"""
Synfirechain-like example
"""
#!/usr/bin/python
import pacman103.front.pynn as p
import visualiser.visualiser_modes as modes
import pylab
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
nNeurons = 200 # number of neurons in each population
p.set_number_of_neurons_per_core("IF_curr_exp", nNeurons / 2)
p.set_number_of_neurons_per_core("DelayExtension", nNeurons / 2)
cell_params_lif = {
'cm': 0.25, # nF
'i_offset': 0.0,
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 5.0,
'tau_syn_I': 5.0,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0
}
populations = list()
projections = list()
weight_to_spike = 2.0
delay = 17
"""
Synfirechain-like example
"""
#!/usr/bin/python
import pacman103.front.pynn as p
import pylab
p.setup(timestep=1.0, min_delay = 1.0, max_delay = 32.0)
p.set_number_of_neurons_per_core("IZK_curr_exp", 100)
nNeurons = 200 # number of neurons in each population
cell_params_izk = {
'a': 0.02,
'b': 0.2,
'c': -65,
'd': 8,
'v_init': -75,
'u_init': 0,
'tau_syn_E': 2,
'tau_syn_I': 2,
'i_offset': 0
}
populations = list()
projections = list()
weight_to_spike = 40
delay = 1
loopConnections = list()
"""
Synfirechain-like example
"""
#!/usr/bin/python
import pacman103.front.pynn as p
import pylab
p.setup(timestep=1.0, min_delay=1.0, max_delay=32.0)
p.set_number_of_neurons_per_core("IZK_curr_exp", 100)
nNeurons = 200 # number of neurons in each population
cell_params_izk = {
'a': 0.02,
'b': 0.2,
'c': -65,
'd': 8,
'v_init': -75,
'u_init': 0,
'tau_syn_E': 2,
'tau_syn_I': 2,
'i_offset': 0
}
cell_params_cond = {
'cm': 0.25, # nF
'i_offset': 0.0,
'tau_m': 10.0,
'tau_refrac': 2.0,
'tau_syn_E': 2.5,
'tau_syn_I': 2.5,
"""
Synfirechain-like example
"""
#!/usr/bin/python
import pacman103.front.pynn as p
import visualiser.visualiser_modes as modes
import numpy, pylab
p.setup(timestep=1.0, min_delay = 1.0, max_delay = 144.0)
nNeurons = 200 # number of neurons in each population
max_delay = 50
p.set_number_of_neurons_per_core("IF_curr_exp", nNeurons / 2)
p.set_number_of_neurons_per_core("DelayExtension", nNeurons / 2)
cell_params_lif = {'cm' : 0.25, # nF
'i_offset' : 0.0,
'tau_m' : 20.0,
'tau_refrac': 2.0,
'tau_syn_E' : 5.0,
'tau_syn_I' : 5.0,
'v_reset' : -70.0,
'v_rest' : -65.0,
'v_thresh' : -50.0
}
populations = list()
projections = list()
weight_to_spike = 2.0
delay = numpy.random.RandomState()
delays = list()
FWD = 0
BWD = 1
input_size = 128 # Size of each population
subsample_size = 32
runtime = 60000 * 2
#runtime = 60
n_orientations = 4
size_gabor = 7
# Simulation Setup
p.setup(timestep=1.0, min_delay=1.0, max_delay=11.0
) # Will add some extra parameters for the spinnPredef.ini in here
p.set_number_of_neurons_per_core('IF_curr_exp',
128) # this will set one population per core
cell_params = {
'tau_m': 64,
'i_offset': 0,
'v_rest': -75,
'v_reset': -95,
'v_thresh': -40,
'tau_syn_E': 15,
'tau_syn_I': 15,
'tau_refrac': 2
}
cell_params_subsample = {
'tau_m': 32,
'i_offset': 0,
__author__ = 'stokesa6'
"""
retina example that just feeds data from retina to vis
"""
#!/usr/bin/python
import pacman103.front.pynn as p
import visualiser.visualiser_modes as modes
import numpy, pylab
import pacman48_examples.vampire_bot.retina_lib as retina_lib
#set up pacman103
p.setup(timestep=1.0, min_delay = 1.0, max_delay = 32.0)
p.set_number_of_neurons_per_core('IF_curr_exp', 128) # this will set one population per core
cell_params_lif = {'cm' : 0.25, # nF
'i_offset' : 0.0,
'tau_m' : 10.0,
'tau_refrac': 2.0,
'tau_syn_E' : 0.5,
'tau_syn_I' : 0.5,
'v_reset' : -65.0,
'v_rest' : -65.0,
'v_thresh' : -64.4
}
#external stuff population requiremenets
connected_chip_coords = {'x': 0, 'y': 0}
virtual_chip_coords = {'x': 0, 'y': 5}
link = 4
