Last modified by adavison on 2022/10/04 13:55
From version 19.1
edited by adavison
on 2021/12/01 13:17
Change comment: There is no comment for this version
To version 22.1
edited by shailesh
on 2021/12/09 20:27
Change comment: There is no comment for this version

Summary

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Author
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1 -XWiki.adavison
1 +XWiki.shailesh
Content
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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, tau_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=1.1)
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.
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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, tau_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=1.1)(%%)
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, tau_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=1.1)(%%)
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)(%%)
... ... @@ -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(%%)
169 +(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
170 170  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
171 +(% style="color:#e74c3c" %)rng = NumpyRNG(seed=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" %) tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
176 -
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)
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=0.5)
266 +(% style="color:#e74c3c" %)connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5, rng=rng)
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)(%%)
... ... @@ -307,19 +307,20 @@
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(%%)
310 +(% style="color:#000000" %)from pyNN.random import RandomDistribution, NumpyRNG(%%)
311 311  (% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
312 +(% style="color:#000000" %)rng = NumpyRNG(seed=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" %) tau_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
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")
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")
319 319  population2.set(i_offset=0)
320 320  population1.record("v")
321 -population2.record("v")(%%)
322 -(% style="color:#000000" %)connection_algorithm = sim.FixedProbabilityConnector(p=0.5)
322 +population2.record("v")
323 +connection_algorithm = sim.FixedProbabilityConnector(p_connect=0.5, rng=rng)
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)(%%)