leon-nn’s gists

leon-nn / openGLRender.py

Created January 26, 2018 20:30

This is a trivial example to demonstrate how to use modern OpenGL in Python via PyOpenGL to render offscreen to a texture buffer in a framebuffer object

	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

leon-nn / mayaviRender.py

Created January 24, 2018 11:43

This demonstrates how to use Mayavi/mlab to render a mesh object in a precise pixel location within a viewport of predetermined size.

	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 / nonhomogenousPoisson.m

Last active May 13, 2017 16:25

Nonhomogenous Poisson spiking train in a spiking neural network framework

	%% 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 / ornsteinUhlenbeck.m

Last active November 26, 2017 08:54

Ornstein-Uhlenbeck Process

	%% 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

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