filename = 'trash' if (len(sys.argv)<2) else sys.argv[1] N = 20 if (len(sys.argv)<3) else int(sys.argv[2]) nframes = 1000 if (len(sys.argv)<4) else int(sys.argv[3]) zeta = 2 L = 0.8 a = np.zeros(N*N) sigma = 0.001 gamma = 1.0 eta = 10.0 dt = 0.01 order = 2 alpha = 0.1 phi = 10 e = GaussianEnv(zeta,gamma,eta,L,N,-2.0,-2.0,4.0,4.0,sigma,order) neuron = PoissonNeuron(np.random.normal(0.0,1.0,N*N),alpha*np.identity(N*N),phi,N) lifneuron = IFNeuron(np.zeros(N),dt,1) code = PoissonPlasticCode() video = VideoSink((N,N),filename,rate = 25,byteorder = "Y8") print "Now sampling and plotting...\n" delete_them = [] gr = lambda x : (grayscale(x,-6.0,6.0)) vgr = np.vectorize(gr) for i in range(0,nframes): plot = e.samplestep(dt)
from gaussianenv import GaussianEnv emat = GaussianEnv(1.0,1.0,1.0,1.0,1,0.0,0.0,1.0,1.0,0.00001,2) eou = GaussianEnv(1.0,1.0,1.0,1.0,1,0.0,0.0,1.0,1.0,0.00001,1) ts = arange(0.0,4.0,0.01) smat1 = [] sou1 = [] for i in ts: smat1.append(emat.samplestep(0.01)) sou1.append(eou.samplestep(0.01)) smat1 = array(smat1).ravel() sou1 = array(sou1).ravel() emat.reset() eou.reset() smat2 = [] sou2 = [] for i in ts: smat2.append(emat.samplestep(0.01)) sou2.append(eou.samplestep(0.01)) smat2 = array(smat2).ravel() sou2 = array(sou2).ravel() emat.reset()
