Implementation of a biologically inspired neuron-model in FPGA

This paper presents the implementation of a biologically inspired neuron-model. Learning is performed on-line in special synapses based on the biologically proved Hebbian learning algorithm. This algorithm is implemented on-chip allowing an architecture of autonomous neural units. The algorithm is transparent so connections between the neurons can easily be engineered. Due to their functionality and their flexibility only few neurons are needed to fulfil basic tasks. A parallel and a serial concept for an implementation in an FPGA (Field Programmable Gate-Array) are discussed. A prototype of the serial approach is developed in a XILINX FPGA series 3090. This solution has one excitatory, one inhibitory, two Hebbian synapses and one output operating with 8 bit resolution. The internal computation is performed at higher resolution to eliminate errors due to overflow. The Hebbian weights are stored at a precision of 19 bit for multiplication. The prototype works at a clock frequency of 5 MHz leading to an update rate of 333 kCUPS.