rate_transformer_node – Rate neuron that sums up incoming rates and applies a nonlinearity specified via the template


Base class for rate transformer model of the form

\[X_i(t) = \phi( \sum w_{ij} \cdot \psi( X_j(t-d_{ij}) ) )\]

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, 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 rate transformer node and connecting the rate transformer node to the receiving rate neuron instead of using a direct connection. Please note that for instantaneous rate connections the rate arrives one time step later at the receiving rate neurons as with a direct connection.

Weights on connections from and to the rate_transformer_node are handled as usual. Delays are honored on incoming and outgoing connections.


InstantaneousRateConnectionEvent, DelayedRateConnectionEvent


InstantaneousRateConnectionEvent, DelayedRateConnectionEvent


Only the parameter linear_summation and the parameters from the class Nonlinearities can be set in the status dictionary.

Examples using this model