A method of producing data representing an identifier of a neuron from a cluster of L neurons belonging to a neural network having C clusters. L and C are natural integers of values greater than or equal to two. Each neuron has at least two states. The method includes, for at least one current cluster Ci: producing a set E of neural states originating from at least one cluster Cj, j≠i; producing a set A of coefficients of adjacency between at least one neuron of the current cluster Ci, and at least one neuron of a cluster Cj of the neural network j≠i; calculating, as a function of the set E of neural states, the set A of adjacency coefficients and, as a function of state(s) of the neurons of the current cluster Ci, at least one winning neuron NG.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for obtaining, within a system, a piece of data representing an identifier of one neuron from among a set comprising L neurons called a cluster, L being a natural integer of a value greater than or equal to two, said cluster belonging to a neural network comprising C clusters, C being a natural integer of a value greater than or equal to two, each neuron of said neural network comprising a current state among at least two possible states, each neuron of said neural network belonging to a single cluster, where the method, during an iterative process of transmission of states of neurons between said C clusters of said neural network, for at least one current cluster Ci among said C clusters: obtaining from the system comprising the neural network a set E of current states of neurons originating from at least one cluster Cj, j≠i; at least one act of obtaining a set A of coefficients of adjacency between a neuron of said current cluster Ci, and one neuron of a cluster Cj of the neural network j≠i; and computing, as a function of said state E of states of neurons, said set A of coefficients of adjacency and, as a function of at least one state among the states of said neurons of said current cluster Ci, at least one winning neuron NG, delivering said piece of data representing an identifier of said at least one winning neuron NG. 2. The method according to claim 1, wherein the computing comprises, for a current neuron ni,j of said current cluster Ci, the application of the following formula: ϑ  ( n i , j , t + 1 ) =  k = 1 , k ≠ i C  ( (  g = 1 L  ϑ  ( n k , g , t )  w ( i , j )  ( k , g ) )  (  g = 1 L  ϑ  ( n k , g , t ) _ ) ) wherein: (ni,j,t+1) is the state of the neuron ni,j at the instant t+1; Λk=1,k≠iC( . . . ) is a conjunction (logic AND) of C−1 non-zero binary elements given by the logic equations applied to all the other clusters of the neural network; w(i,j)(k,g) is the coefficient of adjacency between the neuron nk,g and the neuron ni,j; (nk,g,t) is the state of the neuron nk,g at the instant t; Vg=1L . . . is the operation of disjunction (logic OR) of L binary elements representing the state (active or not active) at the instant t of the neurons of the remote clusters; ( Vg=1L . . . ) is the operation of complemented disjunction (logic NOR) of L binary elements. 3. The method according to claim 2, wherein the computing comprises selection, from among the neurons of said current cluster Ci, of the neuron NG, the state of which at the instant t+1 is 1. 4. The method according to claim 2, wherein the computing comprises selection, from among the neurons of said current cluster Ci, of the neuron NG, the state of which at the instant t+2 is 1, in applying the following function: ϑ  ( n i , j , t + 2 ) =  i = 1 L  ( ϑ  ( n i , t + 1 )  Λ  (  j = 1 , j ≠ l L  ϑ  ( n j , t + 1 ) ) _ ′ ) 5. The method according to claim 1, wherein obtaining a set A of coefficients of adjacency originating from at least one cluster Cj, j≠i comprises a plurality of acts of access to at least one centralized structure for memorizing coefficients of adjacency of neurons of said neurons of said clusters of said neural network. 6. The method according to claim 5, wherein said at least one centralized structure for memorizing coefficients of adjacency of neurons takes the form of a blockwise triangular matrix comprising a number of blocks equal to Σi=1C-1 i, one block comprising L coefficients of adjacency. 7. The method according to claim 1, wherein the obtaining a set E of states of neurons originating from at least one cluster Cj, j≠i comprises L acts of simultaneous transmission by each cluster Cj, j≠i, of a single state of a single neuron. 8. The method according to claim 1, where in the obtaining a set E of states of neurons originating from at least one cluster Cj, j≠i comprises C acts of simultaneous transmission by each cluster Cj, j≠i, of all the states of the cluster. 9. The method according to claim 7, wherein the obtaining a set E of states of neurons originating from at least one cluster Cj, j≠i comprises, within said current cluster Ci, implementing a shift register of a predetermined size. 10. The method according to claim 1, wherein said at least one cluster Cj, implements at least one part of said of computing and transmits to said cluster Ci, the sum and/or the disjunction of the coefficients of adjacency of these active neurons. 11. A device for obtaining, within a system comprising a neural network, a piece of data representing an identifier of one neuron from among a set comprising L neurons called a cluster, L being a natural integer of a value greater than or equal to two, said cluster belonging to a neural network comprising C clusters, C being a natural integer of a value greater than or equal to two, each neuron of said neural network comprising a current state among at least two possible states, each neuron of said neural network belonging to a single cluster, wherein the device comprises: means for implementing an iterative process of transmission of states of neurons between said C clusters of said neural network, for at least one current cluster Ci among said C clusters, including: means for obtaining a set E of current states of neurons originating from at least one cluster Cj, j≠i; means for obtaining a set A of coefficients of adjacency between one neuron of said current cluster Ci, and one neuron of a cluster Cj of the neural network j≠i; and means for computing, as a function of said state E of states of neurons, said set A of coefficients of adjacency and as a function of at least one state among states of said neurons of said current cluster Ci, at least one winning neuron NG, delivering said piece of data representing an identifier of said at least one winning neuron NG. 12. A non-transitory computer-readable medium comprising a computer program product recorded thereon and comprising program code instructions execution of a method for obtaining, within a system, a piece of data representing an identifier of one neuron from among a set comprising L neurons called a cluster, when the instructions are executed on a processor, wherein L is a natural integer of a value greater than or equal to two, said cluster belongs to a neural network comprising C clusters, C is a natural integer of a value greater than or equal to two, each neuron of said neural network comprises a current state among at least two possible states, and each neuron of said neural network belongs to a single cluster, wherein the instructions configure the processor to perform the following acts during an iterative process of transmission of states of neurons between said C clusters of said neural network, for at least one current cluster Ci among said C clusters: obtaining a set E of current states of neurons originating from at least one cluster Cj, j≠i; at least one act of obtaining a set A of coefficients of adjacency between a neuron of said current cluster Ci, and one neuron of a cluster Cj of the neural network j≠i; and computing, as a function of said state E of states of neurons, said set A of coefficients of adjacency and, as a function of at least one state among the states of said neurons of said current cluster Cj, at least one winning neuron NG, delivering said piece of data representing an identifier of said at least one winning neuron NG.