Add attributes to spike events

doc/nestml_language/neurons_in_nestml.rst

@@ -31,48 +31,7 @@ A neuron model written in NESTML can be configured to receive two distinct types
        AMPA_spikes <- spike
        I_stim pA <- continuous
 
-The general syntax is:
-
-::
-
-    port_name <- inputQualifier spike
-    port_name dataType <- continuous
-
 The spiking input ports are declared without a data type, whereas the continuous input ports must have a data type.
-For spiking input ports, the qualifier keywords decide whether inhibitory and excitatory inputs are lumped together into a single named input port, or if they are separated into differently named input ports based on their sign. When processing a spike event, some simulators (including NEST) use the sign of the amplitude (or weight) property in the spike event to indicate whether it should be considered an excitatory or inhibitory spike. By using the qualifier keywords, a single spike handler can route each incoming spike event to the correct input buffer (excitatory or inhibitory). Compare:
-
-.. code-block:: nestml
-
-   input:
-       # [...]
-       all_spikes <- spike
-
-In this case, all spike events will be processed through the ``all_spikes`` input port. A spike weight could be positive or negative, and the occurrences of ``all_spikes`` in the model should be considered a signed quantity.
-
-.. code-block:: nestml
-
-   input:
-       # [...]
-       AMPA_spikes <- excitatory spike
-       GABA_spikes <- inhibitory spike
-
-In this case, spike events that have a negative weight are routed to the ``GABA_spikes`` input port, and those that have a positive weight to the ``AMPA_spikes`` port.
-
-It is equivalent if either both `inhibitory` and `excitatory` are given, or neither: an unmarked port will by default handle all incoming presynaptic spikes.
-
-.. list-table::
-   :header-rows: 1
-   :widths: 10 60
-
-   * - Keyword
-     - The incoming weight :math:`w`...
-   * - none, or ``excitatory`` and ``inhibitory``
-     - ... may be positive or negative. It is added to the buffer with signed value :math:`w` (positive or negative).
-   * - ``excitatory``
-     - ... should not be negative. It is added to the buffer with non-negative magnitude :math:`w`.
-   * - ``inhibitory``
-     - ... should be negative. It is added to the buffer with non-negative magnitude :math:`-w`.
-
 
 
 Integrating current input
@@ -214,12 +173,12 @@ The input ports can also be defined as vectors. For example,
 
    neuron multi_synapse_vectors:
        input:
-           AMPA_spikes <- excitatory spike
-           GABA_spikes <- inhibitory spike
+           AMPA_spikes <- spike
+           GABA_spikes <- spike
            NMDA_spikes <- spike
            foo[2] <- spike
-           exc_spikes[3] <- excitatory spike
-           inh_spikes[3] <- inhibitory spike
+           exc_spikes[3] <- spike
+           inh_spikes[3] <- spike
 
        equations:
            kernel I_kernel_exc = exp(-1 / tau_syn_exc * t)

doc/pynestml_toolchain/front.rst

@@ -267,8 +267,6 @@ Given the fact that context conditions have the commonality of checking the cont
 
 -  *CoCoConvolveHasCorrectParameter*: Checks that *convolve* calls are not provided with complex expressions, but only variables.
 
--  *CoCoTypeOfBufferUnique*: Checks that no keyword is stated twice in an input buffer declaration, e.g., *inhibitory inhibitory spike*.
-
 -  *CoCoUserDeclaredFunctionCorrectlyDefined*: Checks that user-defined functions are correctly defined, i.e., only parameters of the function are used, and the return type is correctly stated.
 
 -  *CoCoVariableOncePerScope*: Checks that each variable is defined at most once per scope, i.e., no variable is redefined.

doc/running/running_nest.rst

@@ -133,7 +133,7 @@ Multiple input ports with vectors in NEST
 
 See :ref:`Multiple input ports with vectors` for an example with input ports defined as vectors.
 
