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C.A.P. Linssen
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Jul 5, 2023
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""" | ||
""" | ||
synapse clopath_synapse: | ||
state: | ||
w real = 1. @nest::weight # Synaptic weight | ||
pre_trace real = 0. | ||
post_membrane_potential_avg_plus mV = -70 mV | ||
post_membrane_potential_avg_minus mV = -70 mV | ||
post_membrane_avg_avg mV = -70 mV | ||
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parameters: | ||
d ms = 1 ms @nest::delay # Synaptic transmission delay | ||
w_max real = 100 | ||
tau_post_membrane_avg_plus ms = 7 ms | ||
tau_post_membrane_avg_minus ms = 10 ms | ||
tau_post_membrane_avg_avg ms = 500 ms | ||
tau_pre_tr ms = 15 ms | ||
theta_minus mV = -70.6 mV | ||
theta_plus mV = -45.3 mV # should be greater than theta_minus | ||
A_LTD real = 14.0e-5 | ||
A_LTP real = 8.0e-5 | ||
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equations: | ||
pre_trace' = -pre_trace / tau_pre_tr | ||
post_membrane_potential_avg_plus' = (-post_membrane_potential_avg_plus + post_membrane_potential) / tau_post_membrane_avg_plus | ||
post_membrane_potential_avg_minus' = (-post_membrane_potential_avg_minus + post_membrane_potential) / tau_post_membrane_avg_minus | ||
post_membrane_avg_avg' = (-post_membrane_avg_avg + post_membrane_potential_avg_minus) / tau_post_membrane_avg_avg | ||
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input: | ||
pre_spikes real <- spike | ||
post_spikes real <- spike | ||
post_membrane_potential mV <- continuous | ||
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output: | ||
spike | ||
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onReceive(post_spikes): | ||
if post_membrane_potential > theta_plus and post_membrane_potential_avg_plus > theta_minus: | ||
# potentiate synapse | ||
# w += A_LTP * pre_trace * (post_membrane_potential - theta_plus) * (post_membrane_potential_avg_plus(t - membrane_potential_delay) - theta_minus) | ||
w += A_LTP * pre_trace * (post_membrane_potential - theta_plus) * (post_membrane_potential_avg_plus - theta_minus) | ||
w = min(w, w_max) | ||
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onReceive(pre_spikes): | ||
pre_trace += 1 / tau_pre_tr | ||
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if post_membrane_potential_avg_minus > theta_minus: | ||
# depress synapse | ||
#w -= A_LTD * (post_membrane_potential_avg_minus(t - membrane_potential_delay) - theta_minus) | ||
w -= A_LTD * (post_membrane_potential_avg_minus - theta_minus) | ||
w = max(w, 0) | ||
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# deliver spike to postsynaptic partner | ||
deliver_spike(w, d) |
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# -*- coding: utf-8 -*- | ||
# | ||
# clopath_synapse_test.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/>. | ||
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import numpy as np | ||
import os | ||
import pytest | ||
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import nest | ||
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from pynestml.codegeneration.nest_tools import NESTTools | ||
from pynestml.frontend.pynestml_frontend import generate_nest_target | ||
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try: | ||
import matplotlib | ||
matplotlib.use("Agg") | ||
import matplotlib.ticker | ||
import matplotlib.pyplot as plt | ||
TEST_PLOTS = True | ||
except Exception: | ||
TEST_PLOTS = False | ||
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sim_mdl = True | ||
sim_ref = True | ||
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class TestClopathSynapse: | ||
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neuron_model_name = "iaf_psc_exp_nestml__with_clopath_nestml" | ||
ref_neuron_model_name = "iaf_psc_exp_nestml_non_jit" | ||
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synapse_model_name = "clopath_nestml__with_iaf_psc_exp_nestml" | ||
ref_synapse_model_name = "clopath_synapse" | ||
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@pytest.fixture(scope="module", autouse=True) | ||
def setUp(self): | ||
"""Generate the model code""" | ||
