Computational Neuroscience

Computational neuroscience: neuron and network simulation


The neuron is the most common component in our brain,  we have nearly 86 billion neurons and 1000 trillion synaptic connections, estimates to a computer with a 1 trillion bit per seconds processor

Computational Neuroscience

Is the field of study in which we measure and simulate the neurons process. Our brain is a complex machine and its behavior is non-linear.

We need previous knowledge of electronics, ODE’s, neurobiology, chemistry, and programming.

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Lane Line Recognition

Lane line recognition algorithms

The Hough Transform has been broadly used in many fields, including autonomous cars. It is used to detect straight and curved lines with high accuracy.


This is the result of the Lane Line program I have designed to detect edges on the road.

The algorithm used, it is used to detect edges on images. Then, we discard using analytics and probability the ones which overlap each other and the less probable.

It is very efficient and can be used in parallel computing for rather good optimizations. The results are quite astonishing.

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Dynamic Systems

N-Order Linear Systems

I have done a project for my math class where we analyze the linear systems. We have classified the first and second-order linear systems and give a general formula for an n-linear system. We have used algebra notation and we have encountered with the companion matrix power to the n problem.

The n-linear systems are used widely in finance for loans, interests, etc.

PDF download: N-LinearDynamicSystem

GitHub repository: DynamicSystem


Connect 4

Recurrent algorithms

I have been working on recurrent algorithms. They’re a bunch of examples in which you can implement these algorithms. However, we usually see that they are efficient.

4 in a row is a simplistic game that can be easily programmed to win every time. I have made in Python an AI that solves and wins. It is not programmed in C++ because I wanted a GUI.

This is an example of a game I have played against the AI. It performs really well  🙂

Computational Fluid Dynamics (CFD)

Navier-Stokes + Incompressible + Finite difference method

Discretization methods for approximating the Partial Differential Equations (PDEs):

  1. Finite Difference (FD) \checkmark
  2. Finite Elements (FE)
  3. Finite Volume (FV)

Finite Difference (FD)

Taylor’s polynomial

    \[f(x_0 + h) = f(x_0) + \frac{f'(x_0)}{1!}h + \frac{f^{(2)}(x_0)}{2!}h^2 + \cdots + \frac{f^{(n)}(x_0)}{n!}h^n + R_n(x) \]

    \[f'(a)\approx {f(a+h)-f(a)\over h}. \]

    \[f'(a)\approx{f(a+h)+f(a-h)-2f(h)\over \Delta h^2 \]

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