COMMENT /* Copyright (c) 2015 EPFL-BBP, All rights reserved. THIS SOFTWARE IS PROVIDED BY THE BLUE BRAIN PROJECT ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE BLUE BRAIN PROJECT BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/legalcode or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. */ ENDCOMMENT TITLE GABAAB receptor with presynaptic short-term plasticity COMMENT GABAA receptor conductance using a dual-exponential profile presynaptic short-term plasticity based on Fuhrmann et al, 2002 Implemented by Srikanth Ramaswamy, Blue Brain Project, March 2009 _EMS (Eilif Michael Srikanth) Modification of ProbGABAA: 2-State model by Eilif Muller, Michael Reimann, Srikanth Ramaswamy, Blue Brain Project, August 2011 This new model was motivated by the following constraints: 1) No consumption on failure. 2) No release just after release until recovery. 3) Same ensemble averaged trace as deterministic/canonical Tsodyks-Markram using same parameters determined from experiment. 4) Same quantal size as present production probabilistic model. To satisfy these constaints, the synapse is implemented as a uni-vesicular (generalization to multi-vesicular should be straight-forward) 2-state Markov process. The states are {1=recovered, 0=unrecovered}. For a pre-synaptic spike or external spontaneous release trigger event, the synapse will only release if it is in the recovered state, and with probability u (which follows facilitation dynamics). If it releases, it will transition to the unrecovered state. Recovery is as a Poisson process with rate 1/Dep. This model satisfies all of (1)-(4). ENDCOMMENT COMMENT /** @file ProbGABAAB_EMS.mod @brief GABAAB receptor with presynaptic short-term plasticity @author Eilif Muller, Michael Reimann, Srikanth Ramaswamy, James King @ BBP @date 2011 */ ENDCOMMENT NEURON { THREADSAFE POINT_PROCESS ProbGABAAB_EMS RANGE tau_r_GABAA, tau_d_GABAA, tau_r_GABAB, tau_d_GABAB RANGE Use, u, Dep, Fac, u0, Rstate, tsyn_fac, u RANGE i,i_GABAA, i_GABAB, g_GABAA, g_GABAB, g, e_GABAA, e_GABAB, GABAB_ratio RANGE A_GABAA_step, B_GABAA_step, A_GABAB_step, B_GABAB_step NONSPECIFIC_CURRENT i POINTER rng RANGE synapseID, verboseLevel } PARAMETER { tau_r_GABAA = 0.2 (ms) : dual-exponential conductance profile tau_d_GABAA = 8 (ms) : IMPORTANT: tau_r < tau_d tau_r_GABAB = 3.5 (ms) : dual-exponential conductance profile :Placeholder value from hippocampal recordings SR tau_d_GABAB = 260.9 (ms) : IMPORTANT: tau_r < tau_d :Placeholder value from hippocampal recordings Use = 1.0 (1) : Utilization of synaptic efficacy (just initial values! Use, Dep and Fac are overwritten by BlueBuilder assigned values) Dep = 100 (ms) : relaxation time constant from depression Fac = 10 (ms) : relaxation time constant from facilitation e_GABAA = -80 (mV) : GABAA reversal potential e_GABAB = -97 (mV) : GABAB reversal potential gmax = .001 (uS) : weight conversion factor (from nS to uS) u0 = 0 :initial value of u, which is the running value of release probability synapseID = 0 verboseLevel = 0 GABAB_ratio = 0 (1) : The ratio of GABAB to GABAA } COMMENT The Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1 for comparison with Pr to decide whether to activate the synapse or not ENDCOMMENT VERBATIM #include #include #include double nrn_random_pick(void* r); void* nrn_random_arg(int argpos); ENDVERBATIM ASSIGNED { v (mV) i (nA) i_GABAA (nA) i_GABAB (nA) g_GABAA (uS) g_GABAB (uS) A_GABAA_step B_GABAA_step A_GABAB_step B_GABAB_step g (uS) factor_GABAA factor_GABAB rng : Recording these three, you can observe full state of model : tsyn_fac gives you presynaptic times, Rstate gives you : state transitions, : u gives you the "release probability" at transitions : (attention: u is event based based, so only valid at incoming events) Rstate (1) : recovered state {0=unrecovered, 1=recovered} tsyn_fac (ms) : the time of the last spike u (1) : running release probability } STATE { A_GABAA : GABAA state variable to construct the dual-exponential profile - decays with conductance tau_r_GABAA B_GABAA : GABAA state variable to construct the dual-exponential profile - decays with conductance tau_d_GABAA A_GABAB : GABAB state variable to construct the dual-exponential profile - decays with conductance tau_r_GABAB B_GABAB : GABAB state variable to construct the