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Testing and fix for 'round_robin_halt policy'; 'expsyn_curr' mechanism
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| Original file line number | Diff line number | Diff line change |
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| : Exponential current-based synapse | ||
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| NEURON { | ||
| POINT_PROCESS expsyn_curr | ||
| RANGE w, tau, R_mem | ||
| NONSPECIFIC_CURRENT I | ||
| } | ||
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| UNITS { | ||
| (ms) = (milliseconds) | ||
| (mV) = (millivolt) | ||
| (MOhm) = (megaohm) | ||
| } | ||
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| PARAMETER { | ||
| R_mem = 10.0 (MOhm) : membrane resistance | ||
| tau = 5.0 (ms) : synaptic time constant | ||
| w = 4.20075 (mV) : weight | ||
| } | ||
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| STATE { | ||
| g (mV) : instantaneous synaptic conductance | ||
| } | ||
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| INITIAL { | ||
| I = 0 | ||
| g = 0 | ||
| } | ||
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| BREAKPOINT { | ||
| :SOLVE state METHOD cnexp | ||
| SOLVE state METHOD sparse : to match with expsyn_curr_calcium_plasticity | ||
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| I = -g / R_mem | ||
| } | ||
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| DERIVATIVE state { | ||
| : Exponential decay of postsynaptic potential | ||
| g' = -g / tau | ||
| } | ||
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| NET_RECEIVE(weight) { | ||
| : Start of postsynaptic potential | ||
| g = g + w | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,200 @@ | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # test_multiple_connections.py | ||
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| import unittest | ||
| import types | ||
| import numpy as np | ||
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| import arbor as arb | ||
| from .. import fixtures | ||
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| """ | ||
| tests for multiple excitatory and inhibitory connections onto the same postsynaptic label and for one connection that has the same net impact as the multiple-connection paradigm, | ||
| thereby testing the selection policies 'round_robin', 'round_robin_halt', and 'univalent' (in principle testing if the right instances of mechanisms are used) | ||
| """ | ||
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| class TestMultipleConnections(unittest.TestCase): | ||
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| @fixtures.context | ||
| @fixtures.art_spiker_recipe | ||
| @fixtures.sum_weight_hh_spike | ||
| def test_multiple_connections(self, context, art_spiker_recipe, sum_weight_hh_spike): | ||
| runtime = 2 # ms | ||
| dt = 0.01 # ms | ||
| weight = sum_weight_hh_spike # connection strength which is just small enough so that all connections (exc. and inh.) from neuron 0 added up will evoke an immediate spike | ||
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| # define new method 'connections_on()' and overwrite the original one in the 'art_spiker_recipe' object | ||
| def connections_on(self, gid): | ||
| # incoming to neurons 0--2 | ||
| if gid < 3: | ||
| return [] | ||
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| # incoming to neuron 3 | ||
| elif gid == 3: | ||
| source_label_0 = arb.cell_global_label(0, "spike_source") # referring to the "spike_source" label of neuron 0 | ||
| source_label_1 = arb.cell_global_label(1, "spike_source") # referring to the "spike_source" label of neuron 1 | ||
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| target_label_rr_halt = arb.cell_local_label("postsyn_target", arb.selection_policy.round_robin_halt) # referring to the current item in the "postsyn_target" label group of neuron 3 | ||
| target_label_rr = arb.cell_local_label("postsyn_target", arb.selection_policy.round_robin) # referring to the current item in the "postsyn_target" label group of neuron 3, moving to the next item afterwards | ||
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| conn_0_3_n1 = arb.connection(source_label_0, target_label_rr_halt, 1, 0.2) # first (exc.) connection from neuron 0 to 3 | ||
| conn_0_3_n2 = arb.connection(source_label_0, target_label_rr_halt, 1, 0.2) # second (exc.) connection from neuron 0 to 3 | ||
| conn_0_3_n3 = arb.connection(source_label_0, target_label_rr, 1, 0.2) # third (exc.) connection from neuron 0 to 3 | ||
| conn_1_3_n1 = arb.connection(source_label_1, target_label_rr_halt, 1, 0.6) # first (inh.) connection from neuron 1 to 3 | ||
