Last modified by adavison on 2022/10/04 13:55
From version 11.2
edited by adavison
on 2021/09/30 13:51
Change comment: There is no comment for this version
To version 10.2
edited by adavison
on 2021/08/04 16:19
Change comment: There is no comment for this version

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... ... @@ -106,95 +106,17 @@
106 106  (% 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)(%%)
107 107  (% style="color:#e74c3c" %)population1 = sim.Population(100, cell_type, label="Population 1")
108 108  population1.record("v")
109 -sim.run(100.0)(%%)
110 -\\**Run script in terminal**
109 +sim.run(100.0)
111 111  )))
112 112  
113 113  PyNN has some built-in tools for making simple plots, so let's import those, and plot the membrane voltage of the zeroth neuron in our population (remember Python starts counting at zero).
114 114  
115 -(% class="box infomessage" %)
116 -(((
117 -**Screencast** - current state of editor
118 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
119 -\\import pyNN.nest as sim(%%)
120 -(% style="color:#e74c3c" %)from pyNN.utility.plotting import Figure, Panel(%%)
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, t_refrac=1, tau_m=10, cm=1, i_offset=0.1)(%%)
123 -(% style="color:#000000" %)population1 = sim.Population(100, cell_type, label="Population 1")
124 -population1.record("v")
125 -sim.run(100.0)(%%)
126 -(% style="color:#e74c3c" %)data_v = population1.get_data().segments[0].filter(name='v')[0]
127 -Figure(
128 - Panel(
129 - data_v[:, 0],
130 - xticks=True, xlabel="Time (ms)",
131 - yticks=True, ylabel="Membrane potential (mV)"
132 - ),
133 - title="Response of neuron #0",
134 - annotations="Simulated with NEST"
135 -).show()(%%)
136 -\\**Run script in terminal, show figure**
137 -)))
138 -
139 139  As you'd expect, the bias current causes the membrane voltage to increase until it reaches threshold~-~--it doesn't increase in a straight line because it's a //leaky// integrate-and-fire neuron~-~--then once it hits the threshold the voltage is reset, and then stays at the same level for a short time~-~--this is the refractory period~-~--before it starts to increase again.
140 140  
141 141  Now, all 100 neurons in our population are identical, so if we plotted the first neuron, the second neuron, ..., we'd get the same trace.
142 142  
143 -(% class="box infomessage" %)
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(
156 - Panel(
157 - data_v[:, (% style="color:#e74c3c" %)0:5(% style="color:#000000" %)],
158 - xticks=True, xlabel="Time (ms)",
159 - yticks=True, ylabel="Membrane potential (mV)"
160 - ),
161 - title="Response of (% style="color:#e74c3c" %)first five neurons(% style="color:#000000" %)",
162 - annotations="Simulated with NEST"
163 -).show()(%%)
164 -\\**Run script in terminal, show figure**
165 -)))
118 +Let's change that. In nature every neuron is a little bit different, so let's set the resting membrane potential and the spike threshold randomly from a Gaussian distribution, and let's plot membrane voltage from _all_ the  neurons.
166 166  
167 -Let's change that. In nature every neuron is a little bit different, so let's set the resting membrane potential and the spike threshold randomly from a Gaussian distribution.
168 -
169 -(% class="box infomessage" %)
170 -(((
171 -**Screencast** - current state of editor
172 -\\(% style="color:#000000" %)"""Simple network model using PyNN"""
173 -\\import pyNN.nest as sim(%%)
174 -(% style="color:#000000" %)from pyNN.utility.plotting import Figure, Panel(%%)
175 -(% style="color:#e74c3c" %)from pyNN.random import RandomDistribution(%%)
176 -(% style="color:#000000" %)sim.setup(timestep=0.1)(%%)
177 -(% style="color:#000000" %)cell_type  = sim.IF_curr_exp(
178 - (% style="color:#e74c3c" %) v_rest=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}),
179 - v_thresh=RandomDistribution('normal', {'mu': -55.0, 'sigma': 1.0}),
180 - v_reset=RandomDistribution('normal', {'mu': -65.0, 'sigma': 1.0}), (%%)
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(
187 - Panel(
188 - data_v[:, 0:5],
189 - xticks=True, xlabel="Time (ms)",
190 - yticks=True, ylabel="Membrane potential (mV)"
191 - ),
192 - title="Response of first five neurons (% style="color:#e74c3c" %)with heterogeneous parameters(% style="color:#000000" %)",
193 - annotations="Simulated with NEST"
194 -).show()(%%)
195 -\\**Run script in terminal, show figure**
196 -)))
197 -
198 198  Now if we run our simulation again, we can see the effect of this heterogeneity in the neuron population.
199 199  
200 200  TO BE COMPLETED