Large scale BRIAN2 simulation¶
This example involves a large scale simulation of a BRIAN2 network Using BRIAN2 with pypet. The example is taken from the Litwin-Kumar and Doiron paper from Nature neuroscience 2012.
It is split into three different modules: The clusternet.py file containing the network specification, the runscript.py file to start a simulation (you have to be patient, BRIAN simulations can take some time), and the plotff.py to plot the results of the parameter exploration, i.e. the Fano Factor as a function of the clustering parameter R_ee.
Download: clusternet.py
Download: runscript.py
Download: plotff.py
Clusternet¶
"""Module to run the clustered Neural Network Simulations as in Litwin-Kumar & Doiron 2012"""
__author__ = 'Robert Meyer'
import os
import numpy as np
import matplotlib.pyplot as plt
from pypet.trajectory import Trajectory
from pypet.brian2.parameter import Brian2Parameter, Brian2MonitorResult
from pypet.brian2.network import NetworkComponent, NetworkRunner, NetworkAnalyser
from brian2 import NeuronGroup, rand, Synapses, Equations, SpikeMonitor, StateMonitor, ms
def _explored_parameters_in_group(traj, group_node):
"""Checks if one the parameters in `group_node` is explored.
:param traj: Trajectory container
:param group_node: Group node
:return: `True` or `False`
"""
explored = False
for param in traj.f_get_explored_parameters():
if param in group_node:
explored = True
break
return explored
class CNNeuronGroup(NetworkComponent):
"""Class to create neuron groups.
Creates two groups of excitatory and inhibitory neurons.
"""
@staticmethod
def add_parameters(traj):
"""Adds all neuron group parameters to `traj`."""
assert(isinstance(traj,Trajectory))
scale = traj.simulation.scale
traj.v_standard_parameter = Brian2Parameter
model_eqs = '''dV/dt= 1.0/tau_POST * (mu - V) + I_syn : 1
mu : 1
I_syn = - I_syn_i + I_syn_e : Hz
'''
conn_eqs = '''I_syn_PRE = x_PRE/(tau2_PRE-tau1_PRE) : Hz
dx_PRE/dt = -(normalization_PRE*y_PRE+x_PRE)*invtau1_PRE : 1
dy_PRE/dt = -y_PRE*invtau2_PRE : 1
'''
traj.f_add_parameter('model.eqs', model_eqs,
comment='The differential equation for the neuron model')
traj.f_add_parameter('model.synaptic.eqs', conn_eqs,
comment='The differential equation for the synapses. '
'PRE will be replaced by `i` or `e` depending '
'on the source population')
traj.f_add_parameter('model.synaptic.tau1', 1*ms, comment = 'The decay time')
traj.f_add_parameter('model.synaptic.tau2_e', 3*ms, comment = 'The rise time, excitatory')
traj.f_add_parameter('model.synaptic.tau2_i', 2*ms, comment = 'The rise time, inhibitory')
traj.f_add_parameter('model.V_th', 'V >= 1.0', comment = "Threshold value")
traj.f_add_parameter('model.reset_func', 'V=0.0',
comment = "String representation of reset function")
traj.f_add_parameter('model.refractory', 5*ms, comment = "Absolute refractory period")
traj.f_add_parameter('model.N_e', int(2000*scale), comment = "Amount of excitatory neurons")
traj.f_add_parameter('model.N_i', int(500*scale), comment = "Amount of inhibitory neurons")
traj.f_add_parameter('model.tau_e', 15*ms, comment = "Membrane time constant, excitatory")
traj.f_add_parameter('model.tau_i', 10*ms, comment = "Membrane time constant, inhibitory")
traj.f_add_parameter('model.mu_e_min', 1.1, comment = "Lower bound for bias, excitatory")
traj.f_add_parameter('model.mu_e_max', 1.2, comment = "Upper bound for bias, excitatory")
traj.f_add_parameter('model.mu_i_min', 1.0, comment = "Lower bound for bias, inhibitory")
traj.f_add_parameter('model.mu_i_max', 1.05, comment = "Upper bound for bias, inhibitory")
@staticmethod
def _build_model_eqs(traj):
"""Computes model equations for the excitatory and inhibitory population.
