Merge branch 'master' into nest-server-mpi-logger

Author: babsey

bin/nest-server-mpi

@@ -77,5 +77,5 @@ else:
                 logger.error("Failed to execute call")
                 continue
 
-        log(call_name, f"sending response gather, data={response}")
-        comm.gather(nest.serializable(response), root=0)
+        log(call_name, f"sending reponse gather, data={response}")
+        comm.gather(nest.serialize_data(response), root=0)

models/aeif_cond_alpha.h

@@ -103,7 +103,14 @@ and
 For the reference implementation of this model, see
 `aeif_models_implementation <../model_details/aeif_models_implementation.ipynb>`_ notebook.
 
-See also [1]_.
+
+.. note::
+
+    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
+    Brette & Gerstner [1]_.  Thus, an ``aeif`` neuron with default parameters can fire multiple spikes in a single
+    time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
+
+    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
 
 Parameters
 ++++++++++

models/aeif_cond_alpha_astro.h

@@ -110,7 +110,14 @@ For implementation details of the adaptive exponential integrate-and-fire neuron
 model, see the
 `aeif_models_implementation <../model_details/aeif_models_implementation.ipynb>`_ notebook.
 
-See also [1]_.
+
+.. note::
+
+    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
+    Brette & Gerstner [1]_.  Thus, an ``aeif`` neuron with default parameters can fire multiple spikes in a single
+    time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
+
+    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
 
 Parameters
 ++++++++++

models/aeif_cond_alpha_multisynapse.h

@@ -96,6 +96,14 @@ When the neuron fires a spike, the adaptation current :math:`w <- w + b`.
 For implementation details see the
 `aeif_models_implementation <../model_details/aeif_models_implementation.ipynb>`_ notebook.
 
+.. note::
+
+    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
+    Brette & Gerstner [1]_.  Thus, an ``aeif`` neuron with default parameters can fire multiple spikes in a single
+    time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
+
+    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
+
 Parameters
 ++++++++++
 
@@ -155,6 +163,14 @@ Receives
 
 SpikeEvent, CurrentEvent, DataLoggingRequest
 
+References
+++++++++++
+
+.. [1] Brette R and Gerstner W (2005). Adaptive exponential
+       integrate-and-fire model as an effective description of neuronal
+       activity. Journal of Neurophysiology. 943637-3642
+       DOI: https://doi.org/10.1152/jn.00686.2005
+
 See also
 ++++++++
 

models/aeif_cond_beta_multisynapse.h

@@ -113,6 +113,14 @@ When the neuron fires a spike, the adaptation current `w <- w + b`.
 For implementation details see the
 `aeif_models_implementation <../model_details/aeif_models_implementation.ipynb>`_ notebook.
 
+.. note::
+
+    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
+    Brette & Gerstner [1]_.  Thus, an ``aeif`` neuron with default parameters can fire multiple spikes in a single
+    time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
+
+    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
+
 Parameters
 ++++++++++
 
@@ -172,6 +180,14 @@ Receives
 
 SpikeEvent, CurrentEvent, DataLoggingRequest
 
+References
+++++++++++
+
+.. [1] Brette R and Gerstner W (2005). Adaptive exponential
+       integrate-and-fire model as an effective description of neuronal
+       activity. Journal of Neurophysiology. 943637-3642
+       DOI: https://doi.org/10.1152/jn.00686.2005
+
 See also
 ++++++++
 

models/aeif_cond_exp.h

@@ -105,7 +105,14 @@ setting V_peak too high.
 For implementation details see the
 `aeif_models_implementation <../model_details/aeif_models_implementation.ipynb>`_ notebook.
 
-See also [1]_.
+
+.. note::
+
+    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
+    Brette & Gerstner [1]_.  Thus, an ``aeif`` neuron with default parameters can fire multiple spikes in a single
+    time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
+
+    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
 
 
 Parameters:

models/aeif_psc_alpha.h

@@ -95,7 +95,13 @@ Here :math:`H(t)` is the Heaviside step function and `k` indexes incoming spikes
 For implementation details see the
 `aeif_models_implementation <../model_details/aeif_models_implementation.ipynb>`_ notebook.
 
