General object recognition in complex backgrounds is still challenging. On one hand, the various backgrounds, where object may appear at different locations, make it difficult to find the object of interest. On the other hand, with the numbers of locations, types and variations in each type (e.g., rotation) increasing, conventional model-based approaches start to break down. The Where-What Networks (WWNs) were a biologically inspired framework for recognizing learned objects (appearances) from complex backgrounds. However, they do not have an adaptive receptive field for an object of a curved contour. Leaked-in background pixels will cause problems when different objects look similar. This work introduces a new biologically inspired mechanism - synapse maintenance and uses both supervised (motor-supervised for class response) and unsupervised learning (synapse maintenance) to realize objects recognition. Synapse maintenance is meant to automatically decide which synapse should be active firing of the post-synaptic neuron. With the synapse maintenance, the network has achieved a significant improvement in the network performance.