apleonhardt

% Apply filter
for idx = 1:m-1
    temp_signal = conv(s(:,idx), k);
    f(:,idx) = highpass(s(:,idx), tau, dt) + temp_signal;
end

% Add DC
f = f + dc * s;

% ...

apleonhardt

%% Power spectrum demo

clear all;

dt = 0.01;

% Ten seconds at dt=0.01
t = 0:dt:10;

% Frequencies to be included in the signal. NB: Due

apleonhardt

function [output] = generate_stimulus(stimulus_type)

n_spatial = 180;
timesteps = 5000;
precision = 6000;

output = zeros(n_spatial, timesteps);

switch stimulus_type
    

apleonhardt

% EXERCISE SET 5 -- Part I

close all;
clear all;

%% Parameters

dt = 0.001; % s
dur = 1; % s
t = 0:dt:dur;

apleonhardt

%% EXERCISE 4 -- PART I: Firing rate estimation

clear all;

%% Parameters
rate = 40; % in Hz
dt = 0.001; % Time step in s
duration = 5; % in s

% NB, these are VERY wide kernels

apleonhardt

function sta = spike_triggered_avg(spikes, stimulus, winlen)

    slices = arrayfun(@(x) stimulus(x-winlen:x-1), ...
        find(spikes(winlen:end)) + winlen, ...
        'UniformOutput', 0);
    sta = mean(cat(2, slices{:}), 2);

end

apleonhardt

# Implementation of Hodgkin-Huxley model
# v2

import matplotlib.pyplot as plt

from numpy import *
from scipy import *

# Spike counting

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def decorated(func):

    def r_func(*args, **kwargs):
        print "Decorated call."
        return func(*args, **kwargs)

    return r_func

@decorated
def double(x):