rate_transformer_node – Rate neuron that sums up incoming rates and applies a nonlinearity specified via the template
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Description
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Base class for rate transformer model of the form
.. math::
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.
Receives
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InstantaneousRateConnectionEvent, DelayedRateConnectionEvent
Sends
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InstantaneousRateConnectionEvent, DelayedRateConnectionEvent
Parameters
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Only the parameter ``linear_summation`` and the parameters from the class ``Nonlinearities`` can be set in the
status dictionary.
Examples using this model
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.. listexamples:: rate_transformer_node