def simulate(seed, na=2, rate=100, predict=False):
net = NetPopPredict() if predict else NetPop(na)
W = np.copy(net.W)
W[:-1, :-1] *= gamma
W -= (1 - gamma) * net.competition
u0 = np.linspace(-2, .04, len(W))
np.random.seed(seed)
s, u = runpopU(W * 1. / popsize, u0, step, popsize, rate)
pre = np.zeros((int(250 / step), len(W)))
for t in [-40]:
pre[int(t / step), range(-2, -2 - na, -1)] = 1
for t in [-240, -200, -160, -120, -80, -10, -2, -1]:
pre[int(t / step), range(-2 - na, -2 - 2 * na, -1)] = .1
pres = np.vstack((pre, s))
tmp = np.array([
np.mean(np.array([
smooth_spikes(pres[:, j * popsize + i], 40, .2)
for i in range(popsize)
]),
axis=0) for j in range(len(W) - na,
len(W) - 8 * na - 1, -na)
])[:, 250 / step:750 / step]
tmp2 = np.array([
np.mean(np.array([
smooth_spikes(pres[:, j * popsize + i], 40, .2)
for i in range(popsize)
]),
axis=0)
for j in range(len(W) - na - 1,
len(W) - 8 * na - 2, -na)
])[:, 250 / step:750 / step]
return [np.array(map(np.argmax, tmp)) * step, tmp, tmp2, s]
def sim(u0, rate, reset, tm, th, rb, r0, r1, r2, rp, tmean, tsigma, ir, runs):
step = .2
net = NetPop(1)
np.random.seed(0)
tmp = u0 * np.ones(len(net.W))
tmp[-1] = .5
s = np.array([
run(net.W,
u0 * np.ones(len(net.W)),
step,
1,
rate=rate,
T=1700,
tm=tm,
reset=reset,
seed=i,
th=th,
rbase=rb,
r0=r0,
r1=r1,
rp=rp,
tmean=tmean,
tsigma=tsigma) for i in range(runs)
])
s3 = np.array([
run(net.W,
u0 * np.ones(len(net.W)),
step,
1,
rate=rate,
T=1700,
tm=tm,
reset=reset,
seed=i,
th=th,
rbase=rb,
r0=r0,
r1=r2,
rp=rp,
tmean=tmean,
tsigma=tsigma) for i in range(runs)
])
pre = np.ones((int(250 / step), len(net.W))) * step * ir * 100 / 1000.
pre[:, -1] = 0
return np.array([
smooth_spikes(np.vstack((pre, np.mean(s3, axis=0)))[:, 1], 40, step),
smooth_spikes(np.vstack((pre, np.mean(s3, axis=0)))[:, 0], 40, step),
smooth_spikes(np.vstack((pre, np.mean(s, axis=0)))[:, 1], 40, step),
smooth_spikes(np.vstack((pre, np.mean(s, axis=0)))[:, 0], 40, step),
smooth_spikes(np.vstack((pre, np.mean(s, axis=0)))[:, 2], 40, step)
])
import matplotlib # these lines to
matplotlib.use('Agg') # work remotely
import numpy as np
import pylab as pl
from sys import path
path.append('../')
from scipy.sparse import csr_matrix
from NetPop import NetPop
import cfunctions as cfn
from functions import simpleaxis, errorfill, init_fig
init_fig()
net = NetPop(1)
W = np.copy(net.W)
dt = 1
R0 = net.R4flatPi(net.pstart_state)
Rmax = np.dot(net.calc_Qvalue().max(axis=1), net.pstart_state)
ref = 20
step = .2
rate = 400
## offline ##
try:
perf = np.load('results/performance.npy')
except IOError:
try:
S = np.load('results/spikes.npz')['S']
except IOError:
import matplotlib # these lines to
matplotlib.use('Agg') # work remotely
import numpy as np
import pylab as pl
from sys import path
path.append('../')
from scipy.sparse import csr_matrix
from NetPop import NetPop
import cfunctions as cfn
from functions import simpleaxis, errorfill, init_fig
init_fig()
gamma = .98
net = NetPop(1)
W = np.copy(net.W)
W[:-1, :-1] *= gamma
W -= (1 - gamma) * net.competition
R0 = net.R4flatPi(0, gamma)
Rmax = net.calc_Qvalue(gamma).max(axis=1)[0]
ref = 20
step = .2
rate = 400
## offline ##
try:
perf = np.load('results/performance.npy')
except IOError:
try:
np.array([.902, .624, 0])) * i / 4 for i in range(5)]) +\
map(tuple, [np.array([0, .62, .451]) +
(np.array([.337, .706, .914]) -
np.array([0, .62, .451])) * i / 4 for i in range(1, 5)])
u0 = .2
thresh = 7
rate = 200
reset = 0.0
tm = 25.0
tdec = 300
trise = 110
tdly = 60
runs = 100
step = .2
net = NetPop(1)
##### function definitions #####
def sim(u0,
rate,
reset,
tm,
offers,
ratio,
tdec,
trise,
tdly,
runs,
rb=0,
pl.rc('legend', **{'fontsize': 36})
pl.rc('axes', linewidth=2)
pl.rc('xtick.major', size=10, width=1.5)
pl.rc('ytick.major', size=10, width=1.5)
# colors for colorblind from http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
col = ['#56B4E9', '#E69F00', '#CC79A7', '#009E73']
####################
#### parameters ####
####################
gamma = .98
sigma = .5
net = NetPop(1, 3)
W = np.copy(net.W)
W[:-1, :-1] *= gamma**3
W -= (1 - gamma**3) * net.competition
valley = [8, 16]
##############################
#### function definitions ####
##############################
def get_path(x, initpv=valley, maxtrials=300):
pv = [initpv]
count = 0
while count < maxtrials:
g0 = accumulate(x)
th = thresh * gamma ** c
for t in range(3500):
g = g0.next()
if abs(g) >= th:
break
return t, g
popsize = 1
gamma = .7
step = .2
tau_r = 80.
