add timestep() predefined function akin to resolution()

models/neurons/iaf_psc_delta_fixed_timestep_neuron.nestml

@@ -0,0 +1,98 @@
+"""
+iaf_psc_delta_fixed_timestep - Current-based leaky integrate-and-fire neuron model with delta-kernel post-synaptic currents
+###########################################################################################################################
+
+Description
++++++++++++
+
+iaf_psc_delta is an implementation of a leaky integrate-and-fire model
+where the potential jumps on each spike arrival.
+
+The threshold crossing is followed by an absolute refractory period
+during which the membrane potential is clamped to the resting potential.
+
+Spikes arriving while the neuron is refractory, are discarded by
+default. If the property ``with_refr_input`` is set to true, such
+spikes are added to the membrane potential at the end of the
+refractory period, dampened according to the interval between
+arrival and end of refractoriness.
+
+The general framework for the consistent formulation of systems with
+neuron like dynamics interacting by point events is described in
+[1]_. A flow chart can be found in [2]_.
+
+This model differs from ``iaf_psc_delta_fixed_timestep`` in that it
+assumes a fixed-timestep simulator, so the function ``resolution()``
+can be used.
+
+
+References
+++++++++++
+
+.. [1] Rotter S, Diesmann M (1999). Exact simulation of
+ time-invariant linear systems with applications to neuronal
+ modeling. Biologial Cybernetics 81:381-402.
+ DOI: https://doi.org/10.1007/s004220050570
+.. [2] Diesmann M, Gewaltig M-O, Rotter S, & Aertsen A (2001). State
+ space analysis of synchronous spiking in cortical neural
+ networks. Neurocomputing 38-40:565-571.
+ DOI: https://doi.org/10.1016/S0925-2312(01)00409-X
+
+
+See also
+++++++++
+
+iaf_psc_alpha, iaf_psc_exp
+"""
+model iaf_psc_delta_fixed_timestep_neuron:
+ state:
+ V_m mV = E_L # Membrane potential
+ refr_t ms = 0 ms # Refractory period timer
+ is_refractory boolean = false
+
+ equations:
+ kernel K_delta = delta(t)
+ V_m' = -(V_m - E_L) / tau_m + convolve(K_delta, spikes) * (mV / ms) + (I_e + I_stim) / C_m
+
+ parameters:
+ tau_m ms = 10 ms # Membrane time constant
+ C_m pF = 250 pF # Capacity of the membrane
+ refr_T ms = 2 ms # Duration of refractory period
+ tau_syn ms = 2 ms # Time constant of synaptic current
+ E_L mV = -70 mV # Resting membrane potential
+ V_reset mV = -70 mV # Reset potential of the membrane
+ V_th mV = -55 mV # Spike threshold
+ V_min mV = -inf * 1 mV # Absolute lower value for the membrane potential
+
+ # constant external input current
+ I_e pA = 0 pA
+
+ input:
+ spikes <- spike
+ I_stim pA <- continuous
+
+ output:
+ spike
+
+ update:
+ if is_refractory:
+ # neuron is absolute refractory, do not evolve V_m
+ refr_t -= resolution()
+ else:
+ # neuron not refractory, so evolve all ODEs (including V_m)
+ integrate_odes()
+
+ onCondition(not is_refractory and V_m >= V_th):
+ # threshold crossing
+ V_m = V_reset
+
+ if refr_T > 0 ms:
+ refr_t = refr_T # start of the refractory period
+ is_refractory = true
+
+ emit_spike()
+
+ onCondition(is_refractory and refr_t <= resolution() / 2):
+ # end of refractory period
+ refr_t = 0 ms
+ is_refractory = false

tests/nest_tests/stdp_triplet_synapse_test.py

@@ -44,7 +44,7 @@
 def nestml_generate_target():
  r"""Generate the neuron model code"""
 
- files = [os.path.join("models", "neurons", "iaf_psc_delta_neuron.nestml"),
+ files = [os.path.join("models", "neurons", "iaf_psc_delta_fixed_timestep_neuron.nestml"),
  os.path.join("models", "synapses", "stdp_triplet_synapse.nestml")]
  input_path = [os.path.realpath(os.path.join(os.path.dirname(__file__), os.path.join(
  os.pardir, os.pardir, s))) for s in files]
@@ -53,7 +53,7 @@ def nestml_generate_target():
  suffix="_nestml",
  codegen_opts={"neuron_parent_class": "StructuralPlasticityNode",
  "neuron_parent_class_include": "structural_plasticity_node.h",
- "neuron_synapse_pairs": [{"neuron": "iaf_psc_delta_neuron",
+ "neuron_synapse_pairs": [{"neuron": "iaf_psc_delta_fixed_timestep_neuron",
  "synapse": "stdp_triplet_synapse",
  "post_ports": ["post_spikes"]}],
  "delay_variable": {"stdp_triplet_synapse": "d"},
@@ -365,11 +365,11 @@ def _test_stdp_triplet_synapse(delay, spike_times_len):
  }
 
  if experiment == "test_nestml_pair_synapse":
- neuron_model_name = "iaf_psc_delta_neuron_nestml__with_stdp_triplet_synapse_nestml"
+ neuron_model_name = "iaf_psc_delta_fixed_timestep_neuron_nestml__with_stdp_triplet_synapse_nestml"
  neuron_opts = {"tau_minus__for_stdp_triplet_synapse_nestml": syn_opts["tau_minus"],
  "tau_y__for_stdp_triplet_synapse_nestml": syn_opts["tau_y"]}
 
- synapse_model_name = "stdp_triplet_synapse_nestml__with_iaf_psc_delta_neuron_nestml"
+ synapse_model_name = "stdp_triplet_synapse_nestml__with_iaf_psc_delta_fixed_timestep_neuron_nestml"
  nest_syn_opts = {"d": delay}
  nest_syn_opts.update(syn_opts)
  nest_syn_opts.pop("tau_minus") # these have been moved to the neuron