Last modified by adavison on 2022/10/04 13:55
From version 14.1
edited by adavison
on 2021/09/30 15:27
Change comment: There is no comment for this version
To version 15.1
edited by adavison
on 2021/09/30 15:30
Change comment: There is no comment for this version

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... ... @@ -142,17 +142,11 @@
142 142  
143 143  (% class="box infomessage" %)
144 144  (((
145 -**Screencast** - current state of editor
146 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
147 -\\import pyNN.nest as sim(%%)
148 -(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
149 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
150 -(% 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)(%%)
151 -(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
152 -population1.record("v")
153 -sim.run(100.0)(%%)
154 -(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
155 -Figure(
145 +**Screencast** - changes in editor
146 +
147 +
148 +**...**
149 +(% style="color:#000000" %)Figure(
156 156   Panel(
157 157   data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)],
158 158   xticks=True, xlabel="Time (ms)",
... ... @@ -168,7 +168,7 @@
168 168  
169 169  (% class="box infomessage" %)
170 170  (((
171 -**Screencast** - current state of editor
165 +**Screencast** - changes in editor
172 172  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
173 173  \\import pyNN.nest as sim(%%)
174 174  (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
... ... @@ -179,11 +179,12 @@
179 179   v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
180 180   v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
181 181  (% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
182 -(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
183 -population1.record("v")
184 -sim.run(100.0)(%%)
185 -(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
186 -Figure(
176 +
177 +
178 +**...**
179 +
180 +
181 +(% style="color:#000000" %)Figure(
187 187   Panel(
188 188   data_v[:, 0:5],
189 189   xticks=True, xlabel="Time (ms)",
... ... @@ -207,17 +207,8 @@
207 207  
208 208  (% class="box infomessage" %)
209 209  (((
210 -**Screencast** - current state of editor
211 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
212 -\\import pyNN.nest as sim(%%)
213 -(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
214 -(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
215 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
216 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
217 - (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
218 - v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
219 - v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
220 -(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
205 +**Screencast** - changes in editor
206 +\\**...**
221 221  (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
222 222  (% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2")
223 223  population2.set(i_offset=0)(%%)
... ... @@ -224,16 +224,7 @@
224 224  (% style="color:#000000" %)population1.record("v")(%%)
225 225  (% style="color:#e74c3c" %)population2.record("v")(%%)
226 226  (% style="color:#000000" %)sim.run(100.0)(%%)
227 -(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
228 -Figure(
229 - Panel(
230 - data_v[:, 0:5],
231 - xticks=True, xlabel="Time (ms)",
232 - yticks=True, ylabel="Membrane potential (mV)"
233 - ),
234 - title="Response of first five neurons with heterogeneous parameters",
235 - annotations="Simulated with NEST"
236 -).show()
213 +**...**
237 237  )))
238 238  
239 239  Now we want to create synaptic connections between the neurons in Population 1 and those in Population 2. There are lots of different ways these could be connected.
... ... @@ -281,36 +281,16 @@
281 281  
282 282  (% class="box infomessage" %)
283 283  (((
284 -**Screencast** - current state of editor
285 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
286 -\\import pyNN.nest as sim(%%)
287 -(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
288 -(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
289 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
290 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
291 - (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
292 - v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
293 - v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
294 -(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
295 -(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
296 -(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
297 -population2.set(i_offset=0)
298 -population1.record("v")
299 -population2.record("v")(%%)
261 +**Screencast** - changes in editor
262 +
263 +
264 +**...**
265 +(% style="color:#000000" %)population2.record("v")(%%)
300 300  (% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
301 301  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
302 302  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
303 303  (% style="color:#000000" %)sim.run(100.0)(%%)
304 -(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
305 -Figure(
306 - Panel(
307 - data_v[:, 0:5],
308 - xticks=True, xlabel="Time (ms)",
309 - yticks=True, ylabel="Membrane potential (mV)"
310 - ),
311 - title="Response of first five neurons with heterogeneous parameters",
312 - annotations="Simulated with NEST"
313 -).show()
270 +**...**
314 314  )))
315 315  
316 316  (% class="wikigeneratedid" %)
... ... @@ -318,25 +318,8 @@
318 318  
319 319  (% class="box infomessage" %)
320 320  (((
321 -**Screencast** - current state of editor
322 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
323 -\\import pyNN.nest as sim(%%)
324 -(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
325 -(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
326 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
327 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
328 - (% style="color:#e74c3c" %) (% style="color:#000000" %)v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
329 - v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
330 - v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
331 -(% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
332 -(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%)
333 -(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2")
334 -population2.set(i_offset=0)
335 -population1.record("v")
336 -population2.record("v")(%%)
337 -(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
338 -synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
339 -connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
278 +**Screencast** - changes in editor
279 +\\**...**
340 340  (% style="color:#000000" %)sim.run(100.0)(%%)
341 341  (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%)
342 342  (% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%)
... ... @@ -363,7 +363,7 @@
363 363  
364 364  (% class="box infomessage" %)
365 365  (((
366 -**Screencast** - current state of editor
306 +**Screencast** - final state of editor
367 367  \\(% style="color:#000000" %)"""Simple network model using PyNN"""
368 368  \\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%)
369 369  (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)