Equation objects are created by fusing `model.eqs` and `model.synaptic.eqs`
and replacing `PRE` by `i` (for inhibitory) or `e` (for excitatory) depending
on the type of population.
:return: Dictionary with 'i' equation object for inhibitory neurons and 'e' for excitatory
"""
model_eqs = traj.model.eqs
post_eqs={}
for name_post in ['i','e']:
variables_dict ={}
new_model_eqs=model_eqs.replace('POST', name_post)
for name_pre in ['i', 'e']:
conn_eqs = traj.model.synaptic.eqs
new_conn_eqs = conn_eqs.replace('PRE', name_pre)
new_model_eqs += new_conn_eqs
tau1 = traj.model.synaptic['tau1']
tau2 = traj.model.synaptic['tau2_'+name_pre]
normalization = (tau1-tau2) / tau2
invtau1=1.0/tau1
invtau2 = 1.0/tau2
variables_dict['invtau1_'+name_pre] = invtau1
variables_dict['invtau2_'+name_pre] = invtau2
variables_dict['normalization_'+name_pre] = normalization
variables_dict['tau1_'+name_pre] = tau1
variables_dict['tau2_'+name_pre] = tau2
variables_dict['tau_'+name_post] = traj.model['tau_'+name_post]
post_eqs[name_post] = Equations(new_model_eqs, **variables_dict)
return post_eqs
def pre_build(self, traj, brian_list, network_dict):
"""Pre-builds the neuron groups.
Pre-build is only performed if none of the
relevant parameters is explored.
:param traj: Trajectory container
:param brian_list:
List of objects passed to BRIAN network constructor.
Adds:
Inhibitory neuron group
Excitatory neuron group
:param network_dict:
Dictionary of elements shared among the components
Adds:
'neurons_i': Inhibitory neuron group
'neurons_e': Excitatory neuron group
"""
self._pre_build = not _explored_parameters_in_group(traj, traj.parameters.model)
if self._pre_build:
self._build_model(traj, brian_list, network_dict)
def build(self, traj, brian_list, network_dict):
"""Builds the neuron groups.
Build is only performed if neuron group was not
pre-build before.
:param traj: Trajectory container
:param brian_list:
List of objects passed to BRIAN network constructor.
Adds:
Inhibitory neuron group
Excitatory neuron group
:param network_dict:
Dictionary of elements shared among the components
Adds:
'neurons_i': Inhibitory neuron group
'neurons_e': Excitatory neuron group
"""
if not hasattr(self, '_pre_build') or not self._pre_build:
self._build_model(traj, brian_list, network_dict)
def _build_model(self, traj, brian_list, network_dict):
"""Builds the neuron groups from `traj`.
Adds the neuron groups to `brian_list` and `network_dict`.
"""
model = traj.parameters.model
# Create the equations for both models
eqs_dict = self._build_model_eqs(traj)
# Create inhibitory neurons
eqs_i = eqs_dict['i']
neurons_i = NeuronGroup(N=model.N_i,
model = eqs_i,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
method='Euler')
# Create excitatory neurons
eqs_e = eqs_dict['e']
neurons_e = NeuronGroup(N=model.N_e,
model = eqs_e,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
method='Euler')
# Set the bias terms
neurons_e.mu =rand(model.N_e) * (model.mu_e_max - model.mu_e_min) + model.mu_e_min
neurons_i.mu =rand(model.N_i) * (model.mu_i_max - model.mu_i_min) + model.mu_i_min
# Set initial membrane potentials
neurons_e.V = rand(model.N_e)
neurons_i.V = rand(model.N_i)
# Add both groups to the `brian_list` and the `network_dict`
brian_list.append(neurons_i)
brian_list.append(neurons_e)
network_dict['neurons_e']=neurons_e
network_dict['neurons_i']=neurons_i
class CNConnections(NetworkComponent):
"""Class to connect neuron groups.
In case of no clustering `R_ee=1,0` there are 4 connection instances (i->i, i->e, e->i, e->e).
Otherwise there are 3 + 3*N_c-2 connections with N_c the number of clusters
(i->i, i->e, e->i, N_c conns within cluster, 2*N_c-2 connections from cluster to outside).
