.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/brunel_delta_nest.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_brunel_delta_nest.py: Random balanced network (delta synapses) ---------------------------------------- .. only:: html ---- Run this example as a Jupyter notebook: .. card:: :width: 25% :margin: 2 :text-align: center :link: https://lab.ebrains.eu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fnest%2Fnest-simulator-examples&urlpath=lab%2Ftree%2Fnest-simulator-examples%2Fnotebooks%2Fnotebooks%2Fbrunel_delta_nest.ipynb&branch=main :link-alt: JupyterHub service .. image:: https://nest-simulator.org/TryItOnEBRAINS.png .. grid:: 1 1 1 1 :padding: 0 0 2 0 .. grid-item:: :class: sd-text-muted :margin: 0 0 3 0 :padding: 0 0 3 0 :columns: 4 See :ref:`our guide ` for more information and troubleshooting. ---- This script simulates an excitatory and an inhibitory population on the basis of the network used in [1]_ When connecting the network, customary synapse models are used, which allow for querying the number of created synapses. Using spike recorders, the average firing rates of the neurons in the populations are established. The building as well as the simulation time of the network are recorded. References ~~~~~~~~~~ .. [1] Brunel N (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience 8, 183-208. .. GENERATED FROM PYTHON SOURCE LINES 45-46 Import all necessary modules for simulation, analysis and plotting. .. GENERATED FROM PYTHON SOURCE LINES 46-55 .. code-block:: Python import time import matplotlib.pyplot as plt import nest import nest.raster_plot nest.ResetKernel() .. GENERATED FROM PYTHON SOURCE LINES 56-58 Assigning the current time to a variable in order to determine the build time of the network. .. GENERATED FROM PYTHON SOURCE LINES 58-61 .. code-block:: Python startbuild = time.time() .. GENERATED FROM PYTHON SOURCE LINES 62-63 Assigning the simulation parameters to variables. .. GENERATED FROM PYTHON SOURCE LINES 63-69 .. code-block:: Python dt = 0.1 # the resolution in ms simtime = 1000.0 # Simulation time in ms delay = 1.5 # synaptic delay in ms .. GENERATED FROM PYTHON SOURCE LINES 70-72 Definition of the parameters crucial for asynchronous irregular firing of the neurons. .. GENERATED FROM PYTHON SOURCE LINES 72-77 .. code-block:: Python g = 5.0 # ratio inhibitory weight/excitatory weight eta = 2.0 # external rate relative to threshold rate epsilon = 0.1 # connection probability .. GENERATED FROM PYTHON SOURCE LINES 78-80 Definition of the number of neurons in the network and the number of neurons recorded from .. GENERATED FROM PYTHON SOURCE LINES 80-87 .. code-block:: Python order = 2500 NE = 4 * order # number of excitatory neurons NI = 1 * order # number of inhibitory neurons N_neurons = NE + NI # number of neurons in total N_rec = 50 # record from 50 neurons .. GENERATED FROM PYTHON SOURCE LINES 88-89 Definition of connectivity parameters .. GENERATED FROM PYTHON SOURCE LINES 89-94 .. code-block:: Python CE = int(epsilon * NE) # number of excitatory synapses per neuron CI = int(epsilon * NI) # number of inhibitory synapses per neuron C_tot = int(CI + CE) # total number of synapses per neuron .. GENERATED FROM PYTHON SOURCE LINES 95-97 Initialization of the parameters of the integrate and fire neuron and the synapses. The parameters of the neuron are stored in a dictionary. .. GENERATED FROM PYTHON SOURCE LINES 97-105 .. code-block:: Python tauMem = 20.0 # time constant of membrane potential in ms theta = 20.0 # membrane threshold potential in mV neuron_params = {"C_m": 1.0, "tau_m": tauMem, "t_ref": 2.0, "E_L": 0.0, "V_reset": 0.0, "V_m": 0.0, "V_th": theta} J = 0.1 # postsynaptic amplitude in mV J_ex = J # amplitude of excitatory postsynaptic potential J_in = -g * J_ex # amplitude of inhibitory postsynaptic potential .. GENERATED FROM PYTHON SOURCE LINES 106-110 Definition of threshold rate, which is the external rate needed to fix the membrane potential around its threshold, the external firing rate and the rate of the poisson generator which is multiplied by the in-degree