sigmoid_rate_gg_1998 – rate model with sigmoidal gain function ============================================================== Description +++++++++++ ``sigmoid_rate_gg_1998`` is an implementation of a nonlinear rate model with input function as in [1]_ :math:`input(h) = ( g \cdot h )^4 / ( .1^4 + ( g \cdot h )^4 )`. 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')``. See also [2]_, [3]_. 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 mu real Mean input sigma real Noise parameter g real Gain parameter rectify_rate real Rectifying rate linear_summation boolean Specifies type of non-linearity (see above) rectify_output boolean Switch to restrict rate to values >= rectify_rate ================== ======= ============================================== Note: The boolean parameter linear_summation determines whether the input from different presynaptic neurons is first summed linearly and then transformed by a nonlinearity (true), or if the input from individual presynaptic neurons is first nonlinearly transformed and then summed up (false). Default is true. References ++++++++++ .. [1] Gancarz G, Grossberg S (1998). A neural model of the saccade generator in the reticular formation. Neural Networks, 11(7):1159–1174. DOI: https://doi.org/10.1016/S0893-6080(98)00096-3 .. [2] 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 .. [3] 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 in 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 `