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Last modified by adavison on 2022/10/04 13:55
From version 16.1
edited by annedevismes
on 2021/10/18 10:26
Change comment: There is no comment for this version
To version 20.1
edited by adavison
on 2021/12/01 15:21
Change comment: There is no comment for this version

Summary

Details

Page properties
Author
... ... @@ -1,1 +1,1 @@
1 -XWiki.annedevismes
1 +XWiki.adavison
Content
... ... @@ -92,7 +92,7 @@
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, t_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, tau_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.
... ... @@ -103,7 +103,7 @@
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, t_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, tau_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)(%%)
... ... @@ -119,7 +119,7 @@
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, t_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, tau_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)(%%)
... ... @@ -169,10 +169,10 @@
169 169  (% style="color:#e74c3c" %)from pyNN.random import RandomDistribution(%%)
170 170  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
171 171  (% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
172 - (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
173 - v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
174 - v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
175 -(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
172 + (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0),
173 + v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0),
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)(%%)
176 176  
177 177  
178 178  **...**
... ... @@ -263,7 +263,7 @@
263 263  
264 264  **...**
265 265  (% style="color:#000000" %)population2.record("v")(%%)
266 -(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
266 +(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5)
267 267  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
268 268  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
269 269  (% style="color:#000000" %)sim.run(100.0)(%%)
... ... @@ -289,7 +289,7 @@
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"
... ... @@ -310,16 +310,16 @@
310 310  (% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
311 311  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
312 312  (% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
313 - (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
314 - v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
315 - v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
316 -(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
313 + (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0),
314 + v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0),
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)(%%)
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)
320 320  population1.record("v")
321 321  population2.record("v")(%%)
322 -(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
322 +(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5)
323 323  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
324 324  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
325 325  (% style="color:#000000" %)sim.run(100.0)(%%)
... ... @@ -334,7 +334,7 @@
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" %)"