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jit_codegen_opts = {"neuron_synapse_pairs": [{"neuron": "iaf_psc_exp", | ||
"synapse": "clopath_synapse", | ||
"post_ports": ["post_spikes", | ||
"post_membrane_potential", "V_m"]}]} | ||
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files = [os.path.join("models", "neurons", "iaf_psc_exp.nestml"), | ||
os.path.join("models", "synapses", "clopath_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] | ||
generate_nest_target(input_path=input_path, | ||
logging_level="DEBUG", | ||
suffix="_nestml", | ||
codegen_opts=jit_codegen_opts) | ||
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def test_nest_clopath_synapse(self): | ||
fname_snip = "" | ||
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pre_spike_times = [1., 11., 21.] # [ms] | ||
post_spike_times = [6., 16., 26.] # [ms] | ||
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post_spike_times = np.sort(np.unique(1 + np.round(10 * np.sort(np.abs(np.random.randn(10)))))) # [ms] | ||
pre_spike_times = np.sort(np.unique(1 + np.round(10 * np.sort(np.abs(np.random.randn(10)))))) # [ms] | ||
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post_spike_times = np.sort(np.unique(1 + np.round(100 * np.sort(np.abs(np.random.randn(100)))))) # [ms] | ||
pre_spike_times = np.sort(np.unique(1 + np.round(100 * np.sort(np.abs(np.random.randn(100)))))) # [ms] | ||
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pre_spike_times = np.array([2., 4., 7., 8., 12., 13., 19., 23., 24., 28., 29., 30., 33., 34., | ||
35., 36., 38., 40., 42., 46., 51., 53., 54., 55., 56., 59., 63., 64., | ||
65., 66., 68., 72., 73., 76., 79., 80., 83., 84., 86., 87., 90., 95., | ||
99., 100., 103., 104., 105., 111., 112., 126., 131., 133., 134., 139., 147., 150., | ||
152., 155., 172., 175., 176., 181., 196., 197., 199., 202., 213., 215., 217., 265.]) | ||
post_spike_times = np.array([4., 5., 6., 7., 10., 11., 12., 16., 17., 18., 19., 20., 22., 23., | ||
25., 27., 29., 30., 31., 32., 34., 36., 37., 38., 39., 42., 44., 46., | ||
48., 49., 50., 54., 56., 57., 59., 60., 61., 62., 67., 74., 76., 79., | ||
80., 81., 83., 88., 93., 94., 97., 99., 100., 105., 111., 113., 114., 115., | ||
116., 119., 123., 130., 132., 134., 135., 145., 152., 155., 158., 166., 172., 174., | ||
188., 194., 202., 245., 249., 289., 454.]) | ||
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self.run_synapse_test(neuron_model_name=self.neuron_model_name, | ||
ref_neuron_model_name=self.ref_neuron_model_name, | ||
synapse_model_name=self.synapse_model_name, | ||
ref_synapse_model_name=self.ref_synapse_model_name, | ||
resolution=.5, # [ms] | ||
delay=1.5, # [ms] | ||
pre_spike_times=pre_spike_times, | ||
post_spike_times=post_spike_times, | ||
fname_snip=fname_snip) | ||
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def run_synapse_test(self, neuron_model_name, | ||
ref_neuron_model_name, | ||
synapse_model_name, | ||
ref_synapse_model_name, | ||
resolution=1., # [ms] | ||
delay=1., # [ms] | ||
sim_time=None, # if None, computed from pre and post spike times | ||
pre_spike_times=None, | ||
post_spike_times=None, | ||
fname_snip=""): | ||
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if pre_spike_times is None: | ||
pre_spike_times = [] | ||
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if post_spike_times is None: | ||
post_spike_times = [] | ||
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if sim_time is None: | ||
sim_time = max(np.amax(pre_spike_times), np.amax(post_spike_times)) + 5 * delay | ||
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nest.set_verbosity("M_ALL") | ||
nest.ResetKernel() | ||
nest.Install("nestml_jit_module") | ||
nest.Install("nestml_non_jit_module") | ||
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print("Pre spike times: " + str(pre_spike_times)) | ||
print("Post spike times: " + str(post_spike_times)) | ||
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# nest.set_verbosity("M_WARNING") | ||
nest.set_verbosity("M_ERROR") | ||
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post_weights = {"parrot": []} | ||
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nest.ResetKernel() | ||
nest.SetKernelStatus({"resolution": resolution}) | ||
