danielmk

"""Fitzhugh-Nagumo dynamical system. With animation code.

Author: Daniel Müller-Komorowska @scidanm
"""

import numpy as np
from scipy.integrate import solve_ivp
from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt
import matplotlib as mpl

danielmk

import pandas as pd
import numpy as np
from bokeh.plotting import figure, show, output_notebook
import ipywidgets as widgets
from IPython.display import display, clear_output
output_notebook()
 
"""Load Iris dataset and transform the pandas DataFrame"""
data = pd.read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/hospitalizations/covid-hospitalizations.csv")
data['date'] = pd.to_datetime(data['date'])

danielmk

import numpy as np
from scipy.integrate import odeint, solve_ivp
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
 
def lorenz(t, state, sigma, beta, rho):
    x, y, z = state
     
    dx = sigma * (y - x)
    dy = x * (rho - z) - y

danielmk

"""
NumPy implementation of a balanced spiking neural network. Inspired by MATLAB 
code from Nicola & Clopath (2017): https://doi.org/10.1038/s41467-017-01827-3
"""

import numpy as np
from numpy.random import rand, randn
import matplotlib.pyplot as plt

danielmk

import torch
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

np.random.seed(10000)

danielmk

"""
The functions in this code are taken from a Neuromatch Academy Tutorial about dimensionality
reduction by Alex Cayco Gajic & John Murray:
https://github.com/NeuromatchAcademy/course-content/tree/master/tutorials/W1D5_DimensionalityReduction

Neuromatch Academy content is licensed under a Creative Commons Attribution 4.0 International
https://github.com/NeuromatchAcademy/course-content/blob/master/LICENSE.md

I use these functions to create an animation showing a subset of MNIST samples
reconstructed from an increasing number of principal components.

danielmk

# -*- coding: utf-8 -*-
"""
@author: Daniel Müller-Komorowska
"""

import numpy as np
import scipy.signal as signal

def amp_crosscorr(eeg1, eeg2, samp_freq, low_freq, high_freq):
    """

danielmk

import numpy as np
from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt

x = np.arange(0, 10*np.pi, 0.01)
y_sin = np.sin(x)
y_cos = np.cos(x)

fig = plt.figure()
ax1 = plt.subplot(2, 1, 1)

danielmk

import numpy as np
from matplotlib.animation import FuncAnimation
import matplotlib.pyplot as plt

x = np.arange(0, 10*np.pi, 0.01)
y = np.sin(x)

fig = plt.figure()
ax = plt.subplot(1, 1, 1)

danielmk

# -*- coding: utf-8 -*-
"""
Voltage spikes, also known as action potentials, are considered the key unit of information flow in
the nervous system. One of the first quantitative descriptions of the action potential is the
Hodgkin-Huxley Model, named after nobel prize winning physiologists Alan Lloyd Hodgkin and Andrew
Fielding Huxley. This matplotlib animation shows the result of simulating the Hodgkin-Huxley model
in python with parameters that are set in a way, that the model resembles a cortical pyramidal cell.
At 50ms a current is injected, making the model spike. The upper panel shows the dynamical systems
view of the action potential. Before the current injection, the model stays at the resting
potential. When the current is injected a bifurcation occurs. The system finds a limit cycle.