def __init__(self,
input_neurons_theta_size,
input_neurons_theta_dot_size,
liquid_neurons_size,
readout_neurons_tau1_size,
readout_neurons_tau2_size,
output_layer_weight=100.0,
thread_num=-1):
# Don't make any nest nodes or connections before this line!
nest.SetKernelStatus({
"local_num_threads":
thread_num if thread_num > 0 else multiprocessing.cpu_count()
})
nest.set_verbosity("M_ERROR") # suppress trivial messages
self.total_sim_time = 0.0
self.tau1 = np.zeros(readout_neurons_tau1_size)
self.tau2 = np.zeros(readout_neurons_tau2_size)
self.output_layer_weight = output_layer_weight
self.lsm = lsm.Lsm(input_neurons_theta_size,
input_neurons_theta_dot_size,
liquid_neurons_size,
readout_neurons_tau1_size,
readout_neurons_tau2_size,
output_layer_weight=self.output_layer_weight)
setuptype = '../setupfiles/mc_final_mn256r1.xml'
setupfile = '../setupfiles/final_mn256r1_retina_monster.xml'
nsetup = pyNCS.NeuroSetup(setuptype, setupfile, prefix=prefix)
nsetup.mapper._init_fpga_mapper()
chip = nsetup.chips['mn256r1']
chip.configurator._set_multiplexer(0)
#populate neurons
rcnpop = pyNCS.Population('neurons', 'for fun')
rcnpop.populate_by_id(nsetup, 'mn256r1', 'excitatory', neuron_ids)
#init liquid state machine
print "################# init onchip recurrent connections... [reservoir] "
liquid = L.Lsm(rcnpop, cee=0.7, cii=0.3)
#c = 0.2
#dim = np.round(np.sqrt(len(liquid.rcn.synapses['virtual_exc'].addr)*c))
use_retina_projections = (liquid.matrix_programmable_rec != 1)
liquid.matrix_programmable_exc_inh[
use_retina_projections] = np.random.choice(
[0, 1], size=np.sum(use_retina_projections))
liquid.matrix_programmable_w[use_retina_projections] = np.random.choice(
[0, 1, 2, 3], size=np.sum(use_retina_projections))
liquid.program_config()
###### configure retina
inputpop = pyNCS.Population('', '')
inputpop.populate_by_id(nsetup, 'mn256r1', 'excitatory',
# [email protected] # Juan Pablo Carbajal # [email protected] # # Liquid State Machine class mn256r1 # =============================== ### ========================= import packages =============================== import random import numpy as np import time import pyNCS import sys import lsm as L liquid = L.Lsm() #init liquid state machine prob_n_itter = 0.8 t = np.linspace(0, 1, 1e3)[:, None] M = np.random.randn(1, 256) x = t.dot(M) bias = 0.05 * np.random.randn(1, 256) expo = [([1] * 11 + range(5)) * 16] zeros = np.where(np.random.rand(256, 1) > prob_n_itter)[0] def sys(x, bias, expo, zeros): y = x**expo + bias y[:, 1:10] = np.sin(2 * np.pi * 3 * y[:, 1:10]) for i in zeros: y[:, i] = 0 return y
setuptype = '../setupfiles/mc_final_mn256r1.xml'
setupfile = '../setupfiles/final_mn256r1_retina_monster.xml'
nsetup = pyNCS.NeuroSetup(setuptype, setupfile, prefix=prefix)
nsetup.mapper._init_fpga_mapper()
chip = nsetup.chips['mn256r1']
chip.configurator._set_multiplexer(0)
#populate neurons
rcnpop = pyNCS.Population('neurons', 'for fun')
rcnpop.populate_by_id(nsetup, 'mn256r1', 'excitatory', neuron_ids)
#init liquid state machine
print "################# init onchip recurrent connections... [reservoir] "
liquid = L.Lsm(rcnpop, cee=0.55, cii=0.25)
#c = 0.2
#dim = np.round(np.sqrt(len(liquid.rcn.synapses['virtual_exc'].addr)*c))
if (use_retina):
use_retina_projections = (liquid.matrix_programmable_rec != 1)
liquid.matrix_programmable_exc_inh[
use_retina_projections] = np.random.choice(
[0, 1], size=np.sum(use_retina_projections))
liquid.matrix_programmable_w[
use_retina_projections] = np.random.choice(
[0, 1, 2, 3], size=np.sum(use_retina_projections))
liquid.program_config()
###### configure retina
inputpop = pyNCS.Population('', '')
setuptype = '../setupfiles/mc_final_mn256r1.xml'
setupfile = '../setupfiles/final_mn256r1_retina_monster.xml'
nsetup = pyNCS.NeuroSetup(setuptype, setupfile, prefix=prefix)
nsetup.mapper._init_fpga_mapper()
chip = nsetup.chips['mn256r1']
chip.configurator._set_multiplexer(0)
#populate neurons
rcnpop = pyNCS.Population('neurons', 'for fun')
rcnpop.populate_by_id(nsetup, 'mn256r1', 'excitatory', neuron_ids)
#init liquid state machine
print "################# init onchip recurrent connections... [reservoir] "
liquid = L.Lsm(rcnpop, cee=0.8, cii=0.2)
#c = 0.2
#dim = np.round(np.sqrt(len(liquid.rcn.synapses['virtual_exc'].addr)*c))
use_retina_projections = (liquid.matrix_programmable_rec != 1)
liquid.matrix_programmable_exc_inh[
use_retina_projections] = np.random.choice(
[0, 1], size=np.sum(use_retina_projections))
liquid.matrix_programmable_w[use_retina_projections] = np.random.choice(
[0, 1, 2, 3], size=np.sum(use_retina_projections))
liquid.program_config()
###### configure retina
inputpop = pyNCS.Population('', '')
inputpop.populate_by_id(nsetup, 'mn256r1', 'excitatory',
do_figs_encoding = False
#populations divisible by 2 for encoders
neuron_ids = np.linspace(0, 255, 256)
npops = len(neuron_ids)
#setup
prefix = '../'
setuptype = '../setupfiles/mc_final_mn256r1.xml'
setupfile = '../setupfiles/final_mn256r1_retina_monster.xml'
nsetup = pyNCS.NeuroSetup(setuptype, setupfile, prefix=prefix)
chip = nsetup.chips['mn256r1']
nsetup.mapper._init_fpga_mapper()
chip.configurator._set_multiplexer(0)
#populate neurons
rcnpop = pyNCS.Population('neurons', 'for fun')
rcnpop.populate_by_id(nsetup, 'mn256r1', 'excitatory', neuron_ids)
#init nef on neuromorphic chips
import lsm
liquid = lsm.Lsm(rcnpop) #init liquid state machine
ntrials = 150 #same projection stimulus, but regenerated poisson trains
inputs, outputs = liquid.stimulate_reservoir(trials=ntrials)
for trial in range(ntrials):
np.savetxt("lsm/inputs_correlated_trial_" + str(trial) + ".txt",
inputs[trial])
np.savetxt("lsm/outputs_correlated_trial_" + str(trial) + ".txt",
outputs[trial])