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wr = nest.Create("weight_recorder") | ||
wr_ref = nest.Create("weight_recorder") | ||
nest.CopyModel(synapse_model_name, "clopath_nestml_rec", | ||
{"weight_recorder": wr[0], "w": 1., "d": 1., "receptor_type": 0}) | ||
nest.CopyModel(ref_synapse_model_name, "clopath_ref_rec", | ||
{"weight_recorder": wr_ref[0], "weight": 1., "delay": 1., "receptor_type": 0}) | ||
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# create spike_generators with these times | ||
pre_sg = nest.Create("spike_generator", | ||
params={"spike_times": pre_spike_times}) | ||
post_sg = nest.Create("spike_generator", | ||
params={"spike_times": post_spike_times, | ||
"allow_offgrid_times": True}) | ||
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# create parrot neurons and connect spike_generators | ||
if sim_mdl: | ||
pre_neuron = nest.Create("parrot_neuron") | ||
post_neuron = nest.Create(neuron_model_name) | ||
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if sim_ref: | ||
pre_neuron_ref = nest.Create("parrot_neuron") | ||
post_neuron_ref = nest.Create(ref_neuron_model_name) | ||
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if sim_mdl: | ||
if NESTTools.detect_nest_version().startswith("v2"): | ||
spikedet_pre = nest.Create("spike_detector") | ||
spikedet_post = nest.Create("spike_detector") | ||
else: | ||
spikedet_pre = nest.Create("spike_recorder") | ||
spikedet_post = nest.Create("spike_recorder") | ||
mm = nest.Create("multimeter", params={"record_from": [ | ||
"V_m", "post_trace__for_clopath_nestml"]}) | ||
if sim_ref: | ||
if NESTTools.detect_nest_version().startswith("v2"): | ||
spikedet_pre_ref = nest.Create("spike_detector") | ||
spikedet_post_ref = nest.Create("spike_detector") | ||
else: | ||
spikedet_pre_ref = nest.Create("spike_recorder") | ||
spikedet_post_ref = nest.Create("spike_recorder") | ||
mm_ref = nest.Create("multimeter", params={"record_from": ["V_m"]}) | ||
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if sim_mdl: | ||
nest.Connect(pre_sg, pre_neuron, "one_to_one", syn_spec={"delay": 1.}) | ||
nest.Connect(post_sg, post_neuron, "one_to_one", syn_spec={"delay": 1., "weight": 9999.}) | ||
if NESTTools.detect_nest_version().startswith("v2"): | ||
nest.Connect(pre_neuron, post_neuron, "all_to_all", syn_spec={"model": "clopath_nestml_rec"}) | ||
else: | ||
nest.Connect(pre_neuron, post_neuron, "all_to_all", syn_spec={"synapse_model": "clopath_nestml_rec"}) | ||
nest.Connect(mm, post_neuron) | ||
nest.Connect(pre_neuron, spikedet_pre) | ||
nest.Connect(post_neuron, spikedet_post) | ||
if sim_ref: | ||
nest.Connect(pre_sg, pre_neuron_ref, "one_to_one", syn_spec={"delay": 1.}) | ||
nest.Connect(post_sg, post_neuron_ref, "one_to_one", syn_spec={"delay": 1., "weight": 9999.}) | ||
if NESTTools.detect_nest_version().startswith("v2"): | ||
nest.Connect(pre_neuron_ref, post_neuron_ref, "all_to_all", | ||
syn_spec={"model": ref_synapse_model_name}) | ||
else: | ||
nest.Connect(pre_neuron_ref, post_neuron_ref, "all_to_all", | ||
syn_spec={"synapse_model": ref_synapse_model_name}) | ||
nest.Connect(mm_ref, post_neuron_ref) | ||
nest.Connect(pre_neuron_ref, spikedet_pre_ref) | ||
nest.Connect(post_neuron_ref, spikedet_post_ref) | ||
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# get Clopath synapse and weight before protocol | ||
if sim_mdl: | ||
syn = nest.GetConnections(source=pre_neuron, synapse_model="clopath_nestml_rec") | ||
if sim_ref: | ||
syn_ref = nest.GetConnections(source=pre_neuron_ref, synapse_model=ref_synapse_model_name) | ||
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n_steps = int(np.ceil(sim_time / resolution)) + 1 | ||
t = 0. | ||
t_hist = [] | ||
if sim_mdl: | ||
w_hist = [] | ||
if sim_ref: | ||
w_hist_ref = [] | ||
while t <= sim_time: | ||
nest.Simulate(resolution) | ||
t += resolution | ||
t_hist.append(t) | ||
if sim_ref: | ||
w_hist_ref.append(nest.GetStatus(syn_ref)[0]["weight"]) | ||
if sim_mdl: | ||
w_hist.append(nest.GetStatus(syn)[0]["w"]) | ||
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# plot | ||
if TEST_PLOTS: | ||
fig, ax = plt.subplots(nrows=2) | ||
ax1, ax2 = ax | ||
