lin_rate – Linear rate model ============================ Description +++++++++++ lin_rate is an implementation of a rate model with linear input function :math:`input(h) = g * h`. 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 active, the excitatory input of the model is multiplied with the function :math:`mult\_coupling\_ex(rate) = g_{ex} * ( \theta_{ex} - rate )` and the inhibitory input is multiplied with the function :math:`mult\_coupling\_in(rate) = g_{in} * ( \theta_{in} + rate )`. The model supports connections to other rate models with either zero or non-zero delay, and it 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. Note that some of the parameters only apply to rate neurons and not to rate transformers. =============== ======= ================================================== rate real Rate (unitless) tau ms Time constant of rate dynamics lambda real Passive decay rate mu real Mean input sigma real Noise parameter g real Gain parameter mult_coupling boolean Switch to enable/disable multiplicative coupling g_ex real Linear factor in multiplicative coupling g_in real Linear factor in multiplicative coupling theta_ex real Shift in multiplicative coupling theta_in real Shift in multiplicative coupling rectify_rate real Rectifying rate rectify_output boolean Switch to restrict rate to values >= rectify_rate =============== ======= ================================================== References ++++++++++ .. [1] Hahne J, Dahmen D, Schuecker J, Frommer A, Bolten M, Helias M, Diesmann M (2017). Integration of continuous-time dynamics in a spiking neural network simulator. Frontiers in Neuroinformatics, 11:34. DOI: https://doi.org/10.3389/fninf.2017.00034 .. [2] Hahne J, Helias M, Kunkel S, Igarashi J, Bolten M, Frommer A, Diesmann M (2015). A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations. Frontiers Neuroinformatics, 9:22. DOI: https://doi.org/10.3389/fninf.2015.00022 Sends +++++ InstantaneousRateConnectionEvent, DelayedRateConnectionEvent Receives ++++++++ InstantaneousRateConnectionEvent, DelayedRateConnectionEvent, DataLoggingRequest See also ++++++++ :doc:`Neuron `, :doc:`Rate `