Compiling and using pyNN + NEST + python3
PyNN is a neural simulation language which works well with the NEST simulator. Here I show my progress in using both with python 3 and how to show results in a notebook.
First, let's install NEST. Note that the compiler switches to cmake are only necessary on OSX; under Linux, things should work without them.
git clone https://github.com/nest/nest-simulator.git
cd nest-simulator
cmake -DCMAKE_C_COMPILER=gcc-6 -DCMAKE_CXX_COMPILER=g++-6 -DCMAKE_INSTALL_PREFIX:PATH=/usr/local/nest -DPYTHON_EXECUTABLE=/usr/local/bin/python3 -DPYTHON_INCLUDE_DIR=/usr/local/Frameworks/Python.framework/Versions/3.5/include/python3.5m/ -DPYTHON_LIBRARY=/usr/local/Frameworks/Python.framework/Versions/3.5/lib/libpython3.5.dylib . make
make install
Then, you have to update your shell using:
# NEST
export PATH=/usr/local/nest/bin:$PATH
export PYTHONPATH=/usr/local/nest/lib/python1.4/site-packages
and now pyNN:
git clone https://github.com/NeuralEnsemble/PyNN
cd pyNN
pip3 install -e .
examples/tools
ipython3 run_all_examples.py NEST
I (still) used this patch:
diff --git a/pyNN/nest/simulator.py b/pyNN/nest/simulator.py
index 625c121..335f925 100644
--- a/pyNN/nest/simulator.py
+++ b/pyNN/nest/simulator.py
@@ -78,7 +78,7 @@ class _State(common.control.BaseState):
def min_delay(self):
# this rather complex implementation is needed to handle min_delay='auto'
kernel_delay = nest.GetKernelStatus('min_delay')
- syn_delay = nest.GetDefaults('static_synapse')['min_delay']
+ syn_delay = nest.GetDefaults('static_synapse')['delay']
if syn_delay == numpy.inf or syn_delay > 1e300:
return kernel_delay
else:
In [1]:
%matplotlib inline
import pyNN.nest as sim
import numpy
from pyNN.utility import get_simulator, init_logging, normalized_filename
from pyNN.parameters import Sequence
from pyNN.random import RandomDistribution as rnd
# === Define parameters ========================================================
n = 20 # Number of cells
w = 0.002 # synaptic weight (µS)
cell_params = {
'tau_m' : 20.0, # (ms)
'tau_syn_E' : 2.0, # (ms)
'tau_syn_I' : 4.0, # (ms)
'e_rev_E' : 0.0, # (mV)
'e_rev_I' : -70.0, # (mV)
'tau_refrac' : 2.0, # (ms)
'v_rest' : -60.0, # (mV)
'v_reset' : -70.0, # (mV)
'v_thresh' : -50.0, # (mV)
'cm' : 0.5} # (nF)
dt = 0.1 # (ms)
syn_delay = 1.0 # (ms)
input_rate = 50.0 # (Hz)
simtime = 1000.0 # (ms)
# === Build the network ========================================================
sim.setup()
cells = sim.Population(n, sim.IF_cond_alpha(**cell_params),
initial_values={'v': rnd('uniform', (-60.0, -50.0))},
label="cells")
number = int(2*simtime*input_rate/1000.0)
numpy.random.seed(26278342)
def generate_spike_times(i):
gen = lambda: Sequence(numpy.add.accumulate(numpy.random.exponential(1000.0/input_rate, size=number)))
if hasattr(i, "__len__"):
return [gen() for j in i]
else:
return gen()
assert generate_spike_times(0).max() > simtime
spike_source = sim.Population(n, sim.SpikeSourceArray(spike_times=generate_spike_times))
spike_source.record('spikes')
cells.record('spikes')
cells[0:2].record(('v', 'gsyn_exc'))
syn = sim.StaticSynapse(weight=w,delay=syn_delay)
input_conns = sim.Projection(spike_source, cells, sim.FixedProbabilityConnector(0.5), syn)
# === Run simulation ===========================================================
sim.run(simtime)
print("Mean firing rate: ", cells.mean_spike_count()*1000.0/simtime, "Hz")
# plotting
from pyNN.utility.plotting import Figure, Panel
data = cells.get_data().segments[0]
vm = data.filter(name="v")[0]
gsyn = data.filter(name="gsyn_exc")[0]
Figure(
Panel(vm, ylabel="Membrane potential (mV)"),
Panel(gsyn, ylabel="Synaptic conductance (uS)"),
Panel(data.spiketrains, xlabel="Time (ms)", xticks=True)
)
# === Clean up and quit ========================================================
sim.end()
