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 numpy as np import pylab as pl from sys import argv, path path.append('../') from NetPop import NetPop, NetPopPredict from functions import simpleaxis, smooth_spikes, accumulate, init_fig savefig = False if len(argv) == 1 else True init_fig() pl.rc('legend', **{'fontsize': 30}) # colors for colorblind from http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/ colRT = ["#009E73", "#0072B2", "#D55E00", "#E69F00", "#56B4E9", "#F0E442", "#CC79A7", "#999999"] # orange to green to cyan: col = map(tuple, [np.array([.902, .624, 0]) + (np.array([0, .62, .451]) - 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 / 3 for i in range(1, 4)]) def runpopU(W, uinit, step, pop_size, rate=100, T=700, tm=50, ts=2, reset=3, delay=0): K = len(W) spikes = np.zeros((T / step, K)) spikes[0, K - pop_size:] = 1 fs = np.exp(-step / ts) # decay factor synapse fm = np.exp(-step / tm) # decay factor membrane # combine factor to save computing time