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Updates 2026 #23

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80db529
Fix some things that have changed, and rework to be simpler to use
rowleya Mar 4, 2026
de55466
Use the default Poisson delay and only update for SpiNNaker
rowleya Mar 4, 2026
e43c014
Remove comma (oops)
rowleya Mar 4, 2026
0cbef40
Fixes suggested
rowleya Mar 4, 2026
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49 changes: 0 additions & 49 deletions PyNN/example.py
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90 changes: 90 additions & 0 deletions PyNN/example_nest.py
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# -*- coding: utf-8 -*-
"""
PyNN microcircuit example
---------------------------

Example file to run the microcircuit on NEST.

Based on original PyNEST version by Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016
Adapted for PyNN by Andrew Davison, December 2017

"""

import time
import numpy as np
import os

import network
from network_params import net_dict
from stimulus_params import stim_dict

import pyNN.nest as sim


def setup_nest():
# Masterseed for PyNN and NumPy.
master_seed = 55
# Number of threads per MPI process.
local_num_threads = 1
# Simulation resolution
sim_resolution = 0.1

N_tp = sim.num_processes() * local_num_threads
rng_seeds = list(range(master_seed + 1 + N_tp, master_seed + 1 + (2 * N_tp)))
grng_seed = master_seed + N_tp
sim.setup(timestep=sim_resolution,
threads=local_num_threads,
grng_seed=grng_seed,
rng_seeds=rng_seeds)
if sim.rank() == 0:
print('Master seed: %i ' % master_seed)
print('Number of total processes: %i' % N_tp)
print('Seeds for random number generators of virtual processes: %r' % rng_seeds)
print('Global random number generator seed: %i' % grng_seed)


sim_dict = {
# Simulation time (in ms).
't_sim': 1000.0,
# Path to save the output data.
'data_path': os.path.join(os.getcwd(), 'data_nest'),
# Recording interval of the membrane potential (in ms).
'rec_V_int': 1.0,
# Fraction of neurons from which to record membrane potentials
'frac_record_v' : 0.1,
# Type of voltage init type between 'random' and 'pop_random'
'v_init_type': 'random',
# Function to call to setup
'setup_func': setup_nest
}


# Initialize the network and pass parameters to it.
tic = time.time()
net = network.Network(sim, sim_dict, net_dict, stim_dict)
toc = time.time() - tic
print("Time to initialize the network: %.2f s" % toc)
# Connect all nodes.
tic = time.time()
net.setup()
toc = time.time() - tic
print("Time to create the connections: %.2f s" % toc)
# Simulate.
tic = time.time()
net.simulate()
toc = time.time() - tic
print("Time to simulate: %.2f s" % toc)
tic = time.time()
net.write_data()
toc = time.time() - tic
print("Time to write data: %.2f s" % toc)
# Plot a raster plot of the spikes of the simulated neurons and the average
# spike rate of all populations. For visual purposes only spikes 100 ms
# before and 100 ms after the thalamic stimulus time are plotted here by
# default. The computation of spike rates discards the first 500 ms of
# the simulation to exclude initialization artifacts.
raster_plot_time_idx = np.array(
[stim_dict['th_start'] - 100.0, stim_dict['th_start'] + 100.0]
)
fire_rate_time_idx = np.array([500.0, sim_dict['t_sim']])
net.evaluate(raster_plot_time_idx, fire_rate_time_idx)
105 changes: 105 additions & 0 deletions PyNN/example_spinnaker.py
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# -*- coding: utf-8 -*-
"""
PyNN microcircuit example
---------------------------

Example file to run the microcircuit on SpiNNaker.

Based on original PyNEST version by Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016
Adapted for PyNN by Andrew Davison, December 2017

"""

import time
import numpy as np
import os

import network
from network_params import net_dict
from stimulus_params import stim_dict

import pyNN.spiNNaker as sim


def setup_spinnaker():
# Simulation resolution
sim_resolution = 0.1

if not os.path.exists("spynnaker.cfg"):
with open("spynnaker.cfg", "w") as f:
print("[Machine]", file=f)
print("timeScaleFactor = 1", file=f)
print("enable_reinjection = False", file=f)
print("", file=f)
print("[Buffers]", file=f)
print("use_auto_pause_and_resume = False", file=f)
print("", file=f)
print("[Simulation]", file=f)
print("incoming_spike_buffer_size = 1024", file=f)
print("drop_late_spikes = False", file=f)
print("", file=f)
print("[Reports]", file=f)
print("read_router_provenance_data = True", file=f)
print("read_graph_provenance_data = True", file=f)
print("read_placements_provenance_data = True", file=f)
print("write_provenance = True", file=f)

sim.setup(timestep=sim_resolution)
sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 64)
sim.set_number_of_neurons_per_core(sim.SpikeSourcePoisson, 64)
sim.set_allow_delay_extensions(sim.IF_curr_exp, False)


sim_dict = {
# Simulation time (in ms).
't_sim': 1000.0,
# Path to save the output data.
'data_path': os.path.join(os.getcwd(), 'data_spinnaker'),
# Recording interval of the membrane potential (in ms).
'rec_V_int': 1.0,
# Fraction of neurons from which to record membrane potentials
'frac_record_v' : 0.0,
# Type of voltage init type between 'random' and 'pop_random'
'v_init_type': 'pop_random',
# Function to call to setup
'setup_func': setup_spinnaker
}

# Run at full scale!
net_dict['K_scaling'] = 1.0
net_dict['N_scaling'] = 1.0

# SpiNNaker Poisson at min-delay will use direct input mode,
# which is more efficient with high-rate sources
net_dict['poisson_delay'] = 0.1


# Initialize the network and pass parameters to it.
tic = time.time()
net = network.Network(sim, sim_dict, net_dict, stim_dict)
toc = time.time() - tic
print("Time to initialize the network: %.2f s" % toc)
# Connect all nodes.
tic = time.time()
net.setup()
toc = time.time() - tic
print("Time to create the connections: %.2f s" % toc)
# Simulate.
tic = time.time()
net.simulate()
toc = time.time() - tic
print("Time to simulate: %.2f s" % toc)
tic = time.time()
net.write_data()
toc = time.time() - tic
print("Time to write data: %.2f s" % toc)
# Plot a raster plot of the spikes of the simulated neurons and the average
# spike rate of all populations. For visual purposes only spikes 100 ms
# before and 100 ms after the thalamic stimulus time are plotted here by
# default. The computation of spike rates discards the first 500 ms of
# the simulation to exclude initialization artifacts.
raster_plot_time_idx = np.array(
[stim_dict['th_start'] - 100.0, stim_dict['th_start'] + 100.0]
)
fire_rate_time_idx = np.array([500.0, sim_dict['t_sim']])
net.evaluate(raster_plot_time_idx, fire_rate_time_idx)
2 changes: 1 addition & 1 deletion PyNN/helpers.py
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Expand Up @@ -107,7 +107,7 @@ def get_total_number_of_synapses(net_dict):
prod = np.outer(N_full, N_full)
n_syn_temp = np.log(1. - conn_probs)/np.log((prod - 1.) / prod)
N_full_matrix = np.column_stack(
(N_full for i in list(range(number_N)))
[N_full for i in list(range(number_N))]
)
# If the network is scaled the indegrees are calculated in the same
# fashion as in the original version of the circuit, which is
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