| conn_1_3_n2 = arb.connection(source_label_1, target_label_rr, 1, 0.6) # second (inh.) connection from neuron 1 to 3 | ||
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| return [conn_0_3_n1, conn_0_3_n2, conn_0_3_n3, conn_1_3_n1, conn_1_3_n2] | ||
| art_spiker_recipe.connections_on = types.MethodType(connections_on, art_spiker_recipe) | ||
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| # define new method 'cell_description()' and overwrite the original one in the 'art_spiker_recipe' object | ||
| def cell_description(self, gid): | ||
| # spike source neuron | ||
| if gid < 3: | ||
| return arb.spike_source_cell("spike_source", arb.explicit_schedule(self.trains[gid])) | ||
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| # spike-receiving cable neuron | ||
| elif gid == 3: | ||
| tree, labels, decor = self._cable_cell_elements() | ||
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| syn_mechanism1 = arb.mechanism("expsyn_curr") | ||
| syn_mechanism1.set('w', weight) # set weight for excitation | ||
| syn_mechanism1.set("tau", dt) # set minimal decay time | ||
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| syn_mechanism2 = arb.mechanism("expsyn_curr") | ||
| syn_mechanism2.set('w', -weight) # set weight for inhibition | ||
| syn_mechanism2.set("tau", dt) # set minimal decay time | ||
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| decor.place('"midpoint"', arb.synapse(syn_mechanism2), "postsyn_target") # place synaptic input from one presynaptic neuron at the center of the soma | ||
| decor.place('"midpoint"', arb.synapse(syn_mechanism1), "postsyn_target") # place synaptic input from another presynaptic neuron at the center of the soma | ||
| # (using the same label as above!) | ||
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| return arb.cable_cell(tree, labels, decor) | ||
| art_spiker_recipe.cell_description = types.MethodType(cell_description, art_spiker_recipe) | ||
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| # read connections from recipe for testing | ||
| connections_from_recipe = art_spiker_recipe.connections_on(3) | ||
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| # connection #1 from neuron 0 to 3 | ||
| self.assertEqual(connections_from_recipe[0].dest.label, "postsyn_target") | ||
| self.assertAlmostEqual(connections_from_recipe[0].weight, 1) | ||
| self.assertAlmostEqual(connections_from_recipe[0].delay, 0.2) | ||
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| # connection #2 from neuron 0 to 3 | ||
| self.assertEqual(connections_from_recipe[1].dest.label, "postsyn_target") | ||
| self.assertAlmostEqual(connections_from_recipe[1].weight, 1) | ||
| self.assertAlmostEqual(connections_from_recipe[1].delay, 0.2) | ||
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| # connection #3 from neuron 0 to 3 | ||
| self.assertEqual(connections_from_recipe[2].dest.label, "postsyn_target") | ||
| self.assertAlmostEqual(connections_from_recipe[2].weight, 1) | ||
| self.assertAlmostEqual(connections_from_recipe[2].delay, 0.2) | ||
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| # connection #1 from neuron 1 to 3 | ||
| self.assertEqual(connections_from_recipe[3].dest.label, "postsyn_target") | ||
| self.assertAlmostEqual(connections_from_recipe[3].weight, 1) | ||
| self.assertAlmostEqual(connections_from_recipe[3].delay, 0.6) | ||
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| # connection #2 from neuron 1 to 3 | ||
| self.assertEqual(connections_from_recipe[4].dest.label, "postsyn_target") | ||
| self.assertAlmostEqual(connections_from_recipe[4].weight, 1) | ||
| self.assertAlmostEqual(connections_from_recipe[4].delay, 0.6) | ||
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| # construct domain_decomposition and simulation object | ||
| dd = arb.partition_load_balance(art_spiker_recipe, context) | ||
| sim = arb.simulation(art_spiker_recipe, dd, context) | ||
| sim.record(arb.spike_recording.all) | ||
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| # create schedule and handle to record the membrane potential of neuron 3 | ||
| reg_sched = arb.regular_schedule(0, dt, runtime) | ||
| handle_mem = sim.sample((3, 0), reg_sched) | ||
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| # run the simulation | ||
| sim.run(runtime, dt) | ||
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| # evaluate the outcome | ||
| data_mem, _ = sim.samples(handle_mem)[0] | ||
| #print(data_mem[(data_mem[:, 0] >= 1.0), 1]) | ||
| self.assertGreater(data_mem[(np.round(data_mem[:, 0], 2) == 1.04), 1], -10) | ||
| spike_times = sim.spikes()["time"] | ||
| spike_gids = sim.spikes()["source"]["gid"] | ||
| #print(list(zip(*[spike_times, spike_gids]))) | ||