"""
@staticmethod
def add_parameters(traj):
"""Adds all neuron group parameters to `traj`."""
assert(isinstance(traj,Trajectory))
traj.v_standard_parameter = Brian2Parameter
scale = traj.simulation.scale
traj.f_add_parameter('connections.R_ee', 1.0, comment='Scaling factor for clustering')
traj.f_add_parameter('connections.clustersize_e', 100, comment='Size of a cluster')
traj.f_add_parameter('connections.strength_factor', 2.5,
comment='Factor for scaling cluster weights')
traj.f_add_parameter('connections.p_ii', 0.25,
comment='Connection probability from inhibitory to inhibitory' )
traj.f_add_parameter('connections.p_ei', 0.25,
comment='Connection probability from inhibitory to excitatory' )
traj.f_add_parameter('connections.p_ie', 0.25,
comment='Connection probability from excitatory to inhibitory' )
traj.f_add_parameter('connections.p_ee', 0.1,
comment='Connection probability from excitatory to excitatory' )
traj.f_add_parameter('connections.J_ii', 0.027/np.sqrt(scale),
comment='Connection strength from inhibitory to inhibitory')
traj.f_add_parameter('connections.J_ei', 0.032/np.sqrt(scale),
comment='Connection strength from inhibitory to excitatroy')
traj.f_add_parameter('connections.J_ie', 0.009/np.sqrt(scale),
comment='Connection strength from excitatory to inhibitory')
traj.f_add_parameter('connections.J_ee', 0.012/np.sqrt(scale),
comment='Connection strength from excitatory to excitatory')
def pre_build(self, traj, brian_list, network_dict):
"""Pre-builds the connections.
Pre-build is only performed if none of the
relevant parameters is explored and the relevant neuron groups
exist.
:param traj: Trajectory container
:param brian_list:
List of objects passed to BRIAN network constructor.
Adds:
Connections, amount depends on clustering
:param network_dict:
Dictionary of elements shared among the components
Expects:
'neurons_i': Inhibitory neuron group
'neurons_e': Excitatory neuron group
Adds:
Connections, amount depends on clustering
"""
self._pre_build = not _explored_parameters_in_group(traj, traj.parameters.connections)
self._pre_build = (self._pre_build and 'neurons_i' in network_dict and
'neurons_e' in network_dict)
if self._pre_build:
self._build_connections(traj, brian_list, network_dict)
def build(self, traj, brian_list, network_dict):
"""Builds the connections.
Build is only performed if connections have not
been pre-build.
:param traj: Trajectory container
:param brian_list:
List of objects passed to BRIAN network constructor.
Adds:
Connections, amount depends on clustering
:param network_dict:
Dictionary of elements shared among the components
Expects:
'neurons_i': Inhibitory neuron group
'neurons_e': Excitatory neuron group
Adds:
Connections, amount depends on clustering
"""
if not hasattr(self, '_pre_build') or not self._pre_build:
self._build_connections(traj, brian_list, network_dict)
def _build_connections(self, traj, brian_list, network_dict):
"""Connects neuron groups `neurons_i` and `neurons_e`.
Adds all connections to `brian_list` and adds a list of connections
with the key 'connections' to the `network_dict`.
"""
connections = traj.connections
neurons_i = network_dict['neurons_i']
neurons_e = network_dict['neurons_e']
print('Connecting ii')
self.conn_ii = Synapses(neurons_i,neurons_i, on_pre='y_i += %f' % connections.J_ii)
self.conn_ii.connect('i != j', p=connections.p_ii)
print('Connecting ei')
self.conn_ei = Synapses(neurons_i,neurons_e, on_pre='y_i += %f' % connections.J_ei)
self.conn_ei.connect('i != j', p=connections.p_ei)
print('Connecting ie')
self.conn_ie = Synapses(neurons_e,neurons_i, on_pre='y_e += %f' % connections.J_ie)
self.conn_ie.connect('i != j', p=connections.p_ie)
conns_list = [self.conn_ii, self.conn_ei, self.conn_ie]
if connections.R_ee > 1.0:
# If we come here we want to create clusters
cluster_list=[]
cluster_conns_list=[]
model=traj.model
# Compute the number of clusters
clusters = int(model.N_e/connections.clustersize_e)
traj.f_add_derived_parameter('connections.clusters', clusters, comment='Number of clusters')
# Compute outgoing connection probability
p_out = (connections.p_ee*model.N_e) / \
(connections.R_ee*connections.clustersize_e+model.N_e- connections.clustersize_e)
# Compute within cluster connection probability
p_in = p_out * connections.R_ee
# We keep these derived parameters
traj.f_add_derived_parameter('connections.p_ee_in', p_in ,
comment='Connection prob within cluster')
traj.f_add_derived_parameter('connections.p_ee_out', p_out ,
comment='Connection prob to outside of cluster')
low_index = 0
high_index = connections.clustersize_e
# Iterate through cluster and connect within clusters and to the rest of the neurons
for irun in range(clusters):
cluster = neurons_e[low_index:high_index]
# Connections within cluster
print('Connecting ee cluster #%d of %d' % (irun, clusters))
conn = Synapses(cluster,cluster,
on_pre='y_e += %f' % (connections.J_ee*connections.strength_factor))
conn.connect('i != j', p=p_in)
cluster_conns_list.append(conn)
