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)", ... ... @@ -202,110 +202,9 @@ 202 202 203 203 (% class="box infomessage" %) 204 204 ((( 205 -**Screencast** - changes in editor 206 -\\**...** 207 -(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")(%%) 208 -(% style="color:#e74c3c" %)population2 = sim.Population(100, cell_type, label="Population 2") 209 -population2.set(i_offset=0)(%%) 210 -(% style="color:#000000" %)population1.record("v")(%%) 211 -(% style="color:#e74c3c" %)population2.record("v")(%%) 212 -(% style="color:#000000" %)sim.run(100.0)(%%) 213 -**...** 214 -))) 215 - 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. 217 - 218 -(% class="box successmessage" %) 219 -((( 220 -**Slide** showing all-to-all connections 221 -))) 222 - 223 -We could connect all neurons in Population 1 to all those in Population 2. 224 - 225 -(% class="box successmessage" %) 226 -((( 227 -**Slide** showing random connections 228 -))) 229 - 230 -We could connect the populations randomly, in several different ways. 231 - 232 -(% class="box successmessage" %) 233 -((( 234 -**Slide** showing distance-dependent connections 235 -))) 236 - 237 -(% class="wikigeneratedid" %) 238 -We could connect the populations randomly, but with a probability of connection that depends on the distance between the neurons. 239 - 240 -(% class="box successmessage" %) 241 -((( 242 -**Slide** showing explicit lists of connections 243 -))) 244 - 245 -(% class="wikigeneratedid" %) 246 -Or we could connect the neurons in a very specific manner, based on an explicit list of connections. 247 - 248 -(% class="wikigeneratedid" %) 249 -Just as PyNN provides a variety of neuron models, so it comes with a range of connection algorithms built in. You can also add your own connection methods. 250 - 251 -(% class="box successmessage" %) 252 -((( 253 -**Slide** showing addition of second population, and of connections between them, labelled as a Projection. 254 -))) 255 - 256 -(% class="wikigeneratedid" %) 257 -In PyNN, we call a group of connections between two populations a _Projection_. To create a Projection, we need to specify the presynaptic population, the postsynaptic population, the connection algorithm, and the synapse model. Here we're using the simplest synapse model available in PyNN, for which the synaptic weight is constant over time, there is no plasticity. 258 - 259 -(% class="box infomessage" %) 260 -((( 261 -**Screencast** - changes in editor 262 - 263 - 264 -**...** 265 -(% style="color:#000000" %)population2.record("v")(%%) 266 -(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5) 267 -synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5) 268 -connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%) 269 -(% style="color:#000000" %)sim.run(100.0)(%%) 270 -**...** 271 -))) 272 - 273 -(% class="wikigeneratedid" %) 274 -Finally, let's update our figure, by adding a second panel to show the responses of Population 2. 275 - 276 -(% class="box infomessage" %) 277 -((( 278 -**Screencast** - changes in editor 279 -\\**...** 280 -(% style="color:#000000" %)sim.run(100.0)(%%) 281 -(% style="color:#e74c3c" %)data1_v(% style="color:#000000" %) = population1.get_data().segments[0].filter(name='v')[0](%%) 282 -(% style="color:#e74c3c" %)data2_v = population2.get_data().segments[0].filter(name='v')[0](%%) 283 -(% style="color:#000000" %)Figure( 284 - Panel( 285 - (% style="color:#e74c3c" %)data1_v(% style="color:#000000" %)[:, 0:5], 286 - xticks=True, (% style="color:#e74c3c" %)--xlabel="Time (ms)",--(%%) 287 -(% style="color:#000000" %) yticks=True, ylabel="Membrane potential (mV)" 288 - ), 289 - (% style="color:#e74c3c" %)Panel( 290 - data2_v[:, 0:5], 291 - xticks=True, xlabel="Time (ms)", 292 - yticks=True" 293 - ),(%%) 294 -(% style="color:#000000" %) title="Response of (% style="color:#e74c3c" %)simple network(% style="color:#000000" %)", 295 - annotations="Simulated with NEST" 296 -).show() 297 - 298 -**Run script in terminal, show figure** 299 -))) 300 - 301 -(% class="wikigeneratedid" %) 302 -and there we have it, our simple neuronal network of integrate-and-fire neurons, written in PyNN, simulated with NEST. If you prefer to use the NEURON simulator, PyNN makes this very simple, we import the PyNN-for-NEURON module instead. 