The development, recognition, and learning mechanisms of an animal-like neural network

How animal neural system addresses the object identity-preserving recognition problem is largely unknown. Artificial neural network such as convolution network (CNN) has reached human level performance in recognition tasks, however, animal neural system does not support such kernel scanning operation across retinal neurons, and thus the neuronal responses do not match that of CNN units. Here, we used an alternative recognition-reconstruction network (RRN) architecture as an analogy to animal-like system, and the resulting neural characteristics agreed fairly well with electrophysiological measurements in monkey studies. First, in network development study, the RRN also experienced critical developmental stages characterized by specificities in neuronal types, connectivity strength and firing pattern, from early stage of coarse salience map recognition to mature stage of fine structure recognition. In digit recognition study, we witnessed that the RRN could maintain object invariance representation under various viewing conditions by coordinated adjustment of responses of population neurons. And such concerted population responses contained untangled object identity and properties information that could be accurately extracted via a simple weighted summation decoder. In the learning and forgetting study, novel structure recognition was implemented by adjusting entire synapses in low magnitude while pattern specificities of original synaptic connectivity were preserved, which guaranteed a learning process without disrupting the existing functionalities. This work benefits the understanding of human neural mechanism and the development of humane-like intelligence.