MON: Multiple Output Neurons

Existing basic artificial neurons merge multiple weighted inputs and generate a single activated output. This paper explores the applicability of a new structure of a neuron, which merges multiple weighted inputs like existing neurons, but instead of generating single output, it generates multiple outputs. The proposed “Multiple Output Neuron” (MON) can reduce computation in a basic XOR network. Furthermore, a MON based convolutional neural network layer (MONL) is described. Proposed MONL can backpropagate errors, thus can be used along with other CNN layers. MONL reduces the network computations, by reducing the number of filters. Reduced number of filters limits the network performance, thus MON based neuroevolution (MON-EVO) technique is also proposed. MON-EVO evolves the MONs into single output neurons for further improvement in training. Existing neuroevolution techniques do not utilize backpropagation but MONs can utilize backpropagation. Experimental networks trained using the CIFAR-10 classification dataset show that proposed MONL and MON-EVO provide a solution for reduced training computation and neuroevolution using backpropagation