-See also [1]_.
+.. note::
+
+    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
+    Brette & Gerstner [1]_.  Thus, an ``aeif`` neuron with default parameters can fire multiple spikes in a single
+    time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
+
+    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
 
 Parameters
 ++++++++++

models/aeif_psc_delta.h

@@ -97,7 +97,13 @@ the value of `J` after a spike.
 For implementation details see the
 `aeif_models_implementation <../model_details/aeif_models_implementation.ipynb>`_ notebook.
 
-See also [1]_.
+.. note::
+
+    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
+    Brette & Gerstner [1]_.  Thus, an ``aeif`` neuron with default parameters can fire multiple spikes in a single
+    time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
+
+    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
 
 Parameters
 ++++++++++

models/aeif_psc_delta_clopath.h

@@ -93,6 +93,14 @@ For implementation details see the
 
 See also [2]_.
 
+.. note::
+
+    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
+    Brette & Gerstner [4]_.  Thus, an ``aeif`` neuron with default parameters can fire multiple spikes in a single
+    time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
+
+    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
+
 Parameters
 ++++++++++
 
@@ -190,6 +198,11 @@ References
 .. [3] Voltage-based STDP synapse (Clopath et al. 2010) on ModelDB
        https://modeldb.science/144566?tab=1
 
+.. [4] Brette R and Gerstner W (2005). Adaptive exponential
+       integrate-and-fire model as an effective description of neuronal
+       activity. Journal of Neurophysiology. 943637-3642
+       DOI: https://doi.org/10.1152/jn.00686.2005
+
 See also
 ++++++++
 

models/aeif_psc_exp.h

@@ -95,7 +95,13 @@ Here :math:`H(t)` is the Heaviside step function and `k` indexes incoming spikes
 For implementation details see the
 `aeif_models_implementation <../model_details/aeif_models_implementation.ipynb>`_ notebook.
 
-See also [1]_.
+.. note::
+
+    The default refractory period for ``aeif`` models is zero, consistent with the model definition in
+    Brette & Gerstner [1]_.  Thus, an ``aeif`` neuron with default parameters can fire multiple spikes in a single
+    time step, which can lead to exploding spike numbers and extreme slow-down of simulations.
+
+    To avoid such unphysiological behavior, you should set a refractory time ``t_ref > 0``.
 
 Parameters
 ++++++++++

pynest/examples/EI_clustered_network/network_EI.py

@@ -147,14 +147,18 @@ def create_populations(self):
             "t_ref": self._params["t_ref"],
             "V_th": self._params["V_th_E"],
             "V_reset": self._params["V_r"],
-            "I_e": I_xE
-            if self._params["delta_I_xE"] == 0
-            else I_xE * nest.random.uniform(1 - self._params["delta_I_xE"] / 2, 1 + self._params["delta_I_xE"] / 2),
+            "I_e": (
+                I_xE
+                if self._params["delta_I_xE"] == 0
+                else I_xE * nest.random.uniform(1 - self._params["delta_I_xE"] / 2, 1 + self._params["delta_I_xE"] / 2)
+            ),
             "tau_syn_ex": self._params["tau_syn_ex"],
             "tau_syn_in": self._params["tau_syn_in"],
-            "V_m": self._params["V_m"]
-            if not self._params["V_m"] == "rand"
-            else self._params["V_th_E"] - 20 * nest.random.lognormal(0, 1),
+            "V_m": (
+                self._params["V_m"]
+                if not self._params["V_m"] == "rand"
+                else self._params["V_th_E"] - 20 * nest.random.lognormal(0, 1)
+            ),
         }
         I_neuron_params = {
             "E_L": self._params["E_L"],
@@ -163,14 +167,18 @@ def create_populations(self):
             "t_ref": self._params["t_ref"],
             "V_th": self._params["V_th_I"],
             "V_reset": self._params["V_r"],
-            "I_e": I_xI
-            if self._params["delta_I_xE"] == 0
-            else I_xI * nest.random.uniform(1 - self._params["delta_I_xE"] / 2, 1 + self._params["delta_I_xE"] / 2),
+            "I_e": (
+                I_xI
+                if self._params["delta_I_xE"] == 0
+                else I_xI * nest.random.uniform(1 - self._params["delta_I_xE"] / 2, 1 + self._params["delta_I_xE"] / 2)
+            ),
             "tau_syn_ex": self._params["tau_syn_ex"],
             "tau_syn_in": self._params["tau_syn_in"],
-            "V_m": self._params["V_m"]
-            if not self._params["V_m"] == "rand"
-            else self._params["V_th_I"] - 20 * nest.random.lognormal(0, 1),
+            "V_m": (
+                self._params["V_m"]
+                if not self._params["V_m"] == "rand"
+                else self._params["V_th_I"] - 20 * nest.random.lognormal(0, 1)
+            ),
         }
 
