Changes for page 03. Building and simulating a simple model
Last modified by adavison on 2022/10/04 13:55
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... ... @@ -142,11 +142,17 @@ 142 142 143 143 (% class="box infomessage" %) 144 144 ((( 145 -**Screencast** - changes in editor 146 - 147 - 148 -**...** 149 -(% style="color:#000000" %)Figure( 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( 150 150 Panel( 151 151 data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)], 152 152 xticks=True, xlabel="Time (ms)", ... ... @@ -162,7 +162,7 @@ 162 162 163 163 (% class="box infomessage" %) 164 164 ((( 165 -**Screencast** - c hangesineditor171 +**Screencast** - current state of editor 166 166 \\(% style="color:#000000" %)"""Simple network model using PyNN""" 167 167 \\import pyNN.nest as sim(%%) 168 168 (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%) ... ... @@ -173,12 +173,11 @@ 173 173 v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}), 174 174 v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%) 175 175 (% style="color:#000000" %) t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%) 176 - 177 - 178 -**...** 179 - 180 - 181 -(% style="color:#000000" %)Figure( 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( 182 182 Panel( 183 183 data_v[:, 0:5], 184 184 xticks=True, xlabel="Time (ms)", ... ... @@ -202,8 +202,17 @@ 202 202 203 203 (% class="box infomessage" %) 204 204 ((( 205 -**Screencast** - changes in editor 206 -\\**...** 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)(%%) 207 207 (% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%) 208 208 (% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2") 209 209 population2.set(i_offset=0)(%%) ... ... @@ -210,7 +210,16 @@ 210 210 (% style="color:#000000" %)population1.record("v")(%%) 211 211 (% style="color:#e74c3c" %)population2.record("v")(%%) 212 212 (% style="color:#000000" %)sim.run(100.0)(%%) 213 -**...** 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() 214 214 ))) 215 215 216 216 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. ... ... @@ -258,16 +258,36 @@ 258 258 259 259 (% class="box infomessage" %) 260 260 ((( 261 -**Screencast** - changes in editor 262 - 263 - 264 -**...** 265 -(% style="color:#000000" %)population2.record("v")(%%) 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")(%%) 266 266 (% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=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)(%%) 270 -**...** 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() 271 271 ))) 272 272 273 273 (% class="wikigeneratedid" %) ... ... @@ -275,8 +275,25 @@ 275 275 276 276 (% class="box infomessage" %) 277 277 ((( 278 -**Screencast** - changes in editor 279 -\\**...** 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)(%%) 280 280 (% style="color:#000000" %)sim.run(100.0)(%%) 281 281 (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%) 282 282 (% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%) ... ... @@ -303,7 +303,7 @@ 303 303 304 304 (% class="box infomessage" %) 305 305 ((( 306 -**Screencast** - finalstate of editor366 +**Screencast** - current state of editor 307 307 \\(% style="color:#000000" %)"""Simple network model using PyNN""" 308 308 \\import pyNN.(% style="color:#e74c3c" %)neuron(% style="color:#000000" %) as sim(%%) 309 309 (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)