurons to the

models/sigmoid_rate.h

@@ -46,16 +46,25 @@ Name: sigmoid_rate - rate model with sigmoidal gain function
 
 sigmoid_rate is an implementation of a nonlinear rate model with input
 function \f$ input(h) = g / ( 1. + \exp( -\beta * ( h - \theta ) ) ) \f$.
+It either models a rate neuron with input noise (see rate_neuron_ipn)
+or a rate transformer (see rate_transformer_node).
 Input transformation can either be applied to individual inputs
 or to the sum of all inputs.
 
 The model supports connections to other rate models with either zero or
 non-zero delay, and uses the secondary_event concept introduced with
 the gap-junction framework.
 
+Nonlinear rate neurons can be created by typing
+nest.Create('sigmoid_rate_ipn'). Nonlinear rate transformers can be
+created by typing nest.Create('rate_transformer_sigmoid').
+
+
 Parameters:
 
-The following parameters can be set in the status dictionary.
+The following parameters can be set in the status dictionary.Note
+that some of the parameters only apply to rate neurons and not to rate
+transformers.
 \verbatim embed:rst
 ==================  ======= ==============================================
  rate               real    Rate (unitless)

models/sigmoid_rate_gg_1998.h

@@ -46,16 +46,25 @@ as defined in [1].
 
 sigmoid_rate_gg_1998 is an implementation of a nonlinear rate model with
 input function \f$ input(h) = ( g * h )^4 / ( .1^4 + ( g * h )^4 ) \f$.
+It either models a rate neuron with input noise (see rate_neuron_ipn)
+or a rate transformer (see rate_transformer_node).
 Input transformation can either be applied to individual inputs
 or to the sum of all inputs.
 
 The model supports connections to other rate models with either zero or
 non-zero delay, and uses the secondary_event concept introduced with
 the gap-junction framework.
 
+Nonlinear rate neurons can be created by typing
+nest.Create('sigmoid_rate_gg_1998_ipn'). Nonlinear rate transformers can be
+created by typing nest.Create('rate_transformer_sigmoid_rate_gg_1998').
+
+
 Parameters:
 
-The following parameters can be set in the status dictionary.
+The following parameters can be set in the status dictionary. Note
+that some of the parameters only apply to rate neurons and not to rate
+transformers.
 
 \verbatim embed:rst
 ==================  ======= ==============================================

models/tanh_rate.h

@@ -42,18 +42,28 @@ Name: tanh_rate - rate model with hyperbolic tangent non-linearity
 
 Description:
 
-tanh_rate is an implementation of a nonlinear rate model with input function
-\f$ input(h) = \tanh(g * (h-\theta)) \f$.
-Input transformation can either be applied to individual inputs
-or to the sum of all inputs.
+tanh_rate is an implementation of a nonlinear rate model with input
+function \f$ input(h) = \tanh(g * (h-\theta)) \f$. It either models a
+rate neuron with input noise (see rate_neuron_ipn), a rate neuron with
+output noise (see rate_neuron_opn) or a rate transformer (see
+rate_transformer_node). Input transformation can either be applied to
+individual inputs or to the sum of all inputs.
 
 The model supports connections to other rate models with either zero or
 non-zero delay, and uses the secondary_event concept introduced with
 the gap-junction framework.
 
+Nonlinear rate neurons can be created by typing
+nest.Create('tanh_rate_ipn') or nest.Create('tanh_rate_opn') for input
+noise or output noise, respectively. Nonlinear rate transformers can
+be created by typing nest.Create('rate_transformer_tanh').
+
+
 Parameters:
 
-The following parameters can be set in the status dictionary.
+The following parameters can be set in the status dictionary. Note
+that some of the parameters only apply to rate neurons and not to rate
+transformers.
 
 \verbatim embed:rst
 ==================  ======= ==============================================

models/threshold_lin_rate.h

@@ -47,16 +47,26 @@ Name: threshold_lin_rate - rate model with threshold-linear gain function
 
 threshold_lin_rate is an implementation of a nonlinear rate model with input
 function \f$ input(h) = min( max( g * ( h - \theta ), 0 ), \alpha ) \f$.
-Input transformation can either be applied to individual inputs
-or to the sum of all inputs.
+It either models a rate neuron with input noise (see rate_neuron_ipn),
+a rate neuron with output noise (see rate_neuron_opn) or a rate
+transformer (see rate_transformer_node). Input transformation can
+either be applied to individual inputs or to the sum of all inputs.
 
 The model supports connections to other rate models with either zero or
 non-zero delay, and uses the secondary_event concept introduced with
 the gap-junction framework.
 
+Nonlinear rate neurons can be created by typing
+nest.Create('threshold_lin_rate_ipn') or
+nest.Create('threshold_lin_rate_opn') for input noise or output noise,
+respectively. Nonlinear rate transformers can be created by typing
+nest.Create('rate_transformer_threshold_lin').
+
 Parameters:
 
-The following parameters can be set in the status dictionary.
+The following parameters can be set in the status dictionary. Note
+that some of the parameters only apply to rate neurons and not to rate
+transformers.
 
 \verbatim embed:rst
 ==================  ======= ==============================================