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Port Tsodyks tests from SLI to Python #2683

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Apr 25, 2023
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Port Tsodyks tests from SLI to Python #2683

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83225cd
refactor SLI test to Python test
Apr 24, 2023
d124bba
refactor SLI test to Python test
Apr 25, 2023
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263 changes: 263 additions & 0 deletions testsuite/pytests/test_tsodyks_synapse.py
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# -*- coding: utf-8 -*-
#
# test_tsodyks_synapse.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# NEST is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with NEST. If not, see <http://www.gnu.org/licenses/>.

import nest
import numpy as np
import pytest


@pytest.mark.parametrize("synapse_model", ["tsodyks_synapse", "tsodyks_synapse_hom"])
class TestTsodyksSynapse:
"""Test of Tsodyks synapse model

Test Tsodyks short term plasticity depressing synapses according to:
'Neural Networks with Dynamic Synapses', Misha Tsodyks, Klaus Pawelzik, Henry Markram
Neural computation 10, 821--853 (1998)
"""

def test_tsodyks_depressing(self, synapse_model):
"""Test depression dynamics. Reproduces figure 1 A"""
h = 0.1

# Model parameters
Tau = 40.0 # membrane time constant
Theta = 15.0 # threshold
E_L = 0.0 # reset potential of Vm
R = 1.0
C = Tau / R # Tau [ms] / 1.0 GOhm in NEST units
TauR = 2.0 # refractory time
Tau_psc = 3.0 # time constant of PSC (=Tau_inact)
Tau_rec = 800.0 # recovery time
Tau_fac = 0.0 # facilitation time: 0 == no facilitation
U = 0.5 # facilitation parameter U
A = 250.0 # PSC weight in pA

input_train = np.arange(98., # first spike [ms]
1048.1, # last spike [ms]
50.) # interspike interval [ms]

T_sim = 1200. # simulation time [ms]

nest.ResetKernel()
nest.resolution = h

sg = nest.Create("spike_generator", {"spike_times": input_train})
pn = nest.Create("parrot_neuron")

neuron = nest.Create("iaf_psc_exp_htum", params={"tau_m": Tau,
"t_ref_tot": TauR,
"t_ref_abs": TauR,
"tau_syn_ex": Tau_psc,
"tau_syn_in": Tau_psc,
"C_m": C,
"V_th": Theta,
"V_reset": E_L,
"E_L": E_L,
"V_m": E_L})

vm = nest.Create("voltmeter")
vm.interval = 25.

nest.SetDefaults(synapse_model, {"tau_psc": Tau_psc,
"tau_rec": Tau_rec,
"tau_fac": Tau_fac,
"U": U,
"delay": .1,
"weight": A,
"u": 0.0,
"x": 1.0})

nest.Connect(sg, pn) # pn required, devices cannot project to plastic synapses
nest.Connect(pn, neuron, syn_spec={"synapse_model": synapse_model})
nest.Connect(vm, neuron)

nest.Simulate(T_sim)

# compare results to expected voltage trace
times_vm_expected = np.array([[25, 0],
[50, 0],
[75, 0],
[100, 2.401350000000000e+00],
[125, 5.302440000000000e+00],
[150, 4.108330000000000e+00],
[175, 4.322170000000000e+00],
[200, 3.053390000000000e+00],
[225, 2.871240000000000e+00],
[250, 2.028640000000000e+00],
[275, 1.908020000000000e+00],
[300, 1.396960000000000e+00],
[325, 1.375850000000000e+00],
[350, 1.057780000000000e+00],
[375, 1.103490000000000e+00],
[400, 8.865690000000001e-01],
[425, 9.693530000000000e-01],
[450, 8.028760000000000e-01],
[475, 9.046710000000000e-01],
[500, 7.626880000000000e-01],
[525, 8.738550000000000e-01],
[550, 7.435880000000000e-01],
[575, 8.592780000000000e-01],
[600, 7.345670000000000e-01],
[625, 8.524119999999999e-01],
[650, 7.303210000000000e-01],
[675, 8.491860000000000e-01],
[700, 7.283280000000000e-01],
[725, 8.476730000000000e-01],
[750, 7.273930000000000e-01],
[775, 8.469640000000001e-01],
[800, 7.269550000000000e-01],
[825, 8.466320000000001e-01],
[850, 7.267500000000000e-01],
[875, 8.464760000000000e-01],
[900, 7.266540000000000e-01],
[925, 8.464030000000000e-01],
[950, 7.266089999999999e-01],
[975, 8.463690000000000e-01],
[1000, 7.265880000000000e-01],
[1025, 8.463530000000000e-01],
[1050, 7.265779999999999e-01],
[1075, 8.463460000000000e-01],
[1100, 4.531260000000000e-01],
[1125, 2.425410000000000e-01],
[1150, 1.298230000000000e-01],
[1175, 6.948920000000000e-02]])