303 - 304 -(% class="box infomessage" %) 305 -((( 306 -**Screencast** - final state of editor 210 +**Screencast** - current state of editor 307 307 \\(% style="color:#000000" %)"""Simple network model using PyNN""" 308 -\\import pyNN. (% style="color:#e74c3c" %)neuron(%style="color:#000000"%)as sim(%%)212 +\\import pyNN.nest as sim(%%) 309 309 (% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%) 310 310 (% style="color:#000000" %)from pyNN.random import RandomDistribution(%%) 311 311 (% style="color:#000000" %)sim.setup(timestep=0.1)(%%) ... ... @@ -315,55 +315,38 @@ 315 315 v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%) 316 316 (% 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 -(% style="color:#000000" %)population2 = sim.Population(100, cell_type, label="Population 2") 319 -population2.set(i_offset=0) 320 -population1.record("v") 321 -population2.record("v")(%%) 322 -(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5) 323 -synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5) 324 -connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%) 325 -(% style="color:#000000" %)sim.run(100.0)(%%) 326 -(% style="color:#000000" %)data1_v = population1.get_data().segments[0].filter(name='v')[0] 327 -data2_v = population2.get_data().segments[0].filter(name='v')[0] 222 +(% style="color:#000000" %)population1.record("v") 223 +sim.run(100.0)(%%) 224 +(% style="color:#000000" %)data_v = population1.get_data().segments[0].filter(name='v')[0] 328 328 Figure( 329 329 Panel( 330 - data 1_v[:, 0:5],331 - xticks=True, 227 + data_v[:, 0:5], 228 + xticks=True, xlabel="Time (ms)", 332 332 yticks=True, ylabel="Membrane potential (mV)" 333 333 ), 334 - Panel( 335 - data2_v[:, 0:5], 336 - xticks=True, xlabel="Time (ms)", 337 - yticks=True" 338 - ),(%%) 339 -(% style="color:#000000" %) title="Response of simple network", 340 - annotations="Simulated with (% style="color:#e74c3c" %)NEURON(% style="color:#000000" %)" 341 -).show() 342 - 343 -**Run script in terminal, show figure** 231 + title="Response of first five neurons with heterogeneous parameters", 232 + annotations="Simulated with NEST" 233 +).show()(%%) 234 +\\**Run script in terminal, show figure** 344 344 ))) 345 345 346 -(% class="wikigeneratedid" %) 347 -As you would hope, NEST and NEURON give essentially identical results. 348 348 349 -(% class="box successmessage" %) 350 -((( 351 -**Slide** recap of learning objectives 352 -))) 238 +(% class="wikigeneratedid" id="HSummary28Inthistutorial2CyouhavelearnedtodoX202629" %) 239 +(% class="small" %)**Summary (In this tutorial, you have learned to do X…)** 353 353 354 - That is the end of this tutorial, in which I've demonstrated how to build a simple network using PyNN, and to simulate it using two different simulators, NEST and NEURON.241 +. 355 355 356 -Of course, PyNN allows you to create much more complex networks than this, with more realistic neuron models, synaptic plasticity, spatial structure, and so on. You can also use other simulators, such as Brian or SpiNNaker, and you can run simulations in parallel on clusters or supercomputers. 243 +(% class="wikigeneratedid" id="HAcknowledgementsifappropriate" %) 244 +(% class="small" %)**Acknowledgements if appropriate** 357 357 358 - We will be releasing a series of tutorials, throughout the rest of 2021 and 2022, to introduce these more advanced features of PyNN, so keep an eye on the EBRAINS website.246 +. 359 359 360 -(% class="box successmessage" %) 361 -((( 362 -**Slide** acknowledgements, contact information 363 -))) 248 +(% class="wikigeneratedid" id="HReferencestowebsites28Formoreinformation2Cvisitusat202629" %) 249 +(% class="small" %)**References to websites (For more information, visit us at…)** 364 364 365 -(% class="wikigeneratedid" %) 366 -PyNN has been developed by many different people, with financial support from several different organisations. I'd like to mention in particular the CNRS and the European Commission, through the FACETS, BrainScaleS and Human Brain Project grants. 251 +. 367 367 368 -(% class="wikigeneratedid" %) 369 -For more information visit neuralensemble.org/PyNN. If you have questions you can contact us through the PyNN Github project, the NeuralEnsemble forum, EBRAINS support, or the EBRAINS Community. 253 +(% class="wikigeneratedid" id="HContactinformation28Forquestions2Ccontactusat202629" %) 254 +(% class="small" %)**Contact information (For questions, contact us at…)** 255 + 256 +.