  Spiking Neural Networks (SNN) are third-generation Artificial Neural Networks
(ANN) which are close to the biological neural system. In recent years SNN has
become popular in the area of robotics and embedded applications, therefore, it
has become imperative to explore its real-time and energy-efficient
implementations. SNNs are more powerful than their predecessors because they
encode temporal information and use biologically plausible plasticity rules. In
this paper, a simpler and computationally efficient SNN model using FPGA
architecture is described. The proposed model is validated on a Xilinx Virtex 6
FPGA and analyzes a fully connected network which consists of 800 neurons and
12,544 synapses in real-time.
