GSoC 2026 Project 2: Initial Sphinx-Gallery Infrastructure Setup

docs_sphinx/conf.py

@@ -101,6 +101,7 @@ def __getattr__(cls, name):
     "sphinx.ext.autosummary",
     "sphinx.ext.extlinks",
     "sphinx_tabs.tabs",
+    "sphinx_gallery.gen_gallery"
 ]
 
 # Set readthedocs theme if available
@@ -348,3 +349,8 @@ def __getattr__(cls, name):
 
 # Configure linking to github
 extlinks = {"issue": ("https://github.com/brian-team/brian2/issues/%s", "# %s")}
+sphinx_gallery_conf = {
+    'examples_dirs': '../examples',   # path to your example scripts
+    'gallery_dirs': 'auto_examples',  # path to where to save gallery generated output
+    'filename_pattern': '/plot_',     # Only execute files starting with plot_
+}

examples/README.rst

@@ -0,0 +1,4 @@
+Brian 2 Examples Gallery
+========================
+
+Welcome to the Brian 2 examples gallery. Here are the converted scripts!

examples/plot_CUBA.py

@@ -0,0 +1,83 @@
+"""
+Example: CUBA benchmark
+=======================
+
+This is a Brian script implementing a benchmark described in the following review paper:
+
+Simulation of networks of spiking neurons: A review of tools and strategies (2007). 
+Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe, 
+Natschlager, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller, 
+Davison, El Boustani and Destexhe. Journal of Computational Neuroscience 23(3):349-98
+
+Benchmark 2: random network of integrate-and-fire neurons with exponential synaptic currents.
+
+Clock-driven implementation with exact subthreshold integration (but spike times are aligned to the grid).
+"""
+
+from brian2 import *
+import matplotlib.pyplot as plt
+# %%
+# Setting up the parameters
+# -------------------------
+# First, we define all the time constants and voltages needed for the simulation.
+
+taum = 20*ms
+taue = 5*ms
+taui = 10*ms
+Vt = -50*mV
+Vr = -60*mV
+El = -49*mV
+
+# %%
+# Defining the Equations
+# ----------------------
+# Next, we write the differential equations that control the integrate-and-fire neurons.
+
+eqs = '''
+dv/dt  = (ge+gi-(v-El))/taum : volt (unless refractory)
+dge/dt = -ge/taue : volt
+dgi/dt = -gi/taui : volt
+'''
+
+# %%
+# Creating the Neuron Group
+# -------------------------
+# We create a population of 4000 neurons using the exact integration method.
+
+P = NeuronGroup(4000, eqs, threshold='v>Vt', reset='v = Vr', refractory=5*ms,
+                method='exact')
+P.v = 'Vr + rand() * (Vt - Vr)'
+P.ge = 0*mV
+P.gi = 0*mV
+
+# %%
+# Setting up Synapses
+# -------------------
+# We split the network into excitatory (first 3200 neurons) and inhibitory (remaining 800) connections.
+
+we = (60*0.27/10)*mV # excitatory synaptic weight (voltage)
+wi = (-20*4.5/10)*mV # inhibitory synaptic weight
+Ce = Synapses(P, P, on_pre='ge += we')
+Ci = Synapses(P, P, on_pre='gi += wi')
+Ce.connect('i<3200', p=0.02)
+Ci.connect('i>=3200', p=0.02)
+
+# %%
+# Running the Simulation
+# ----------------------
+# We record the spikes and run the simulation for 1 second of biological time.
+
+s_mon = SpikeMonitor(P)
+run(1 * second)
+
+# %%
+# Plotting the Results
+# --------------------
+# Finally, we generate a raster plot to visualize the firing pattern of the network.
+# In the Sphinx-Gallery documentation, this plot will automatically appear right below this text!
+
+plt.figure() # This tells Sphinx-Gallery "Get the camera ready!"
+plt.plot(s_mon.t/ms, s_mon.i, ',k')
+plt.xlabel('Time (ms)')
+plt.ylabel('Neuron index')
+plt.show() # This takes the picture