# Connections reaching out from cluster
# A cluster consists of `clustersize_e` neurons with consecutive indices.
# So usually the outside world consists of two groups, neurons with lower
# indices than the cluster indices, and neurons with higher indices.
# Only the clusters at the index boundaries project to neurons with only either
# lower or higher indices
if low_index > 0:
rest_low = neurons_e[0:low_index]
print('Connecting cluster with other neurons of lower index')
low_conn = Synapses(cluster,rest_low,
on_pre='y_e += %f' % connections.J_ee)
low_conn.connect('i != j', p=p_out)
cluster_conns_list.append(low_conn)
if high_index < model.N_e:
rest_high = neurons_e[high_index:model.N_e]
print('Connecting cluster with other neurons of higher index')
high_conn = Synapses(cluster,rest_high,
on_pre='y_e += %f' % connections.J_ee)
high_conn.connect('i != j', p=p_out)
cluster_conns_list.append(high_conn)
low_index=high_index
high_index+=connections.clustersize_e
self.cluster_conns=cluster_conns_list
conns_list+=cluster_conns_list
else:
# Here we don't cluster and connection probabilities are homogeneous
print('Connectiong ee')
self.conn_ee = Synapses(neurons_e,neurons_e,
on_pre='y_e += %f' % connections.J_ee)
self.conn_ee.connect('i != j', p=connections.p_ee)
conns_list.append(self.conn_ee)
# Add the connections to the `brian_list` and the network dict
brian_list.extend(conns_list)
network_dict['connections'] = conns_list
class CNNetworkRunner(NetworkRunner):
"""Runs the network experiments.
Adds two BrianParameters, one for an initial run, and one for a run
that is actually measured.
"""
def add_parameters(self, traj):
"""Adds all necessary parameters to `traj` container."""
par= traj.f_add_parameter(Brian2Parameter,'simulation.durations.initial_run', 500*ms,
comment='Initialisation run for more realistic '
'measurement conditions.')
par.v_annotations.order=0
par=traj.f_add_parameter(Brian2Parameter,'simulation.durations.measurement_run', 1500*ms,
comment='Measurement run that is considered for '
'statistical evaluation')
par.v_annotations.order=1
class CNFanoFactorComputer(NetworkAnalyser):
"""Computes the FanoFactor if the MonitorAnalyser has extracted data"""
def add_parameters(self, traj):
traj.f_add_parameter('analysis.statistics.time_window', 100*ms , 'Time window for FF computation')
traj.f_add_parameter('analysis.statistics.neuron_ids', tuple(range(500)),
comment= 'Neurons to be taken into account to compute FF')
@staticmethod
def _compute_fano_factor(spike_res, neuron_id, time_window, start_time, end_time):
"""Computes Fano Factor for one neuron.
:param spike_res:
Result containing the spiketimes of all neurons
:param neuron_id:
Index of neuron for which FF is computed
:param time_window:
Length of the consecutive time windows to compute the FF
:param start_time:
Start time of measurement to consider
:param end_time:
End time of measurement to consider
:return:
Fano Factor (float) or
returns 0 if mean firing activity is 0.
