Generating and identifying functional subnetworks within structural networks

Abstract

In one aspect, a method includes generating a functional subgraph of a network from a structural graph of the network. The structural graph comprises a set of vertices and structural connections between the vertices. Generating the functional subgraph includes identifying a directed functional edge of the functional subgraph based on presence of structural connection and directional communication of information across the same structural connection.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method, comprising:
 receiving, at a neural network, a first input;   dividing functional activity in the neural network that is responsive to the first input into first time bins;   recording, for each of the first time bins, a first measure of the functional activity in the neural network device during that first time bin that is responsive to the first input;   characterizing one or more parameters of the recorded first measure of the functional activity using topological methods; and   receiving, at the neural network, a second input;   dividing functional activity in the neural network that is responsive to the second input into second time bins;   recording, for each of the second time bins, a measure of the functional activity in the neural network device during that second time bin that is responsive to the first input;   characterizing one or more parameters of the recorded second measure of the functional activity using topological methods; and   distinguishing the functional response of the neural network to the first input from the functional response of the neural network to the second input based on the characterized topological parameters.   
     
     
         22 . The method of  claim 21 , wherein the topological methods comprise determining associated directed flag complexes. 
     
     
         23 . The method of  claim 21 , wherein the functional activity in the neural network that is responsive to the first and second inputs includes signal transmission along edges of the neural network. 
     
     
         24 . The method of  claim 21 , wherein the duration of the time bins is constant. 
     
     
         25 . The method of  claim 21 , wherein the measure of the functional activity is a functional connectivity matrix. 
     
     
         26 . The method of  claim 21 , wherein the first input and the second input are known inputs. 
     
     
         27 . The method of  claim 21 , wherein the method further comprises determining that neural network device is functioning properly or trained based on the distinguishing of the functional response to the first input from the functional response to the second input. 
     
     
         28 . A computer-implemented method, comprising:
 receiving, at a first neural network, an input;   dividing functional activity in the neural network that is responsive to the input into time bins;   recording, for each of the time bins, a measure of the functional activity in the neural network device during that time bin that is responsive to the input;   characterizing one or more parameters of the recorded measure of the functional activity using topological methods; and   reconstructing at least some of functioning of the first neural network in a second neural network using the characterizations provided by the topological methods.   
     
     
         29 . The method of  claim 28 , wherein the second neural network is simpler than the first neural network. 
     
     
         30 . The method of  claim 28 , wherein the functioning of the first neural network is reconstructed in the second neural network without training the second neural network. 
     
     
         31 . The method of  claim 28 , wherein the topological methods comprise determining associated directed flag complexes. 
     
     
         32 . The method of  claim 28 , wherein the functional activity in the neural network that is responsive to the input includes signal transmission along edges of the neural network. 
     
     
         33 . The method of  claim 28 , wherein the duration of the time bins is constant. 
     
     
         34 . The method of  claim 28 , wherein the measure of the functional activity is a functional connectivity matrix. 
     
     
         35 . The method of  claim 28 , wherein the method further comprises determining that second neural network is functioning properly or trained based on a functional response of the second neural network to the input pattern.