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environment.cpython-311.pyc
brunel_example.py 9.80 KiB
"""
Definition of spatially extended Brunel network.
This module provides layer and projections declarations suitable for
use with the NEST Topology Module.
It defines a Brunel-style network with neurons placed on a regular grid.
Connectivity is probabilitstic from the entire network, i.e., connectivity
is not structured spatially.
"""
from copy import deepcopy
from math import sqrt
import numpy as np
# from mpl_toolkits.mplot3d import Axes3D
# import matplotlib.pyplot as plt
import nest
import nest.topology as tp
class Brunel3D:
def __init__(self):
self.layer_dict = {}
nest.SetKernelStatus({"local_num_threads": 1})
nest.Install("streamingmodule")
# nest.SetKernelStatus({'print_time': True})
class Parameters:
"""
Define model parameters.
"""
order = 9 # must be square
NE = 4 * order # number of excitatory neurons.
NI = 1 * order # number of inhibitory neurons
lengthE = int(sqrt(NE))
lengthI = int(sqrt(NI))
if not (lengthE**2 == NE and lengthI**2 == NI):
raise ValueError('Order must be a square number (order = n**2)')
# neuron_model = 'iaf_psc_alpha'
neuron_model = 'mat2_psc_exp'
g = 5.0 # ratio inhibitory weight/excitatory weight
eta = 2.0 # external rate relative to threshold rate
epsilon = 0.1 # connection probability
delay = 1.5 # synaptic delay in ms
tauMem = 20.0 # time constant of membrane potential in ms
theta = 20.0 # membrane threshold potential in mV
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
CE = int(epsilon * NE) # number of excitatory synapses per neuron
nu_th = theta / (J * CE * tauMem)
nu_ex = eta * nu_th
p_rate = 1.0 * nu_ex * CE
neuron_params = {"C_m": 1.0,
"tau_m": tauMem,
"t_ref": 2.0,
"E_L": 0.0,
# "V_reset": 0.0, # doesn't work with mat2_psc_exp
# "V_th": theta, # doesn't work with mat2_psc_exp
"V_m": 0.0
}
mm_params = {'record_from': ['V_m', 'V_th'],
'record_to': ['streaming']}
poisson_params = {"rate": p_rate}
def modified_copy(self, orig, diff):
"""
Returns a deep copy of dictionary with changes applied.
@param orig original dictionary, will be deep-copied
@param diff copy will be updated with this dict
"""
tmp = deepcopy(orig)
tmp.update(diff)
return tmp
def make_layer_specs(self):
"""
Returns lists of dictionaries with model, layer, and synapse model
specifications.
"""
P = self.Parameters
self.models = [(P.neuron_model, 'excitatory', P.neuron_params),
(P.neuron_model, 'inhibitory', P.neuron_params),
('poisson_generator', 'noise', P.poisson_params),
('multimeter', 'recordingNode', P.mm_params)]
self.syn_models = [('static_synapse', 'static_excitatory', {})]
dE = 1.0 / float(P.lengthE)
limE = (1.0 - dE) / 2.0
posE = np.linspace(-limE, limE, num=P.lengthE)
dI = 1.0 / float(P.lengthI)
limI = (1.0 - dI) / 2.0
posI = np.linspace(-limI, limI, num=P.lengthI)
self.layers = [('Excitatory', {'positions': [[x, y, 0.4]
for x in posE
for y in posE],
'edge_wrap': True,
'elements': 'excitatory'}),
('Inhibitory', {'positions': [[x, y, -0.4]
for x in posI
for y in posI],
'edge_wrap': True,
'elements': 'inhibitory'})]
self.layers.append(('PoissonGenerator',
{'positions': [[0.0, 0.0, 0.0]],
'elements': 'noise'}))
self.layers.append(('Multimeter',
{'positions': [[0.0, 0.0, 0.0]],
'elements': 'recordingNode'}))
self.layers.append(('SpikeDetector',
{'positions': [[0.0, 0.0, 0.0]],
'elements': 'spike_detector'}))
def make_connection_specs(self):
"""
Returns list of dictionaries specifying projections for Brunel network.