-Each connection in NEST is denoted by a receiver port or ``rport`` number which is an integer that starts with 0. All default connections in NEST have the ``rport`` 0. NESTML routes the spikes with ``excitatory`` and ``inhibitory`` qualifiers into separate input buffers, whereas NEST identifies them with the same ``rport`` number.
+Each connection in NEST is denoted by a receiver port or ``rport`` number which is an integer that starts with 0. All default connections in NEST have the ``rport`` 0.
 
 During the code generation for NEST, NESTML maintains an internal mapping between NEST ``rports`` and NESTML input ports. A list of port names defined in a model and their corresponding ``rport`` numbers can be queried from the status dictionary using the NEST API. For neurons with multiple input ports, the ``receptor_type`` values in the ``nest.Connect()`` call start from 1 as the default ``receptor_type`` 0 is excluded to avoid any accidental connections.
 
@@ -159,7 +159,7 @@ The above code querying for ``receptor_types`` gives a list of port names and NE
      - 1
    * - NMDA_spikes
      - 2
-   * - FOO_0
+   * - FOO_0 XXXXX _VEC_IDX_
      - 3
    * - FOO_1
      - 4

doc/tutorials/spike_frequency_adaptation/models/iaf_psc_alpha.nestml

@@ -83,8 +83,8 @@ model iaf_psc_alpha_neuron:
         I_e pA = 0 pA
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

doc/tutorials/spike_frequency_adaptation/models/iaf_psc_alpha_adapt_curr.nestml

@@ -90,8 +90,8 @@ model iaf_psc_alpha_adapt_curr_neuron:
         I_e pA = 0 pA
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

doc/tutorials/spike_frequency_adaptation/models/iaf_psc_alpha_adapt_thresh.nestml

@@ -90,8 +90,8 @@ model iaf_psc_alpha_adapt_thresh_neuron:
         I_e pA = 0 pA
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

doc/tutorials/spike_frequency_adaptation/models/iaf_psc_alpha_adapt_thresh_OU.nestml

@@ -99,8 +99,8 @@ model iaf_psc_alpha_adapt_thresh_OU_neuron:
         A_noise pA = ((D_noise * tau_syn_exc / 2) * (1 - exp(-2 * resolution() / tau_syn_exc )))**.5
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/aeif_cond_alpha_neuron.nestml

@@ -105,8 +105,8 @@ model aeif_cond_alpha_neuron:
         PSConInit_I nS/ms = nS * e / tau_syn_inh
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/aeif_cond_exp_neuron.nestml

@@ -53,17 +53,19 @@ model aeif_cond_exp_neuron:
         V_m mV = E_L   # Membrane potential
         w pA = 0 pA    # Spike-adaptation current
         refr_t ms = 0 ms    # Refractory period timer
+        g_syn_exc nS = 0 nS
+        g_syn_inh nS = 0 nS
 
     equations:
         inline V_bounded mV = min(V_m, V_peak)    # prevent exponential divergence
-        kernel g_inh = exp(-t / tau_syn_inh)
-        kernel g_exc = exp(-t / tau_syn_exc)
+        g_syn_exc' = -g_syn_exc / tau_syn_exc
+        g_syn_inh' = -g_syn_inh / tau_syn_inh
 
         # Add inlines to simplify the equation definition of V_m
         inline exp_arg real = (V_bounded - V_th) / Delta_T
         inline I_spike pA = g_L * Delta_T * exp(exp_arg)
-        inline I_syn_exc pA = convolve(g_exc, exc_spikes) * nS * (V_bounded - E_exc)
-        inline I_syn_inh pA = convolve(g_inh, inh_spikes) * nS * (V_bounded - E_inh)
+        inline I_syn_exc pA = g_syn_exc * (V_bounded - E_exc)
+        inline I_syn_inh pA = g_syn_inh * nS * (V_bounded - E_inh)
 