         # iaf_psc_exp allows stochasticity, if not used - don't supply the parameters and use

pynest/examples/lin_rate_ipn_network.py

@@ -74,7 +74,7 @@
     # time constant of neuronal dynamics in ms
     "mu": 2.0,
     # mean input
-    "sigma": 5.0
+    "sigma": 5.0,
     # noise parameter
 }
 

testsuite/pytests/test_erfc_neuron.py

@@ -71,7 +71,6 @@ def activation_function_theory(sigma, theta):
 
 
 class ErfcNeuronTheoryTestCase(unittest.TestCase):
-
     """Compare results to theoretical predictions"""
 
     def setUp(self):

testsuite/pytests/test_rate_copy_model.py

@@ -27,7 +27,6 @@
 
 @nest.ll_api.check_stack
 class RateCopyModelTestCase(unittest.TestCase):
-
     """
     Test whether a rate connection created by copy model behaves
     identical to the original version

testsuite/pytests/test_rate_instantaneous_and_delayed.py

@@ -27,7 +27,6 @@
 
 @nest.ll_api.check_stack
 class RateInstantaneousAndDelayedTestCase(unittest.TestCase):
-
     """
     Test whether delayed rate connections have same properties as
     instantaneous connections but with the correct delay

testsuite/pytests/test_rate_neuron.py

@@ -33,7 +33,6 @@
 
 @nest.ll_api.check_stack
 class RateNeuronTestCase(unittest.TestCase):
-
     """Check rate_neuron"""
 
     def setUp(self):

testsuite/pytests/test_rate_neuron_communication.py

@@ -34,7 +34,6 @@ def H(x):
 
 @nest.ll_api.check_stack
 class RateNeuronCommunicationTestCase(unittest.TestCase):
-
     """Check rate_neuron"""
 
     def setUp(self):

testsuite/pytests/test_sp/test_growth_curves.py

@@ -217,7 +217,6 @@ def growth_curve(self, t):
 
 
 class SigmoidNumericSEI(SynapticElementIntegrator):
-
     """
     Compute the number of synaptic element corresponding to a
     sigmoid growth curve

testsuite/pytests/test_step_rate_generator.py

@@ -27,7 +27,6 @@
 
 @nest.ll_api.check_stack
 class StepRateGeneratorTestCase(unittest.TestCase):
-
     """
     Test whether the step_rate_generator produces and
     communicates the desired rates

testsuite/pytests/test_vogels_sprekeler_synapse.py

@@ -29,7 +29,6 @@
 
 @nest.ll_api.check_stack
 class VogelsSprekelerConnectionTestCase(unittest.TestCase):
-
     """Check vogels_sprekeler_synapse model properties."""
 
     def setUp(self):