""" 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