In this work, we present a Neural Network (NN) abstraction approach to reduce the state-space (number of nodes) of NN towards solving the non-scalability of NN formal verification approaches. The main idea consists in merging neurons on the NN layers in order to build an abstract model that over-approximates the original one. Concretely, the outgoing weights of the abstract network are computed as the sum of the absolute value of the weights on the original one, while the incoming weights are intervals determined based on the signs of the outgoing and the incoming weights of the original model.