         V_m' = (-g_L * (V_bounded - E_L) + I_spike - I_syn_exc - I_syn_inh - w + I_e + I_stim) / C_m
         w' = (a * (V_bounded - E_L) - w) / tau_w
@@ -96,13 +98,19 @@ model aeif_cond_exp_neuron:
         I_e pA = 0 pA
 
     input:
-        inh_spikes <- inhibitory spike
-        exc_spikes <- excitatory spike
+        spike_in_port <- spike
         I_stim pA <- continuous
 
     output:
         spike
 
+    onReceive(spike_in_port):
+        # route the incoming spike on the basis of the weight: less than zero means an inhibitory spike; greater than zero means an excitatory spike
+        if spike_in_port < 0:
+            g_syn_inh += spike_in_port * nS * s
+        else:
+            g_syn_exc += spike_in_port * nS * s
+
     update:
         if refr_t > 0 ms:
             # neuron is absolute refractory, do not evolve V_m

models/neurons/hh_cond_exp_destexhe_neuron.nestml

@@ -126,8 +126,8 @@ model hh_cond_exp_destexhe_neuron:
         D_inh uS**2/ms = 2 * sigma_noise_inh**2 / tau_syn_inh
 
     input:
-        inh_spikes <- inhibitory spike
-        exc_spikes <- excitatory spike
+        inh_spikes <- spike
+        exc_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/hh_cond_exp_traub_neuron.nestml

@@ -113,8 +113,8 @@ model hh_cond_exp_traub_neuron:
         I_e pA = 0 pA
 
     input:
-        inh_spikes <- inhibitory spike
-        exc_spikes <- excitatory spike
+        inh_spikes <- spike
+        exc_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/hh_moto_5ht_neuron.nestml

@@ -108,8 +108,8 @@ model hh_moto_5ht_neuron:
         alpha mmol/pA = 1E-5 mmol/pA
 
     input:
-        inh_spikes <- inhibitory spike
-        exc_spikes <- excitatory spike
+        inh_spikes <- spike
+        exc_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/hh_psc_alpha_neuron.nestml

@@ -108,8 +108,8 @@ model hh_psc_alpha_neuron:
         beta_h_init  real = 1. / ( 1. + exp( -( V_m_init / mV + 35. ) / 10. ) )
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/iaf_chxk_2008_neuron.nestml

@@ -80,8 +80,8 @@ model iaf_chxk_2008_neuron:
         PSConInit_AHP real = G_ahp * e / tau_ahp * (ms/nS)
 
     input:
-        inh_spikes <- inhibitory spike
-        exc_spikes <- excitatory spike
+        inh_spikes <- spike
+        exc_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/iaf_cond_alpha_neuron.nestml

@@ -41,14 +41,14 @@ model iaf_cond_alpha_neuron:
         refr_t ms = 0 ms    # Refractory period timer
 
     equations:
-        kernel g_inh = (e/tau_syn_inh) * t * exp(-t/tau_syn_inh)
-        kernel g_exc = (e/tau_syn_exc) * t * exp(-t/tau_syn_exc)
+        kernel g_inh = (e / tau_syn_inh) * t * exp(-t / tau_syn_inh)
+        kernel g_exc = (e / tau_syn_exc) * t * exp(-t / tau_syn_exc)
 
-        inline I_syn_exc pA = convolve(g_exc, exc_spikes) * nS * ( V_m - E_exc )
-        inline I_syn_inh pA = convolve(g_inh, inh_spikes) * nS * ( V_m - E_inh )
-        inline I_leak pA = g_L * ( V_m - E_L )
+        inline I_syn_exc pA = convolve(g_exc, exc_spikes.weight) * (V_m - E_exc)
+        inline I_syn_inh pA = convolve(g_inh, inh_spikes.weight) * (V_m - E_inh)
+        inline I_leak pA = g_L * (V_m - E_L)
 