dual-exponential profile - decays with conductance tau_d_GABAB } INITIAL{ LOCAL tp_GABAA, tp_GABAB Rstate=1 tsyn_fac=0 u=u0 A_GABAA = 0 B_GABAA = 0 A_GABAB = 0 B_GABAB = 0 tp_GABAA = (tau_r_GABAA*tau_d_GABAA)/(tau_d_GABAA-tau_r_GABAA)*log(tau_d_GABAA/tau_r_GABAA) :time to peak of the conductance tp_GABAB = (tau_r_GABAB*tau_d_GABAB)/(tau_d_GABAB-tau_r_GABAB)*log(tau_d_GABAB/tau_r_GABAB) :time to peak of the conductance factor_GABAA = -exp(-tp_GABAA/tau_r_GABAA)+exp(-tp_GABAA/tau_d_GABAA) :GABAA Normalization factor - so that when t = tp_GABAA, gsyn = gpeak factor_GABAA = 1/factor_GABAA factor_GABAB = -exp(-tp_GABAB/tau_r_GABAB)+exp(-tp_GABAB/tau_d_GABAB) :GABAB Normalization factor - so that when t = tp_GABAB, gsyn = gpeak factor_GABAB = 1/factor_GABAB A_GABAA_step = exp(dt*(( - 1.0 ) / tau_r_GABAA)) B_GABAA_step = exp(dt*(( - 1.0 ) / tau_d_GABAA)) A_GABAB_step = exp(dt*(( - 1.0 ) / tau_r_GABAB)) B_GABAB_step = exp(dt*(( - 1.0 ) / tau_d_GABAB)) } BREAKPOINT { SOLVE state g_GABAA = gmax*(B_GABAA-A_GABAA) :compute time varying conductance as the difference of state variables B_GABAA and A_GABAA g_GABAB = gmax*(B_GABAB-A_GABAB) :compute time varying conductance as the difference of state variables B_GABAB and A_GABAB g = g_GABAA + g_GABAB i_GABAA = g_GABAA*(v-e_GABAA) :compute the GABAA driving force based on the time varying conductance, membrane potential, and GABAA reversal i_GABAB = g_GABAB*(v-e_GABAB) :compute the GABAB driving force based on the time varying conductance, membrane potential, and GABAB reversal i = i_GABAA + i_GABAB } PROCEDURE state() { A_GABAA = A_GABAA*A_GABAA_step B_GABAA = B_GABAA*B_GABAA_step A_GABAB = A_GABAB*A_GABAB_step B_GABAB = B_GABAB*B_GABAB_step } NET_RECEIVE (weight, weight_GABAA, weight_GABAB, Psurv, tsyn (ms)){ LOCAL result weight_GABAA = weight weight_GABAB = weight*GABAB_ratio : Locals: : Psurv - survival probability of unrecovered state : tsyn - time since last surival evaluation. INITIAL{ tsyn=t } : Do not perform any calculations if the synapse (netcon) is deactivated. This avoids drawing from the random stream if( !(weight > 0) ) { VERBATIM return; ENDVERBATIM } : calc u at event- if (Fac > 0) { u = u*exp(-(t - tsyn_fac)/Fac) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al. } else { u = Use } if(Fac > 0){ u = u + Use*(1-u) :update facilitation variable if Fac>0 Eq. 2 in Fuhrmann et al. } : tsyn_fac knows about all spikes, not only those that released : i.e. each spike can increase the u, regardless of recovered state. tsyn_fac = t : recovery if (Rstate == 0) { : probability of survival of unrecovered state based on Poisson recovery with rate 1/tau Psurv = exp(-(t-tsyn)/Dep) result = urand() if (result>Psurv) { Rstate = 1 : recover if( verboseLevel > 0 ) { printf( "Recovered! %f at time %g: Psurv = %g, urand=%g\n", synapseID, t, Psurv, result ) } } else { : survival must now be from this interval tsyn = t if( verboseLevel > 0 ) { printf( "Failed to recover! %f at time %g: Psurv = %g, urand=%g\n", synapseID, t, Psurv, result ) } } } if (Rstate == 1) { result = urand() if (result 0 ) { printf( "Release! %f at time %g: vals %g %g %g \n", synapseID, t, A_GABAA, weight_GABAA, factor_GABAA ) } } else { if( verboseLevel > 0 ) { printf("Failure! %f at time %g: urand = %g\n", synapseID, t, result ) } } } } PROCEDURE setRNG() { VERBATIM { /** * This function takes a NEURON Random object declared in hoc and makes it usable by this mod file. * Note that this method is taken from Brett paper as used by netstim.hoc and netstim.mod */ void** pv = (void**)(&_p_rng); if( ifarg(1)) { *pv = nrn_random_arg(1); } else { *pv = (void*)0; } } ENDVERBATIM } FUNCTION urand() { VERBATIM double value; if (_p_rng) { /* :Supports separate independent but reproducible streams for : each instance. However, the corresponding hoc Random : distribution MUST be set to Random.uniform(1) */ value = nrn_random_pick(_p_rng); //printf("random stream for this simulation = %lf\n",value); return value; }else{ ENDVERBATIM : the old standby. Cannot use if reproducible parallel sim : independent of nhost or which host this instance is on : is desired, since each instance on this cpu draws from : the same stream urand = scop_random(1) VERBATIM } ENDVERBATIM urand = value } FUNCTION toggleVerbose() { verboseLevel = 1 - verboseLevel }