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Last modified by adavison on 2022/10/04 13:55
From version 23.1
edited by shailesh
on 2021/12/09 21:40
Change comment: There is no comment for this version
To version 21.1
edited by shailesh
on 2021/12/09 20:14
Change comment: There is no comment for this version

Summary

Details

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Content
... ... @@ -84,7 +84,7 @@
84 84  
85 85  PyNN comes with a selection of integrate-and-fire models. We're going to use the IF_curr_exp model, where "IF" is for integrate-and-fire, "curr" means that synaptic responses are changes in current, and "exp" means that the shape of the current is a decaying exponential function.
86 86  
87 -This is where we set the parameters of the model: the resting membrane potential is -65 millivolts, the spike threshold is -55 millivolts, the reset voltage after a spike is again -65 millivolts, the refractory period after a spike is one millisecond, the membrane time constant is 10 milliseconds, and the membrane capacitance is 1 nanofarad. We're also going to inject a constant bias current of 1.1 nanoamps into these neurons, so that we get some action potentials.
87 +This is where we set the parameters of the model: the resting membrane potential is -65 millivolts, the spike threshold is -55 millivolts, the reset voltage after a spike is again -65 millivolts, the refractory period after a spike is one millisecond, the membrane time constant is 10 milliseconds, and the membrane capacitance is 1 nanofarad. We're also going to inject a constant bias current of 0.1 nanoamps into these neurons, so that we get some action potentials.
88 88  
89 89  (% class="box infomessage" %)
90 90  (((
... ... @@ -166,14 +166,14 @@
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(%%)
169 -(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
169 +(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution(%%)
170 170  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
171 -(% style="color:#e74c3c" %)rng = NumpyRNG(seed=1)(%%)
172 172  (% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
173 - (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng),
174 - v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0, rng=rng),
175 - v_reset=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng), (%%)
176 -(% style="color:#000000" %) tau_refrac=1, tau_m=10, cm=1, i_offset=1.1)
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=1.1)(%%)
176 +
177 177  
178 178  **...**
179 179  
... ... @@ -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_connect=0.5, rng=rng)
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)(%%)
... ... @@ -307,20 +307,19 @@
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(%%)
310 -(% style="color:#000000" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
310 +(% style="color:#000000" %)from pyNN.random import RandomDistribution(%%)
311 311  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
312 -(% style="color:#000000" %)rng = NumpyRNG(seed=1)(%%)
313 313  (% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
314 - v_rest=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng),
315 - v_thresh=RandomDistribution('normal', mu=-55.0, sigma=1.0, rng=rng),
316 - v_reset=RandomDistribution('normal', mu=-65.0, sigma=1.0, rng=rng), 
317 - tau_refrac=1, tau_m=10, cm=1, i_offset=1.1)
318 -population1 = sim.Population(100, cell_type, label="Population 1")
319 -population2 = sim.Population(100, cell_type, label="Population 2")
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=1.1)(%%)
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")
320 320  population2.set(i_offset=0)
321 321  population1.record("v")
322 -population2.record("v")
323 -connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5, rng=rng)
321 +population2.record("v")(%%)
322 +(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5)
324 324  synapse_type = sim.StaticSynapse(weight=0.5, delay=0.5)
325 325  connections = sim.Projection(population1, population2, connection_algorithm, synapse_type)(%%)
326 326  (% style="color:#000000" %)sim.run(100.0)(%%)