CE and converted to Hz by multiplication by 1000. .. GENERATED FROM PYTHON SOURCE LINES 110-115 .. code-block:: Python nu_th = theta / (J * CE * tauMem) nu_ex = eta * nu_th p_rate = 1000.0 * nu_ex * CE .. GENERATED FROM PYTHON SOURCE LINES 116-120 Configuration of the simulation kernel by the previously defined time resolution used in the simulation. Setting ``print_time`` to `True` prints the already processed simulation time as well as its percentage of the total simulation time. .. GENERATED FROM PYTHON SOURCE LINES 120-127 .. code-block:: Python nest.resolution = dt nest.print_time = True nest.overwrite_files = True print("Building network") .. GENERATED FROM PYTHON SOURCE LINES 128-133 Creation of the nodes using ``Create``. We store the returned handles in variables for later reference. Here the excitatory and inhibitory, as well as the poisson generator and two spike recorders. The spike recorders will later be used to record excitatory and inhibitory spikes. Properties of the nodes are specified via ``params``, which expects a dictionary. .. GENERATED FROM PYTHON SOURCE LINES 133-140 .. code-block:: Python nodes_ex = nest.Create("iaf_psc_delta", NE, params=neuron_params) nodes_in = nest.Create("iaf_psc_delta", NI, params=neuron_params) noise = nest.Create("poisson_generator", params={"rate": p_rate}) espikes = nest.Create("spike_recorder") ispikes = nest.Create("spike_recorder") .. GENERATED FROM PYTHON SOURCE LINES 141-145 Configuration of the spike recorders recording excitatory and inhibitory spikes by sending parameter dictionaries to ``set``. Setting the property `record_to` to *"ascii"* ensures that the spikes will be recorded to a file, whose name starts with the string assigned to the property `label`. .. GENERATED FROM PYTHON SOURCE LINES 145-151 .. code-block:: Python espikes.set(label="brunel-py-ex", record_to="ascii") ispikes.set(label="brunel-py-in", record_to="ascii") print("Connecting devices") .. GENERATED FROM PYTHON SOURCE LINES 152-158 Definition of a synapse using ``CopyModel``, which expects the model name of a pre-defined synapse, the name of the customary synapse and an optional parameter dictionary. The parameters defined in the dictionary will be the default parameter for the customary synapse. Here we define one synapse for the excitatory and one for the inhibitory connections giving the previously defined weights and equal delays. .. GENERATED FROM PYTHON SOURCE LINES 158-162 .. code-block:: Python nest.CopyModel("static_synapse", "excitatory", {"weight": J_ex, "delay": delay}) nest.CopyModel("static_synapse", "inhibitory", {"weight": J_in, "delay": delay}) .. GENERATED FROM PYTHON SOURCE LINES 163-169 Connecting the previously defined poisson generator to the excitatory and inhibitory neurons using the excitatory synapse. Since the poisson generator is connected to all neurons in the population the default rule (# ``all_to_all``) of ``Connect`` is used. The synaptic properties are inserted via ``syn_spec`` which expects a dictionary when defining multiple variables or a string when simply using a pre-defined synapse. .. GENERATED FROM PYTHON SOURCE LINES 169-173 .. code-block:: Python nest.Connect(noise, nodes_ex, syn_spec="excitatory") nest.Connect(noise, nodes_in, syn_spec="excitatory") .. GENERATED FROM PYTHON SOURCE LINES 174-178 Connecting the first ``N_rec`` nodes of the excitatory and inhibitory population to the associated spike recorders using excitatory synapses. Here the same shortcut for the specification of the synapse as defined above is used. .. GENERATED FROM PYTHON SOURCE LINES 178-186 .. code-block:: Python nest.Connect(nodes_ex[:N_rec], espikes, syn_spec="excitatory") nest.Connect(nodes_in[:N_rec], ispikes, syn_spec="excitatory") print("Connecting network") print("Excitatory connections") .. GENERATED FROM PYTHON SOURCE LINES 187-193 Connecting the excitatory population to all neurons using the pre-defined excitatory synapse. Beforehand, the connection parameter are defined in a dictionary. Here we use the connection rule ``fixed_indegree``, which requires the definition of