-        V_m' = ( -I_leak - I_syn_exc - I_syn_inh + I_e + I_stim ) / C_m
+        V_m' = (-I_leak - I_syn_exc - I_syn_inh + I_e + I_stim) / C_m
         refr_t' = -1e3 * ms/s    # refractoriness is implemented as an ODE, representing a timer counting back down to zero. XXX: TODO: This should simply read ``refr_t' = -1 / s`` (see https://github.com/nest/nestml/issues/984)
 
     parameters:
@@ -68,8 +68,8 @@ model iaf_cond_alpha_neuron:
         I_e pA = 0 pA
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike(weight nS)
+        inh_spikes <- spike(weight nS)
         I_stim pA <- continuous
 
     output:

models/neurons/iaf_cond_beta_neuron.nestml

@@ -100,8 +100,8 @@ model iaf_cond_beta_neuron:
         g_I_const real = 1 / (exp(-t_peak_I / tau_syn_decay_I) - exp(-t_peak_I / tau_syn_rise_I))
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/iaf_cond_exp_neuron.nestml

@@ -59,8 +59,8 @@ model iaf_cond_exp_neuron:
         I_e pA = 0 pA
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/iaf_cond_exp_sfa_rr_neuron.nestml

@@ -79,8 +79,8 @@ model iaf_cond_exp_sfa_rr_neuron:
         I_e pA = 0 pA
 
     input:
-        inh_spikes <- inhibitory spike
-        exc_spikes <- excitatory spike
+        inh_spikes <- spike
+        exc_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/iaf_psc_alpha_neuron.nestml

@@ -83,8 +83,8 @@ model iaf_psc_alpha_neuron:
         I_e pA = 0 pA
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/iaf_psc_exp_dend_neuron.nestml

@@ -63,8 +63,8 @@ model iaf_psc_exp_dend_neuron:
         I_e pA = 0 pA
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/iaf_psc_exp_htum_neuron.nestml

@@ -98,8 +98,8 @@ model iaf_psc_exp_htum_neuron:
         RefractoryCountsTot integer = steps(t_ref_tot) [[RefractoryCountsTot > 0]]
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        exc_spikes <- spike
+        inh_spikes <- spike
         I_stim pA <- continuous
 
     output:

models/neurons/iaf_psc_exp_neuron.nestml

@@ -80,13 +80,19 @@ model iaf_psc_exp_neuron:
         I_e pA = 0 pA
 
     input:
-        exc_spikes <- excitatory spike
-        inh_spikes <- inhibitory spike
+        spike_in_port <- spike(weight pA)
         I_stim pA <- continuous
 
     output:
         spike
 
+    onReceive(spike_in_port):
+        # route the incoming spike on the basis of the weight: less than zero means an inhibitory spike; greater than zero means an excitatory spike
+        if spike_in_port.weight > 0 pA:
+            I_syn_exc += spike_in_port.weight
+        else:
+            I_syn_inh -= spike_in_port.weight
+
     update:
         if refr_t > 0 ms:
             # neuron is absolute refractory, do not evolve V_m
@@ -95,12 +101,6 @@ model iaf_psc_exp_neuron:
             # neuron not refractory
             integrate_odes(I_syn_exc, I_syn_inh, V_m)
 
-    onReceive(exc_spikes):
-        I_syn_exc += exc_spikes * pA * s
-
-    onReceive(inh_spikes):
-        I_syn_inh += inh_spikes * pA * s
-
     onCondition(refr_t <= 0 ms and V_m >= V_th):
         # threshold crossing
         refr_t = refr_T    # start of the refractory period

models/neurons/izhikevich_psc_alpha_neuron.nestml

@@ -72,8 +72,8 @@ model izhikevich_psc_alpha_neuron:
         I_e pA = 0 pA
 
     input:
-        inh_spikes <- inhibitory spike
-        exc_spikes <- excitatory spike
+        inh_spikes <- spike
+        exc_spikes <- spike
         I_stim pA <- continuous
 
     output: