NMA Computational Neuroscience course
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Updated
Aug 15, 2024 - Jupyter Notebook
NMA Computational Neuroscience course
Kalman Filter implementation in Python using Numpy only in 30 lines.
Robust control for dynamic nonlinear systems with parameter uncertainties
Modelling crowd behaviour in panic sitations
Notes on topics ranging from Recurrent Newtorks to Automatic Control and Reinforcement Learning
Pytorch implementation of Stable Vector Fields on Lie Groups through Diffeomorphism
ODESCA is a MATLAB tool for the creation and analysis of dynamic systems described by ordinary differential equations
An Animation-Interpolator, "reverse-engineered" from facebook/rebound
Realtime 3-dimensional phase portraits in phase space
Here, I include my thoughts about how does the brain of the worm give rise to remarkable behavioral plasticities
Granger Causality with Signal-dependent Noise
Real time parameter estimation on Grey Box Dynamic Systems
Computational model of the electromotor command network of pulse-type mormyrids.
C++ object-oriented system to estimate the attractor dimension and the model order of a time series.
Implements the algorithm introduced in our paper: Temporal Logic Explanations for Dynamic Decision Systems using Anchors and Monte Carlo Tree Search
Critério de Jury para verificar estabilidade em python. Jury stability criterion implemented in python.
2-dimensional random dynamic systems visualized using WebGL
Segmentação Semi-Supervisionada de Imagens através de Dinâmicas Coletivas em Redes Complexas
Introduction to analyze piecewise-smooth systems using python
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