the indegree. Since the synapse specification is reduced to assigning the pre-defined excitatory synapse it suffices to insert a string. .. GENERATED FROM PYTHON SOURCE LINES 193-199 .. code-block:: Python conn_params_ex = {"rule": "fixed_indegree", "indegree": CE} nest.Connect(nodes_ex, nodes_ex + nodes_in, conn_params_ex, "excitatory") print("Inhibitory connections") .. GENERATED FROM PYTHON SOURCE LINES 200-204 Connecting the inhibitory population to all neurons using the pre-defined inhibitory synapse. The connection parameters as well as the synapse parameters are defined analogously to the connection from the excitatory population defined above. .. GENERATED FROM PYTHON SOURCE LINES 204-208 .. code-block:: Python conn_params_in = {"rule": "fixed_indegree", "indegree": CI} nest.Connect(nodes_in, nodes_ex + nodes_in, conn_params_in, "inhibitory") .. GENERATED FROM PYTHON SOURCE LINES 209-210 Storage of the time point after the buildup of the network in a variable. .. GENERATED FROM PYTHON SOURCE LINES 210-213 .. code-block:: Python endbuild = time.time() .. GENERATED FROM PYTHON SOURCE LINES 214-215 Simulation of the network. .. GENERATED FROM PYTHON SOURCE LINES 215-220 .. code-block:: Python print("Simulating") nest.Simulate(simtime) .. GENERATED FROM PYTHON SOURCE LINES 221-222 Storage of the time point after the simulation of the network in a variable. .. GENERATED FROM PYTHON SOURCE LINES 222-225 .. code-block:: Python endsimulate = time.time() .. GENERATED FROM PYTHON SOURCE LINES 226-228 Reading out the total number of spikes received from the spike recorder connected to the excitatory population and the inhibitory population. .. GENERATED FROM PYTHON SOURCE LINES 228-232 .. code-block:: Python events_ex = espikes.n_events events_in = ispikes.n_events .. GENERATED FROM PYTHON SOURCE LINES 233-237 Calculation of the average firing rate of the excitatory and the inhibitory neurons by dividing the total number of recorded spikes by the number of neurons recorded from and the simulation time. The multiplication by 1000.0 converts the unit 1/ms to 1/s=Hz. .. GENERATED FROM PYTHON SOURCE LINES 237-241 .. code-block:: Python rate_ex = events_ex / simtime * 1000.0 / N_rec rate_in = events_in / simtime * 1000.0 / N_rec .. GENERATED FROM PYTHON SOURCE LINES 242-245 Reading out the number of connections established using the excitatory and inhibitory synapse model. The numbers are summed up resulting in the total number of synapses. .. GENERATED FROM PYTHON SOURCE LINES 245-250 .. code-block:: Python num_synapses_ex = nest.GetDefaults("excitatory")["num_connections"] num_synapses_in = nest.GetDefaults("inhibitory")["num_connections"] num_synapses = num_synapses_ex + num_synapses_in .. GENERATED FROM PYTHON SOURCE LINES 251-253 Establishing the time it took to build and simulate the network by taking the difference of the pre-defined time variables. .. GENERATED FROM PYTHON SOURCE LINES 253-257 .. code-block:: Python build_time = endbuild - startbuild sim_time = endsimulate - endbuild .. GENERATED FROM PYTHON SOURCE LINES 258-259 Printing the network properties, firing rates and building times. .. GENERATED FROM PYTHON SOURCE LINES 259-270 .. code-block:: Python print("Brunel network simulation (Python)") print(f"Number of neurons : {N_neurons}") print(f"Number of synapses: {num_synapses}") print(f" Excitatory : {num_synapses_ex}") print(f" Inhibitory : {num_synapses_in}") print(f"Excitatory rate : {rate_ex:.2f} Hz") print(f"Inhibitory rate : {rate_in:.2f} Hz") print(f"Building time : {build_time:.2f} s") print(f"Simulation time : {sim_time:.2f} s") .. GENERATED FROM PYTHON SOURCE LINES 271-272 Plot a raster of the excitatory neurons and a histogram. .. GENERATED FROM PYTHON SOURCE LINES 272-275 .. code-block:: Python nest.raster_plot.from_device(espikes, hist=True) plt.show() .. _sphx_glr_download_auto_examples_brunel_delta_nest.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: brunel_delta_nest.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: brunel_delta_nest.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_