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, feed

netgen.py

##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.
##This function takes three arguments: N, C and D. N is a 
##1 by n list of integers larger than 1, where n is an 
##integer from 1 to 3 and signifies the number of layers
##in the architecture of the network. Each value in N is the
##number of neurons in each layer. C and D are L by L lists
##where L is the number of layers in the architecture. C stores
##the synaptic strength of each set of connections and D stores
##the sparsity of each set of connections. This function builds
##subsets of connections with individual strengths and sparsities
##which connect any one layer to another. The subsets are then
##concatenated into a big matrix which describes the connectivity
##and the overall architecture of the network.
##
##    Example: This generates a simple three-layer feedforward net
##        >>>N=[2, 3, 2]#neurons per layer
##        >>>C=[[1,2,3],[4,5,4],[3,2,1]]#connection strength
##        >>>D=[[.5,0,0],[0,.7,0],[0,0,.5]]#connection density
##        >>>S=netgen(N,C,D)
##        >>>print S
##[[ 0.         0.96162857 0.         0.         0.         0.         0.       ]
## [ 0.15385049 0.         0.         0.         0.         0.         0.       ]
## [ 0.         0.         0.         0.         0.         0.         0.       ]
## [ 0.         0.         0.17980379 0.09374685 0.         0.         0.       ]
## [ 0.         0.         2.41982174 1.72593395 0.         0.         0.       ]
## [ 0.         0.         0.         0.         0.         0.99835747 0.       ]
## [ 0.         0.         0.         0.         0.         0.98608675 0.985044 ]]

from scipy import *
from secnet import sprand


def netgen(N,C,D): #generic network architecture
    L=len(N) #number of layers
    K=sum(N) #total number of neurons
    # check parameters here
    if not(isinstance(N,list)) or L < 1.0 or L > 3.0 or len(shape(N))!=1 or K<2:
        raise ValueError('N should be a 1 by n list of integers >1, where n is an integer from 1 to 3')
    if shape(C)[0]!=shape(C)[1]!=L or shape(D)[0]!=shape(D)[1]!=L:
        raise ValueError('C and D should both be L by L lists, where L is the number of layers of the network')
    
    S=zeros((K,K)) #empty synaptic matrix
    for i in range(L):
        for j in range(L):
            S[(sum(N[:i+1])-N[i]):sum(N[:i+1]),(sum(N[:j+1])-N[j]):sum(N[:j+1])]= \
            (C[i][j]*sprand(N[i],N[j],D[i][j]).todense())        
    
    return S,K