"""
assert(end_time >= start_time+time_window)
# Number of time bins
bins = (end_time-start_time)/time_window
bins = int(np.floor(bins))
# Arrays for binning of spike counts
binned_spikes = np.zeros(bins)
# DataFrame only containing spikes of the particular neuron
spike_array_neuron = spike_res.t[spike_res.i==neuron_id]
for bin in range(bins):
# We iterate over the bins to calculate the spike counts
lower_time = start_time+time_window*bin
upper_time = start_time+time_window*(bin+1)
# Filter the spikes
spike_array_interval = spike_array_neuron[spike_array_neuron >= lower_time]
spike_array_interval = spike_array_interval[spike_array_interval < upper_time]
# Add count to bins
spikes = len(spike_array_interval)
binned_spikes[bin]=spikes
var = np.var(binned_spikes)
avg = np.mean(binned_spikes)
if avg > 0:
return var/float(avg)
else:
return 0
@staticmethod
def _compute_mean_fano_factor( neuron_ids, spike_res, time_window, start_time, end_time):
"""Computes average Fano Factor over many neurons.
:param neuron_ids:
List of neuron indices to average over
:param spike_res:
Result containing all the spikes
:param time_window:
Length of the consecutive time windows to compute the FF
:param start_time:
Start time of measurement to consider
:param end_time:
End time of measurement to consider
:return:
Average fano factor
"""
ffs = np.zeros(len(neuron_ids))
for idx, neuron_id in enumerate(neuron_ids):
ff=CNFanoFactorComputer._compute_fano_factor(
spike_res, neuron_id, time_window, start_time, end_time)
ffs[idx]=ff
mean_ff = np.mean(ffs)
return mean_ff
def analyse(self, traj, network, current_subrun, subrun_list, network_dict):
"""Calculates average Fano Factor of a network.
:param traj:
Trajectory container
Expects:
`results.monitors.spikes_e`: Data from SpikeMonitor for excitatory neurons
Adds:
`results.statistics.mean_fano_factor`: Average Fano Factor
:param network:
The BRIAN network
:param current_subrun:
BrianParameter
:param subrun_list:
Upcoming subruns, analysis is only performed if subruns is empty,
aka the final subrun has finished.
:param network_dict:
Dictionary of items shared among componetns
"""
#Check if we finished all subruns
if len(subrun_list)==0:
spikes_e = traj.results.monitors.spikes_e
time_window = traj.parameters.analysis.statistics.time_window
start_time = traj.parameters.simulation.durations.initial_run
end_time = start_time+traj.parameters.simulation.durations.measurement_run
neuron_ids = traj.parameters.analysis.statistics.neuron_ids
mean_ff = self._compute_mean_fano_factor(
neuron_ids, spikes_e, time_window, start_time, end_time)
traj.f_add_result('statistics.mean_fano_factor', mean_ff, comment='Average Fano '
'Factor over all '
'exc neurons')
print('R_ee: %f, Mean FF: %f' % (traj.R_ee, mean_ff))
class CNMonitorAnalysis(NetworkAnalyser):
"""Adds monitors for recoding and plots the monitor output."""
@staticmethod
def add_parameters( traj):
traj.f_add_parameter('analysis.neuron_records',(0,1,100,101),
comment='Neuron indices to record from.')
traj.f_add_parameter('analysis.plot_folder',
os.path.join('experiments', 'example_24', 'PLOTS'),
comment='Folder for plots')
traj.f_add_parameter('analysis.show_plots', 0, comment='Whether to show plots.')
traj.f_add_parameter('analysis.make_plots', 1, comment='Whether to make plots.')
def add_to_network(self, traj, network, current_subrun, subrun_list, network_dict):
"""Adds monitors to the network if the measurement run is carried out.