rate = 100
### simulation ###
net = NetPop()
W = np.copy(net.W)
W[:-1, :-1] *= gamma
W -= (1 - gamma) * net.competition
if deval:
W[2, -1] /= 2.
s = []
for seed in range(30):
u0 = np.zeros(len(W))
np.random.seed(seed)
s += [runU(W, u0, step, rate)]
s = np.array(s)
pre = np.zeros((int(250 / step), len(W)))
pres = np.array([np.vstack((pre, ss)) for ss in s])
Ratels = [np.array([np.mean(np.array([smooth_spikes(p[:, j * popsize + i], 40, .2)
for i in range(popsize)]), axis=0)
import matplotlib # these lines to
matplotlib.use('Agg') # work remotely
import numpy as np
import pylab as pl
from sys import path
path.append('../')
from scipy.sparse import csr_matrix
from NetPop import NetPop
import cfunctions as cfn
from functions import simpleaxis, errorfill, init_fig
init_fig()
gamma = .98
net = NetPop(1)
W = np.copy(net.W)
W[:-1, :-1] *= gamma
W -= (1 - gamma) * net.competition
R0 = net.R4flatPi(0, gamma)
Rmax = net.calc_Qvalue(gamma).max(axis=1)[0]
ref = 20
step = .2
rate = 400
## offline ##
try:
perf = np.load('results/performance.npy')
except IOError:
matplotlib.use("Agg") # work remotely
import numpy as np
import pylab as pl
from sys import path
path.append("../")
from scipy.sparse import csr_matrix
from NetPop import NetPop
import cfunctions as cfn
from functions import simpleaxis, errorfill, init_fig
init_fig()
net = NetPop(1)
W = np.copy(net.W)
dt = 1
R0 = net.R4flatPi(net.pstart_state)
Rmax = np.dot(net.calc_Qvalue().max(axis=1), net.pstart_state)
ref = 20
step = 0.2
rate = 400
## offline ##
try:
perf = np.load("results/performance.npy")
except IOError:
try:
import cfunctions as cfn
from functions import simpleaxis, errorfill
pl.rc("figure", facecolor="white", dpi=90, frameon=False)
pl.rc("font", size=44, **{"family": "sans-serif", "sans-serif": ["Computer Modern"]})
pl.rc("lines", lw=4)
pl.rc("text", usetex=True)
pl.rc("legend", **{"fontsize": 36})
pl.rc("axes", linewidth=2)
pl.rc("xtick.major", size=10, width=1.5)
pl.rc("ytick.major", size=10, width=1.5)
gamma = 0.98
net = NetPop(1)
W = np.copy(net.W)
W[:-1, :-1] *= gamma
W -= (1 - gamma) * net.competition
R0 = net.R4flatPi(0, gamma)
Rmax = net.calc_Qvalue(gamma).max(axis=1)[0]
ref = 20
step = 0.2
rate = 400
## offline ##
try:
perf = np.load("results/performance.npy")
except IOError:
from NetPop import NetPop
import integrate2d as cf
import cfunctions as cfn
from functions import simpleaxis, errorfill, init_fig
init_fig()
# colors for colorblind from http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
col = ['#56B4E9', '#E69F00', '#CC79A7', '#009E73']
####################
#### parameters ####
####################
gamma = .98
sigma = .5
net = NetPop(1, 3)
W = np.copy(net.W)
W[:-1, :-1] *= gamma**3
W -= (1 - gamma**3) * net.competition
valley = [8, 16]
##############################
#### function definitions ####
##############################
def get_path(x, initpv=valley, maxtrials=300):
pv = [initpv]
count = 0
while count < maxtrials:
