Add Tsodyks synapse model

models/synapses/tsodyks_synapse.nestml

@@ -0,0 +1,100 @@
+"""
+tsodyks_synapse - Synapse type with short term plasticity
+#########################################################
+
+Description
++++++++++++
+
+This synapse model implements synaptic short-term depression and short-term
+facilitation according to [1]_. In particular it solves Eqs (3) and (4) from
+this paper in an exact manner.
+
+Synaptic depression is motivated by depletion of vesicles in the readily
+releasable pool of synaptic vesicles (variable x in equation (3)). Synaptic
+facilitation comes about by a presynaptic increase of release probability,
+which is modeled by variable U in Eq (4).
+
+The original interpretation of variable y is the amount of glutamate
+concentration in the synaptic cleft. In [1]_ this variable is taken to be
+directly proportional to the synaptic current caused in the postsynaptic
+neuron (with the synaptic weight w as a proportionality constant). In order
+to reproduce the results of [1]_ and to use this model of synaptic plasticity
+in its original sense, the user therefore has to ensure the following
+conditions:
+
+1.) The postsynaptic neuron must be of type ``iaf_psc_exp`` or ``iaf_psc_exp_htum``,
+because these neuron models have a postsynaptic current which decays
+exponentially.
+
+2.) The time constant of each ``tsodyks_synapse`` targeting a particular neuron
+must be chosen equal to that neuron's synaptic time constant. In particular
+that means that all synapses targeting a particular neuron have the same
+parameter ``tau_psc``.
+
+However, there are no technical restrictions using this model of synaptic
+plasticity also in conjunction with neuron models that have a different
+dynamics for their synaptic current or conductance. The effective synaptic
+weight, which will be transmitted to the postsynaptic neuron upon occurrence
+of a spike at time t is :math:`u(t) \cdot x(t) \cdot w`, where ``u(t)`` and ``x(t)``
+are defined in Eq (3) and (4), w is the synaptic weight specified upon connection.
+The interpretation is as follows: The quantity :math:`u(t) \cdot x(t)` is the release
+probability times the amount of releasable synaptic vesicles at time t of the
+presynaptic neuron's spike, so this equals the amount of transmitter expelled
+into the synaptic cleft.
+
+The amount of transmitter then relaxes back to 0 with time constant tau_psc
+of the synapse's variable y. Since the dynamics of ``y(t)`` is linear, the
+postsynaptic neuron can reconstruct from the amplitude of the synaptic
+impulse :math:`u(t) \cdot x(t) \cdot w` the full shape of ``y(t)``. The postsynaptic
+neuron, however, might choose to have a synaptic current that is not necessarily
+identical to the concentration of transmitter ``y(t)`` in the synaptic cleft. It may
+realize an arbitrary postsynaptic effect depending on ``y(t)``.
+
+
+References
+++++++++++
+
+.. [1] Tsodyks M, Uziel A, Markram H (2000). Synchrony generation in recurrent
+       networks with frequency-dependent synapses. Journal of Neuroscience,
+       20 RC50. URL: http://infoscience.epfl.ch/record/183402
+"""
+model tsodyks_synapse:
+    state:
+        x real = 1
+        y real = 0
+        u real = 0
+
+    parameters:
+        w real = 1    @nest::weight   # Synaptic weight
+        d ms = 1 ms  @nest::delay   # Synaptic transmission delay
+        tau_psc ms = 3 ms
+        tau_fac ms = 0 ms
+        tau_rec ms = 800 ms
+        U real = .5
+
+    input:
+        pre_spikes <- spike
+
+    output:
+        spike
+
+    onReceive(pre_spikes):
+        Puu real = tau_fac == 0 ? 0 : exp(-resolution() / tau_fac)
+        Pyy real = exp(-resolution() / tau_psc)
+        Pzz real = exp(-resolution() / tau_rec)
+        Pxy real = ((Pzz - 1) * tau_rec - (Pyy - 1) * tau_psc) / (tau_psc - tau_rec)
+        Pxz real = 1 - Pzz
+        z real = 1 - x - y
+
+        # depress synapse
+        u *= Puu
+        x += Pxy * y + Pxz * z
+        y *= Pyy
+        u += U * (1 - u)
+
+        delta_y_tsp real = u * x
+        x -= delta_y_tsp
+        y += delta_y_tsp
+
+        # deliver spike to postsynaptic partner
+        emit_spike(delta_y_tsp * w, d)

pynestml/codegeneration/resources_nest/point_neuron/common/SynapseHeader.h.jinja2

@@ -671,14 +671,23 @@ public:
   send( Event& e, const thread tid, const {{synapseName}}CommonSynapseProperties& cp )
 {%- endif %}
   {
-    const double __resolution = nest::Time::get_resolution().get_ms();  // do not remove, this is necessary for the resolution() function
+    const double __t_spike = e.get_stamp().get_ms();
+
+    double __resolution;    // do not remove, this is necessary for the resolution() function
+    if (t_lastspike_ < 0.)
+    {
+        __resolution = __t_spike;
+    }
+    else
+    {
+        __resolution = __t_spike - t_lastspike_;
+    }
 
     auto get_thread = [tid]()
     {
         return tid;
     };
 
-    const double __t_spike = e.get_stamp().get_ms();
 #ifdef DEBUG
     std::cout << "{{synapseName}}::send(): handling pre spike at t = " << __t_spike << std::endl;
 #endif