% # iGABAB_TC_TRN_NN:
%
% SCALED synaptic GABA-B-ergic inhibitory current
% This is, very slightly, an original formulation of the GABA-B current.
% The state variable [R] for GABAB is 'customized' here, in that, rather
% than the popular formulation of a 0.5 mM box 0.3 ms long for the
% transmitter amount, inherited from (Destexhe 1996), the VERY similar
% 2 * (1 + tanh( V / 4) ) method of calculating transmitter concentration
% from (Olufsen 2003) is used. 0.3 ms is about as long as a neuron's
% voltage is above 0 mV, the latter being the definition of when this
% voltage-sensitive transmitter concentration is non-zero. The same
% concentration amplitude was used since I had already seen a long time
% of realistic results with GABAA responding to it, and GABAB's effect
% obviously has a much more malleable effector in its maximal conductance.
% In many ways, this is a simplified version of the GABA-B current from (Vijayan
% 2012): instead of a fixed spike of transmitter concentration when the
% presynaptic cell spikes, we use the same GABA concentration calculation
% method from the GABA-A current.
%
% References:
% - Destexhe, A., Bal, T., McCormick, D. A., & Sejnowski, T. J. (1996). Ionic
% mechanisms underlying synchronized oscillations and propagating waves in a
% model of ferret thalamic slices. Journal of Neurophysiology, 76(3), 2049–2070.
% - Olufsen, M. S., Whittington, M. A., Camperi, M., & Kopell, N. (2003). New
% roles for the gamma rhythm: population tuning and preprocessing for the beta
% rhythm. Journal of Computational Neuroscience, 14(1), 33–54.
% - Vijayan, S., & Kopell, N. J. (2012). Thalamic model of awake alpha
% oscillations and implications for stimulus processing. Proceedings of the
% National Academy of Sciences, 109(45), 18553–18558.
% doi:10.1073/pnas.1215385109
%
% From (Destxhe 1996),
% K1 = 0.5 mM^{-1} ms^{-1}
% K2 = 0.0012 ms^{-1}
% K3 = 0.18 ms^{-1}
% K4 = 0.034 ms^{-1}
%
% Tags: synapse, connection, inhibition
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Parameters
gGABAB = 0.001
EGABAB = -95
rGABABIC = 0
rGABABNoiseIC= 0.1
sGABABIC = 0
sGABABNoiseIC= 0.1
% Connectivity
% Connective radius, aka how many target cells each source cell connects
% to, from the source's perspective.
radius = 10
% Remove autapses to the dendrite corresponding to this soma
removeRecurrentBool = 0
% We also need to normalize the conductance in mS/cm^2 by the number of
% connections each target cell is receiving on average, so that the TOTAL
% sum of their conductive inputs adds to our overall maximal conductance
% above.
normalizingFactor = min(((2*radius + (1-removeRecurrentBool)) / (N_post/N_pre)), N_pre)
% Note that what is passed is 2x the radius
netcon = netconNearestNeighbors(2*radius, N_pre, N_post, removeRecurrentBool)
% Functions
iGABAB_TC_TRN_NN(X,sGABAB) = -gGABAB/N_pre.*((sGABAB.^4./(sGABAB.^4 + 100))*netcon).*(X-EGABAB)
% This way we record the synaptic currents!
monitor iGABAB_TC_TRN_NN
% ODEs and ICs
rGABAB' = 0.5.*(2.*(1 + tanh(X_pre./4))).*(1-rGABAB) - 0.0012.*rGABAB
rGABAB(0) = rGABABIC+rGABABNoiseIC.*rand(1,Npre)
sGABAB' = 0.18.*rGABAB - 0.034.*sGABAB
sGABAB(0) = sGABABIC+sGABABNoiseIC.*rand(1,Npre)
% Linker
@current += iGABAB_TC_TRN_NN(X_post,sGABAB)