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An FPGA-based approach for parameter estimation in spiking neural networks

We present an FPGA-based approach for estimating the delayed synaptic weights of spiking neural networks. Our approach makes explicit use of the fact that reverse engineering of a spiking neural network can be cast as a linear programming problem, whereby the objective function is based on the network spiking activity. The solution is obtained by employing the widely used simplex algorithm. Numerical results on a Xilinx Spartan 3 FPGA board show that the present approach can be used to reproduce a desired output from the observed network spiking activity.