:param traj: Trajectory container
:param network: The BRIAN network
:param current_subrun: BrianParameter
:param subrun_list: List of coming subrun_list
:param network_dict:
Dictionary of items shared among the components
Expects:
'neurons_e': Excitatory neuron group
Adds:
'monitors': List of monitors
0. SpikeMonitor of excitatory neurons
1. StateMonitor of membrane potential of some excitatory neurons
(specified in `neuron_records`)
2. StateMonitor of excitatory synaptic currents of some excitatory neurons
3. State monitor of inhibitory currents of some excitatory neurons
"""
if current_subrun.v_annotations.order == 1:
self._add_monitors(traj, network, network_dict)
def _add_monitors(self, traj, network, network_dict):
"""Adds monitors to the network"""
neurons_e = network_dict['neurons_e']
monitor_list = []
# Spiketimes
self.spike_monitor = SpikeMonitor(neurons_e)
monitor_list.append(self.spike_monitor)
# Membrane Potential
self.V_monitor = StateMonitor(neurons_e,'V',
record=list(traj.neuron_records))
monitor_list.append(self.V_monitor)
# Exc. syn .Current
self.I_syn_e_monitor = StateMonitor(neurons_e, 'I_syn_e',
record=list(traj.neuron_records))
monitor_list.append(self.I_syn_e_monitor)
# Inh. syn. Current
self.I_syn_i_monitor = StateMonitor(neurons_e, 'I_syn_i',
record=list(traj.neuron_records))
monitor_list.append(self.I_syn_i_monitor)
# Add monitors to network and dictionary
network.add(*monitor_list)
network_dict['monitors'] = monitor_list
def _make_folder(self, traj):
"""Makes a subfolder for plots.
:return: Path name to print folder
"""
print_folder = os.path.join(traj.analysis.plot_folder,
traj.v_name, traj.v_crun)
print_folder = os.path.abspath(print_folder)
if not os.path.isdir(print_folder):
os.makedirs(print_folder)
return print_folder
def _plot_result(self, traj, result_name):
"""Plots a state variable graph for several neurons into one figure"""
result = traj.f_get(result_name)
varname = result.record_variables[0]
values = result[varname]
times = result.t
record = result.record
for idx, celia_neuron in enumerate(record):
plt.subplot(len(record), 1, idx+1)
plt.plot(times, values[idx,:])
if idx==0:
plt.title('%s' % varname)
if idx==1:
plt.ylabel('%s' % ( varname))
if idx == len(record)-1:
plt.xlabel('t')
def _print_graphs(self, traj):
"""Makes some plots and stores them into subfolders"""
print_folder = self._make_folder(traj)
# If we use BRIAN's own raster_plot functionality we
# need to sue the SpikeMonitor directly
plt.figure()
plt.scatter(self.spike_monitor.t, self.spike_monitor.i, s=1)
plt.xlabel('t')
plt.ylabel('Exc. Neurons')
plt.title('Spike Raster Plot')
filename=os.path.join(print_folder,'spike.png')
print('Current plot: %s ' % filename)
plt.savefig(filename)
plt.close()
fig=plt.figure()
self._plot_result(traj, 'monitors.V')
filename=os.path.join(print_folder,'V.png')
print('Current plot: %s ' % filename)
fig.savefig(filename)
plt.close()
plt.figure()
self._plot_result(traj, 'monitors.I_syn_e')
filename=os.path.join(print_folder,'I_syn_e.png')
print('Current plot: %s ' % filename)
plt.savefig(filename)
plt.close()
plt.figure()
self._plot_result(traj, 'monitors.I_syn_i')
filename=os.path.join(print_folder,'I_syn_i.png')
print('Current plot: %s ' % filename)
plt.savefig(filename)
plt.close()
if not traj.analysis.show_plots:
plt.close('all')
else:
plt.show()
def analyse(self, traj, network, current_subrun, subrun_list, network_dict):
"""Extracts monitor data and plots.
Data extraction is done if all subruns have been completed,
i.e. `len(subrun_list)==0`
First, extracts results from the monitors and stores them into `traj`.
Next, uses the extracted data for plots.
:param traj:
Trajectory container
Adds:
Data from monitors
:param network: The BRIAN network
:param current_subrun: BrianParameter
:param subrun_list: List of coming subruns
:param network_dict: Dictionary of items shared among all components
"""
if len(subrun_list)==0:
traj.f_add_result(Brian2MonitorResult, 'monitors.spikes_e', self.spike_monitor,
comment = 'The spiketimes of the excitatory population')
traj.f_add_result(Brian2MonitorResult, 'monitors.V', self.V_monitor,
comment = 'Membrane voltage of four neurons from 2 clusters')
traj.f_add_result(Brian2MonitorResult, 'monitors.I_syn_e', self.I_syn_e_monitor,
comment = 'I_syn_e of four neurons from 2 clusters')
traj.f_add_result(Brian2MonitorResult, 'monitors.I_syn_i', self.I_syn_i_monitor,
comment = 'I_syn_i of four neurons from 2 clusters')
print('Plotting')
if traj.parameters.analysis.make_plots:
self._print_graphs(traj)
Runscript¶
"""Starting script to run a network simulation of the clustered network
by Litwin-Kumar and Doiron (Nature neuroscience 2012).