times_vm_sim = np.vstack([vm.get("events")["times"], vm.get("events")["V_m"]]).T

# test uses large atol due to finite precision of reference timeseries
np.testing.assert_allclose(times_vm_sim, times_vm_expected, atol=1E-5)

def test_tsodyks_facilitating(self, synapse_model):
"""Test depression dynamics. Reproduces figure 1 B, (C)"""
# Model parameters

h = 0.1

Tau = 60.0 # membrane time constant
Theta = 15.0 # threshold
E_L = 0.0 # reset potential of Vm
R = 1.0
C = Tau / R # Tau [ms] / 1.0 GOhm in NEST units
TauR = 2.0 # refractory time
Tau_psc = 1.5 # time constant of PSC (=Tau_inact)
Tau_rec = 130.0 # recovery time
Tau_fac = 530.0 # facilitation time
U = 0.03 # facilitation parameter U
A = 1540.0 # PSC weight in pA

input_train = np.arange(98.1, # first spike [ms]
1051., # last spike [ms]
50.1) # interspike interval [ms]

T_sim = 1200. # simulation time [ms]

nest.ResetKernel()

# set resolution and limits on delays
# limits must be set BEFORE connecting any elements
nest.SetKernelStatus({"resolution": h})

sg = nest.Create("spike_generator", {"spike_times": input_train})
pn = nest.Create("parrot_neuron")

neuron = nest.Create("iaf_psc_exp_htum")
neuron.tau_m = Tau
neuron.t_ref_tot = TauR
neuron.t_ref_abs = TauR
neuron.tau_syn_ex = Tau_psc
neuron.tau_syn_in = Tau_psc
neuron.C_m = C
neuron.V_th = Theta
neuron.V_reset = E_L
neuron.E_L = E_L
neuron.V_m = E_L

vm = nest.Create("voltmeter")
vm.interval = 25.

nest.SetDefaults(synapse_model, {"tau_psc": Tau_psc,
"tau_rec": Tau_rec,
"tau_fac": Tau_fac,
"U": U,
"delay": .1,
"weight": A,
"u": 0.0,
"x": 1.0})

nest.Connect(sg, pn) # pn required, devices cannot project to plastic synapses
nest.Connect(pn, neuron, syn_spec={"synapse_model": synapse_model})
nest.Connect(vm, neuron)

nest.Simulate(T_sim)

# compare results to expected voltage trace
times_vm_expected = np.array([[25, 0],
[50, 0],
[75, 0],
[100, 4.739750000000000e-01],
[125, 7.706030000000000e-01],
[150, 1.297120000000000e+00],
[175, 1.757990000000000e+00],
[200, 2.114420000000000e+00],
[225, 2.714490000000000e+00],
[250, 2.785530000000000e+00],
[275, 3.546150000000000e+00],
[300, 3.272710000000000e+00],
[325, 4.233240000000000e+00],
[350, 3.583070000000000e+00],
[375, 4.788130000000000e+00],
[400, 3.739560000000000e+00],
[425, 5.233850000000000e+00],
[450, 3.767230000000000e+00],
[475, 5.593890000000000e+00],
[500, 3.687720000000000e+00],
[525, 5.888270000000000e+00],
[550, 3.881790000000000e+00],
[575, 6.132580000000000e+00],
[600, 4.042850000000000e+00],
[625, 6.338430000000000e+00],
[650, 4.178550000000000e+00],
[675, 6.514250000000000e+00],
[700, 4.294460000000000e+00],
[725, 6.666160000000000e+00],
[750, 4.394600000000000e+00],
[775, 6.798620000000000e+00],
[800, 4.481930000000000e+00],
[825, 6.914990000000000e+00],
[850, 4.558640000000000e+00],
[875, 7.017830000000000e+00],
[900, 4.626440000000000e+00],
[925, 7.109160000000000e+00],
[950, 4.686650000000000e+00],
[975, 7.190620000000000e+00],
[1000, 4.740350000000000e+00],
[1025, 7.263560000000000e+00],
[1050, 4.788430000000000e+00],
[1075, 7.329110000000000e+00],
[1100, 4.831650000000000e+00],
[1125, 3.185220000000000e+00],
[1150, 2.099830000000000e+00],
[1175, 1.384290000000000e+00]])

times_vm_sim = np.vstack([vm.get("events")["times"], vm.get("events")["V_m"]]).T

# test uses large atol due to finite precision of reference timeseries
np.testing.assert_allclose(times_vm_sim, times_vm_expected, atol=1E-5)
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