Last modified by adavison on 2022/10/04 13:55
From version 18.1
edited by adavison
on 2021/12/01 13:07
Change comment: There is no comment for this version
To version 16.1
edited by annedevismes
on 2021/10/18 10:26
Change comment: There is no comment for this version

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1 -XWiki.adavison
1 +XWiki.annedevismes
Content
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92 92  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
93 93  \\import pyNN.nest as sim
94 94  sim.setup(timestep=0.1)(%%)
95 -(% style="color:#e74c3c" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)
95 +(% style="color:#e74c3c" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, t_refrac=1, tau_m=10, cm=1, i_offset=0.1)
96 96  )))
97 97  
98 98  Let's create 100 of these neurons; then, we're going to record the membrane voltage and run a simulation for 100 milliseconds.
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103 103  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
104 104  \\import pyNN.nest as sim
105 105  sim.setup(timestep=0.1)(%%)
106 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
106 +(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
107 107  (% style="color:#e74c3c" %)population1 = sim.Population(100, cell_type, label="Population 1")
108 108  population1.record("v")
109 109  sim.run(100.0)(%%)
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119 119  \\import pyNN.nest as sim(%%)
120 120  (% style="color:#e74c3c" %)from pyNN.utility.plotting import Figure, Panel(%%)
121 121  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
122 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
122 +(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(v_rest=-65, v_thresh=-55, v_reset=-65, t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
123 123  (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
124 124  population1.record("v")
125 125  sim.run(100.0)(%%)
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172 172   (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
173 173   v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
174 174   v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
175 -(% style="color:#000000" %) tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
175 +(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
176 176  
177 177  
178 178  **...**
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289 289   (% style="color:#e74c3c" %)Panel(
290 290   data2_v[:, 0:5],
291 291   xticks=True, xlabel="Time (ms)",
292 - yticks=True
292 + yticks=True"
293 293   ),(%%)
294 294  (% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)",
295 295   annotations="Simulated with NEST"
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313 313   (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
314 314   v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
315 315   v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
316 -(% style="color:#000000" %) tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
316 +(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
317 317  (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
318 318  (% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
319 319  population2.set(i_offset=0)
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334 334   Panel(
335 335   data2_v[:, 0:5],
336 336   xticks=True, xlabel="Time (ms)",
337 - yticks=True
337 + yticks=True"
338 338   ),(%%)
339 339  (% style="color:#000000" %) title="Response of simple network",
340 340   annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)"