A convolutional neural network comprises a plurality of artificial neurons arranged in one or more convolution layers, each convolution layer comprising one or more output matrices, each output matrix comprising a set of output neurons, each output matrix being connected to an input matrix, comprising a set of input neurons, by artificial synapses associated with a convolution matrix. The convolution matrix comprises the weight coefficients associated with the output neurons of the output matrix, the output value of each output neuron being determined from the input neurons of the input matrix to which the output neuron is connected and the weight coefficients of the convolution matrix associated with the output matrix. Each synapse consists of a set of memristive devices comprising at least one memristive device, each set of memristive devices storing a weight coefficient of the convolution matrix. In response to a change of the output value of an input neuron of an input matrix, the neural network is capable of dynamically associating each set of memristive devices storing a coefficient of the convolution matrix with an output neuron connected to the input neuron. The neural network further comprises an accumulator for each output neuron, the accumulator being configured to accumulate the values of the weight coefficients stored in the sets of memristive devices dynamically associated with the output neuron, the output value of the output neuron being determined from the value accumulated in the accumulator.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A convolutional neural network comprising a plurality of artificial neurons arranged in one or more convolution layers, each convolution layer comprising one or more output matrices, each output matrix comprising a set of output neurons, each output matrix being connected to an input matrix, comprising a set of input neurons, by artificial synapses associated with a convolution matrix comprising the weight coefficients associated with the output neurons of said output matrix, the output value of each output neuron being determined from the input neurons of said input matrix to which the output neuron is connected and the weight coefficients of the convolution matrix associated with said output matrix, wherein each synapse consists of a set of memristive devices comprising at least one memristive device, each set of memristive devices storing a weight coefficient of said convolution matrix, and in that wherein, in response to a change of the output value of an input neuron of an input matrix, the neural network is capable of dynamically associating each set of memristive devices storing a coefficient of the convolution matrix with an output neuron connected to said input neuron, the neural network further comprising an accumulator for each output neuron, said accumulator being configured to accumulate the values of the weight coefficients stored in the sets of memristive devices dynamically associated with said output neuron, the output value of said output neuron being determined from the value accumulated in said accumulator. 2. The neural network as claimed in claim 1, wherein the neurons use a time coding, the output value of the input neurons and of the output neurons is coded with spikes. 3. The neural network as claimed in claim 2, wherein the propagation of a value in the network is produced by propagation of at least one spike coding said value, according to said time coding. 4. The neural network as claimed in claim 1, comprising a set of switches and a logic circuit mapping the weight coefficients with the output neurons connected to the input neuron having undergone a change of output value, from the address of said input neuron in the input matrix, to produce said dynamic association. 5. The neural network as claimed in claim 3, wherein each spike comprises a bitstream coding the destination address of the spike according to two right-angled axes X and Y, the reference point corresponding to the reference point of the input matrix and wherein, in response to the arrival of said spike on the input matrix, said address is coded to represent the location of the input neuron to be activated. 6. The neural network as claimed in claim 1, wherein the dynamic association is produced in parallel and in a single cycle, by simultaneously connecting the weight coefficients of the convolution matrix stored in said sets of memristive devices to the output neurons connected to the input neuron having undergone a change of output value. 7. The neural network as claimed in claim 1, wherein the dynamic association is produced semi-sequentially, by connecting the weight coefficients of the convolution matrix stored in said memristive devices, one row of the matrix after the other, to the output neurons connected to the input neuron and having undergone a change of output value. 8. The neural network as claimed in claim 1, wherein the dynamic association is produced semi-sequentially, by connecting the weight coefficients of the convolution matrix stored in said memristive devices, one column of the matrix after the other, to the output neurons connected to the input neuron having undergone a change of output value. 9. The neural network as claimed in claim 1, wherein the dynamic association is produced sequentially, by connecting the coefficients of the convolution matrix, stored in said memristive devices, one after the other, to the output neurons connected to the input neuron having undergone a change of output value. 10. The neural network as claimed in claim 1, wherein the network comprises an accumulator arranged at each output neuron to store the value accumulated in said output neuron, and an auxiliary accumulator arranged at the output of each synapse and wherein, in response to the dynamic association of a set of memristive devices constituting a synapse with an output neuron, the auxiliary accumulator arranged at the output of said synapse produces the accumulation of the value of the weight coefficient stored in said set of memristive devices with the value stored in the accumulator arranged at said output neuron, the value stored in said auxiliary accumulator being then propagated in the accumulator arranged at said output neuron. 11. The neural network as claimed in claim 1, wherein the network comprises an accumulator arranged at each output neuron to store the value accumulated in said output neuron and wherein, in response to the dynamic association of a set of memristive devices with said output neuron, the weight coefficient of said set of memristive devices is propagated in the accumulator arranged at said output neuron, the value thus propagated then being accumulated with the value stored in said accumulator. 12. The neural network as claimed in claim 1, wherein the outputs of output neurons are grouped together, and wherein the accumulated values corresponding to output neurons whose outputs are grouped together are stored in a common accumulator. 13. The neural network as claimed in claim 1, comprising online learning of STDP type from the dynamic mapping. 14. The neural network as claimed in claim 1, wherein the memristive devices are emulated by digital memory cells.