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if sim_mdl: | ||
timevec = nest.GetStatus(mm, "events")[0]["times"] | ||
V_m = nest.GetStatus(mm, "events")[0]["V_m"] | ||
ax2.plot(timevec, nest.GetStatus(mm, "events")[0]["post_trace__for_clopath_nestml"], label="post_tr nestml") | ||
ax1.plot(timevec, V_m, label="nestml", alpha=.7, linestyle=":") | ||
if sim_ref: | ||
pre_ref_spike_times_ = nest.GetStatus(spikedet_pre_ref, "events")[0]["times"] | ||
timevec = nest.GetStatus(mm_ref, "events")[0]["times"] | ||
V_m = nest.GetStatus(mm_ref, "events")[0]["V_m"] | ||
ax1.plot(timevec, V_m, label="nest ref", alpha=.7) | ||
ax1.set_ylabel("V_m") | ||
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for _ax in ax: | ||
_ax.grid(which="major", axis="both") | ||
_ax.grid(which="minor", axis="x", linestyle=":", alpha=.4) | ||
# _ax.minorticks_on() | ||
_ax.set_xlim(0., sim_time) | ||
_ax.legend() | ||
fig.savefig("/tmp/clopath_synapse_test" + fname_snip + "_V_m.png", dpi=300) | ||
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# plot | ||
if TEST_PLOTS: | ||
fig, ax = plt.subplots(nrows=3) | ||
ax1, ax2, ax3 = ax | ||
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if sim_mdl: | ||
pre_spike_times_ = nest.GetStatus(spikedet_pre, "events")[0]["times"] | ||
print("Actual pre spike times: " + str(pre_spike_times_)) | ||
if sim_ref: | ||
pre_ref_spike_times_ = nest.GetStatus(spikedet_pre_ref, "events")[0]["times"] | ||
print("Actual pre ref spike times: " + str(pre_ref_spike_times_)) | ||
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if sim_mdl: | ||
n_spikes = len(pre_spike_times_) | ||
for i in range(n_spikes): | ||
if i == 0: | ||
_lbl = "nestml" | ||
else: | ||
_lbl = None | ||
ax1.plot(2 * [pre_spike_times_[i] + delay], [0, 1], linewidth=2, color="blue", alpha=.4, label=_lbl) | ||
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if sim_mdl: | ||
post_spike_times_ = nest.GetStatus(spikedet_post, "events")[0]["times"] | ||
print("Actual post spike times: " + str(post_spike_times_)) | ||
if sim_ref: | ||
post_ref_spike_times_ = nest.GetStatus(spikedet_post_ref, "events")[0]["times"] | ||
print("Actual post ref spike times: " + str(post_ref_spike_times_)) | ||
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if sim_ref: | ||
n_spikes = len(pre_ref_spike_times_) | ||
for i in range(n_spikes): | ||
if i == 0: | ||
_lbl = "nest ref" | ||
else: | ||
_lbl = None | ||
ax1.plot(2 * [pre_ref_spike_times_[i] + delay], [0, 1], | ||
linewidth=2, color="cyan", label=_lbl, alpha=.4) | ||
ax1.set_ylabel("Pre spikes") | ||
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if sim_mdl: | ||
n_spikes = len(post_spike_times_) | ||
for i in range(n_spikes): | ||
if i == 0: | ||
_lbl = "nestml" | ||
else: | ||
_lbl = None | ||
ax2.plot(2 * [post_spike_times_[i]], [0, 1], linewidth=2, color="black", alpha=.4, label=_lbl) | ||
if sim_ref: | ||
n_spikes = len(post_ref_spike_times_) | ||
for i in range(n_spikes): | ||
if i == 0: | ||
_lbl = "nest ref" | ||
else: | ||
_lbl = None | ||
ax2.plot(2 * [post_ref_spike_times_[i]], [0, 1], linewidth=2, color="red", alpha=.4, label=_lbl) | ||
ax2.plot(timevec, nest.GetStatus(mm, "events")[0]["post_trace__for_clopath_nestml"], label="nestml post tr") | ||
ax2.set_ylabel("Post spikes") | ||
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if sim_mdl: | ||
ax3.plot(t_hist, w_hist, marker="o", label="nestml") | ||
if sim_ref: | ||
ax3.plot(t_hist, w_hist_ref, linestyle="--", marker="x", label="ref") | ||
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ax3.set_xlabel("Time [ms]") | ||
ax3.set_ylabel("w") | ||
for _ax in ax: | ||
_ax.grid(which="major", axis="both") | ||
_ax.xaxis.set_major_locator(matplotlib.ticker.FixedLocator(np.arange(0, np.ceil(sim_time)))) | ||
_ax.set_xlim(0., sim_time) | ||
_ax.legend() | ||
fig.savefig("/tmp/clopath_synapse_test" + fname_snip + ".png", dpi=300) | ||
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# verify | ||
MAX_ABS_ERROR = 1E-6 | ||
assert np.any(np.abs(np.array(w_hist) - 1) > MAX_ABS_ERROR), "No change in the weight!" | ||
assert np.all(np.abs(np.array(w_hist) - np.array(w_hist_ref)) < MAX_ABS_ERROR), \ | ||
"Difference between NESTML model and reference model!" |