leon-nn

%% Ornstein-Uhlenbeck Process
% ...from the paper "FLUCTUATING SYNAPTIC CONDUCTANCES RECREATE IN 
%   VIVO-LIKE ACTIVITY IN NEOCORTICAL NEURONS"
% The stochastic differential equation is given by:
%   dx/dt = 1/tau * (mu - x) + sqrt(D) * chi(t)
% where:
%   x is the random variable
%   D is the amplitude of the stochastic component
%   chi(t) is a normally-distributed (zero-mean) noise source
%   tau is the time constant (tau = 0 gives white noise, tau > 0 gives

leon-nn

%% Non-homogenous Poisson spike train in a spiking neural network framework
% We generate inhomogenous Poisson spike trains for a set of output neurons
% in a spiking neural network. We use the "thinning" approach introduced in
% "Simulation of nonhomogeneous poisson processes by thinning" by Lewis and
% Shedler (1979).

% Number of output neurons
numOut = 10;

% Define a supremum for lambda(t). I.e., this should be the smallest upper

leon-nn

import os
os.environ["QT_API"] = "pyqt"
import numpy as np
import matplotlib.pyplot as plt
from mayavi import mlab

if __name__ == "__main__":
    
    # Create a test 100x100 RGB image: all black and a red border. This is to demonstrate how to align the viewport.
    img = np.zeros((100, 100, 3), dtype = np.uint8)

leon-nn

from OpenGL.GL import *
from OpenGL.GLUT import *
import numpy as np
import matplotlib.pyplot as plt

# Global variables
shaderProgram = None
vertexBufferObject = None
indexBufferObject = None
framebufferObject = None