"""
P = self.Parameters
self.projections = [('Excitatory', 'Excitatory',
{'connection_type': 'convergent',
'synapse_model': 'static_synapse',
'kernel': P.epsilon,
'weights': P.J_ex,
'delays': P.delay}),
('Excitatory', 'Inhibitory',
{'connection_type': 'convergent',
'synapse_model': 'static_synapse',
'kernel': P.epsilon,
'weights': P.J_ex,
'delays': P.delay}),
('Inhibitory', 'Excitatory',
{'connection_type': 'convergent',
'synapse_model': 'static_synapse',
'kernel': P.epsilon,
'weights': P.J_in,
'delays': P.delay}),
('Inhibitory', 'Excitatory',
{'connection_type': 'convergent',
'synapse_model': 'static_synapse',
'kernel': P.epsilon,
'weights': P.J_in,
'delays': P.delay}),
('Multimeter', 'Excitatory',
{'connection_type': 'convergent'}),
('Excitatory', 'SpikeDetector',
{'connection_type': 'convergent'}),
('Inhibitory', 'SpikeDetector',
{'connection_type': 'convergent'}),
('PoissonGenerator', 'Excitatory',
{'connection_type': 'convergent'})]
def make_layers(self):
# First, we copy the models with custom specifications
# Neuron models
for model in self.models:
old_name, new_name, spec = model
nest.CopyModel(old_name, new_name, spec)
# Synapse models
for model in self.syn_models:
old_name, new_name, spec = model
nest.CopyModel(old_name, new_name, spec)
# Next we make the layers
for l in self.layers:
name = l[0]
specs = l[1]
self.layer_dict.update({name: tp.CreateLayer(specs)})
def make_connections(self):
# Connect layers
for proj in self.projections:
pre_layer = self.layer_dict[proj[0]]
post_layer = self.layer_dict[proj[1]]
conn_specs = proj[2]
tp.ConnectLayers(pre_layer, post_layer, conn_specs)
def simulate(self):
nest.Simulate(1000)
# def plot_positions(self):
# ex_pos = self.layers[0][1]['positions']
# in_pos = self.layers[1][1]['positions']
# fig = plt.figure()
# ax = Axes3D(fig)
# for c, m, positions in [('b', 'o', ex_pos), ('r', '^', in_pos)]:
# ax.scatter([x for x, y, z in positions],
# [y for x, y, z in positions],
# [z for x, y, z in positions],
# c=c, marker=m)
# def get_results(self):
# mm = (self.layer_dict['Multimeter'][0] + 1,)
# sd = (self.layer_dict['SpikeDetector'][0] + 1,)
# mm_status = nest.GetStatus(mm)[0]
# sd_status = nest.GetStatus(sd)[0]
# nest.raster_plot.from_device(sd, hist=True)
# senders = mm_status['events']['senders']
# times = mm_status['events']['times']
# v_m = mm_status['events']['V_m']
# v_th = mm_status['events']['V_th']
# step = int(max(senders)/100 + 1) # Only plot results from some GIDs
# mm_events = []
# for i in range(1, max(senders) + 1, step):
# if i in senders:
# indices = np.argwhere(senders == i)
# mm_events.append({'GID': i,
# 'times': [times[n] for n in indices],
# 'V_m': [v_m[n] for n in indices],
# 'V_th': [v_th[n] for n in indices]})
# return {'multimeter': mm_events,
# 'spike_detector': nest.GetStatus(sd)[0]}
# if __name__ == '__main__':
# nest.ResetKernel()
# print('Making specifications')
# brunel = Brunel3D()
# brunel.make_layer_specs()
# brunel.make_connection_specs()
# print('Making layers')
# brunel.make_layers()
# nest.topology.DumpLayerNodes([l[0] for l in brunel.layer_dict.values()][:2],
# 'brunel_nodes.txt')
# print('Making connections')
# brunel.make_connections()
# brunel.simulate()
# print('Getting results')
# brunel.plot_positions()
# results = brunel.get_results()
# for value in ['V_m', 'V_th']:
# plt.figure()
# for n in results['multimeter'][::20]:
# plt.plot(n['times'], n[value], label='{}'.format(n['GID']))
# plt.legend()
# plt.title(value)
# plt.show()