| self.assertGreater(sum(spike_gids == 3), 0) | ||
| self.assertEqual([2, 1, 0, 3], spike_gids.tolist()) | ||
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| # spike in neuron 3 shall occur at around 1.0 ms, when the added input from all connections will cause threshold crossing | ||
| self.assertAlmostEqual(spike_times[(spike_gids == 3)][0], 1.00, delta=0.04) | ||
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| @fixtures.context | ||
| @fixtures.art_spiker_recipe | ||
| @fixtures.sum_weight_hh_spike | ||
| def test_uni_connection(self, context, art_spiker_recipe, sum_weight_hh_spike): | ||
| runtime = 2 # ms | ||
| dt = 0.01 # ms | ||
| weight = sum_weight_hh_spike # set connection strength to the net sum of the connections in test_multiple_connections() (to evoke an immediate spike) | ||
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| # define new method 'connections_on()' and overwrite the original one in the 'art_spiker_recipe' object | ||
| def connections_on(self, gid): | ||
| # incoming to neurons 0--2 | ||
| if gid < 3: | ||
| return [] | ||
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| # incoming to neuron 3 | ||
| elif gid == 3: | ||
| source_label_0 = arb.cell_global_label(0, "spike_source") # referring to the "spike_source" label of neuron 0 | ||
| target_label_uni = arb.cell_local_label("postsyn_target", arb.selection_policy.univalent) # referring to an only item in the "postsyn_target" label group of neuron 3 | ||
| conn_2_3 = arb.connection(source_label_0, target_label_uni, weight, 0.2) # connection from neuron 0 to 3 | ||
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| return [conn_2_3] | ||
| art_spiker_recipe.connections_on = types.MethodType(connections_on, art_spiker_recipe) | ||
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| # define new method 'cell_description()' and overwrite the original one in the 'art_spiker_recipe' object | ||
| def cell_description(self, gid): | ||
| # spike source neuron | ||
| if gid < 3: | ||
| return arb.spike_source_cell("spike_source", arb.explicit_schedule(self.trains[gid])) | ||
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| # spike-receiving cable neuron | ||
| elif gid == 3: | ||
| tree, labels, decor = self._cable_cell_elements() | ||
| syn_mechanism = arb.mechanism("expsyn_curr") | ||
| syn_mechanism.set('w', weight) # set weight for excitation | ||
| syn_mechanism.set("tau", dt) # set minimal decay time | ||
| decor.place('"midpoint"', arb.synapse(syn_mechanism), "postsyn_target") # place synaptic input for one neuron at the center of the soma | ||
| return arb.cable_cell(tree, labels, decor) | ||
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| art_spiker_recipe.cell_description = types.MethodType(cell_description, art_spiker_recipe) | ||
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| # read connections from recipe for testing | ||
| connections_from_recipe = art_spiker_recipe.connections_on(3) | ||
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| # connection from neuron 0 to 3 | ||
| self.assertEqual(connections_from_recipe[0].dest.label, "postsyn_target") | ||
| self.assertAlmostEqual(connections_from_recipe[0].weight, weight) | ||
| self.assertAlmostEqual(connections_from_recipe[0].delay, 0.2) | ||
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| # construct domain_decomposition and simulation object | ||
| dd = arb.partition_load_balance(art_spiker_recipe, context) | ||
| sim = arb.simulation(art_spiker_recipe, dd, context) | ||
| sim.record(arb.spike_recording.all) | ||
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| # create schedule and handle to record the membrane potential of neuron 3 | ||
| reg_sched = arb.regular_schedule(0, dt, runtime) | ||
| handle_mem = sim.sample((3, 0), reg_sched) | ||
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| # run the simulation | ||
| sim.run(runtime, dt) | ||
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| # evaluate the outcome | ||
| data_mem, _ = sim.samples(handle_mem)[0] | ||
| #print(data_mem[(data_mem[:, 0] >= 1.0), 1]) | ||
| self.assertGreater(data_mem[(np.round(data_mem[:, 0], 2) == 1.04), 1], -10) | ||
| spike_times = sim.spikes()["time"] | ||
| spike_gids = sim.spikes()["source"]["gid"] | ||
| #print(list(zip(*[spike_times, spike_gids]))) | ||
| self.assertGreater(sum(spike_gids == 3), 0) | ||
| self.assertEqual([2, 1, 0, 3], spike_gids.tolist()) | ||
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| # spike in neuron 3 shall occur at around 1.0 ms, when the input will cause threshold crossing | ||
| self.assertAlmostEqual(spike_times[(spike_gids == 3)][0], 1.00, delta=0.04) | ||
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