iscadar

##A collection of functions for the generation of 
##neural networks and the setup of various experiments.
##by Alexandros Kourkoulas-Chondrorizos
##v0.1

from scipy import *
from scipy.signal import *
from matplotlib.pyplot import *
from matplotlib.mlab import *
import scipy.cluster.hierarchy as sch

iscadar

from scipy import *
from matplotlib.pyplot import *
##A pendulum simulation using fourth order 
##Runge-Kutta integration
##v0.1
##Alexandros Kourkoulas Chondrorizos

ts=.05 #time step size
td=40 #trial duration
te=int(td/ts) #no of timesteps

iscadar

from scipy.integrate import *
from scipy import *
from matplotlib.pyplot import *
##A pendulum simulation
##v0.2
##Alexandros Kourkoulas Chondrorizos
    
th=pi/4#((rand()*2)-1)*pi #initial angle
om=0 #initial angular velocity
u=0 #torque

iscadar

##Visualising the structural and functional connectivity
##of a neural network.
##by Alexandros Kourkoulas-Chondrorizos
##v0.1
##This is a simple function that calculates the covariance
##matrix of a neural network based on its activity. It then
##reorders the covariance matrix to obtain a depiction of
##functional connectivity and based on that reordering also
##rearranges the connectivity matrix in order to obtain a
##clearer picture of its structural connectivity. Put simply,

iscadar

##A simple genetic algorithm
##by Alexandros Kourkoulas-Chondrorizos
##v0.1
##This is one of the simplest versions of a GA out there.
##You can use it to set up any kind of evolutionary experiment
##and it's a great starting point for designing more complex
##and sophisticated GAs. Note that the function eval_ind() that
##is called in the code below isn't a real function. It's only
##a placeholder for whatever your fitness function happens to be.

iscadar

##A simple function for stimulus estimation/reconstruction
##in a neural system using a Wiener-Kolmogorov filter
##by Alexandros Kourkoulas-Chondorizos
##v0.3
##This function takes two 1-by-N arrays as input, the input 
##signal presented to the neuron or neural net and the neural
##response. It also takes two integers nfft and tstep, where 
##nfft is the number of data points used in each block for the
##FFT and tstep is the sampling frequency. nfft must be even 
##and a power of 2 is most efficient. tstep is an integer 

iscadar

##A generic network connectivity architecture
##by Alexandros Kourkoulas-Chondrorizos
##v0.2
##This is a simple function that generates a variety
##of network connectivities and consequently architectures
##anywhere from a three-layer feedforward network
##to a neural pool of dynamics. It supports recurrent
##connections, lateral and self-connections, feedforward
##connections (obviously), any degree of sparsity (0 to 
##100% connectivity) and both excitatory and inhibitory connections.