The network has been implemented using the *pypet* network framework.
"""
__author__ = 'Robert Meyer'
import numpy as np
import os # To allow path names work under Windows and Linux
import brian2
brian2.prefs.codegen.target = 'numpy'
from pypet.environment import Environment
from pypet.brian2.network import NetworkManager
from clusternet import CNMonitorAnalysis, CNNeuronGroup, CNNetworkRunner, CNConnections,\
CNFanoFactorComputer
def main():
filename = os.path.join('hdf5', 'Clustered_Network.hdf5')
env = Environment(trajectory='Clustered_Network',
add_time=False,
filename=filename,
continuable=False,
lazy_debug=False,
multiproc=True,
ncores=4,
use_pool=False, # We cannot use a pool, our network cannot be pickled
wrap_mode='QUEUE',
overwrite_file=True)
#Get the trajectory container
traj = env.trajectory
# We introduce a `meta` parameter that we can use to easily rescale our network
scale = 1.0 # To obtain the results from the paper scale this to 1.0
# Be aware that your machine will need a lot of memory then!
traj.f_add_parameter('simulation.scale', scale,
comment='Meta parameter that can scale default settings. '
'Rescales number of neurons and connections strenghts, but '
'not the clustersize.')
# We create a Manager and pass all our components to the Manager.
# Note the order, CNNeuronGroups are scheduled before CNConnections,
# and the Fano Factor computation depends on the CNMonitorAnalysis
clustered_network_manager = NetworkManager(network_runner=CNNetworkRunner(),
component_list=(CNNeuronGroup(), CNConnections()),
analyser_list=(CNMonitorAnalysis(),CNFanoFactorComputer()))
# Add original parameters (but scaled according to `scale`)
clustered_network_manager.add_parameters(traj)
# We need `tolist` here since our parameter is a python float and not a
# numpy float.
explore_list = np.arange(1.0, 3.5, 0.4).tolist()
# Explore different values of `R_ee`
traj.f_explore({'R_ee' : explore_list})
# Pre-build network components
clustered_network_manager.pre_build(traj)
# Run the network simulation
traj.f_store() # Let's store the parameters already before the run
env.run(clustered_network_manager.run_network)
# Finally disable logging and close all log-files
env.disable_logging()
if __name__=='__main__':
main()
Plotff¶
"""Script to plot the fano factor graph for a given simulation
stored as a trajectory to an HDF5 file.
"""
__author__ = 'Robert Meyer'
import os
import matplotlib.pyplot as plt
from pypet import Trajectory, Environment
from pypet.brian2.parameter import Brian2MonitorResult, Brian2Parameter
def main():
filename = os.path.join('hdf5', 'Clustered_Network.hdf5')
# If we pass a filename to the trajectory a new HDF5StorageService will
# be automatically created
traj = Trajectory(filename=filename,
dynamically_imported_classes=[Brian2MonitorResult,
Brian2Parameter])
# Let's create and fake environment to enable logging:
env = Environment(traj, do_single_runs=False)
# Load the trajectory, but onyl laod the skeleton of the results
traj.f_load(index=-1, load_parameters=2, load_derived_parameters=2, load_results=1)
# Find the result instances related to the fano factor
fano_dict = traj.f_get_from_runs('mean_fano_factor', fast_access=False)
# Load the data of the fano factor results
ffs = fano_dict.values()
traj.f_load_items(ffs)
# Extract all values and R_ee values for each run
ffs_values = [x.f_get() for x in ffs]
Rees = traj.f_get('R_ee').f_get_range()
# Plot average fano factor as a function of R_ee
plt.plot(Rees, ffs_values)
plt.xlabel('R_ee')
plt.ylabel('Avg. Fano Factor')
plt.show()
# Finally disable logging and close all log-files
env.disable_logging()
if __name__ == '__main__':
main()