Artificial Evolution Network: A Computational Perspective on the Expansibility of the Nervous System.

Neurobiologists recently found the brain can use sudden emerged channels to process information. Based on this finding, we put forward a question whether we can build a computation model that is able to integrate a sudden emerged new type of perceptual channel into itself in an online way. If such a computation model can be established, it will introduce a channel-free property to the computation model and meanwhile deepen our understanding about the extendibility of the brain. In this article, a biologically inspired neural network named artificial evolution (AE) network is proposed to handle the problem. When a new perceptual channel emerges, the neurons in the network can grow new connections to connect the emerged channel according to the Hebb rule. In this article, we design a sensory channel expansion experiment to test the AE network. The experimental results demonstrate that the AE network can handle the sudden emerged perceptual channels effectively.

A Mathematical Theory of Cortex-Receptor Artificial Extension.

Many physiology experiments demonstrate that an organism's cortex and receptor system can be artificially extended, giving the organism new types of perceptual capabilities. To examine artificial extension of the cortex-receptor system, I propose a computational model that allows new types of sensory pathways to be added directly to the computational model itself in an online manner. A synapse expandable artificial neuron model that can grow new synapses, forming a bridge between the novel perceptual information and the existing neural network is introduced to absorb the novel sensory pathway. The experimental results show that the computational model can effectively integrate sudden emerged sensory channels and the neural circuits in the computational model can be reused for novel modalities without influencing the original modality.

Perception Coordination Network: A Neuro Framework for Multimodal Concept Acquisition and Binding.

To simulate the concept acquisition and binding of different senses in the brain, a biologically inspired neural network model named perception coordination network (PCN) is proposed. It is a hierarchical structure, which is functionally divided into the primary sensory area (PSA), the primary sensory association area (SAA), and the higher order association area (HAA). The PSA contains feature neurons which respond to many elementary features, e.g., colors, shapes, syllables, and basic flavors. The SAA contains primary concept neurons which combine the elementary features in the PSA to represent unimodal concept of objects, e.g., the image of an apple, the Chinese word "[píng guǒ]" which names the apple, and the taste of the apple. The HAA contains associated neurons which connect the primary concept neurons of several PSA, e.g., connects the image, the taste, and the name of an apple. It means that the associated neurons have a multimodal response mode. Therefore, this area executes multisensory integration. PCN is an online incremental learning system, it is able to continuously acquire and bind multimodality concepts in an online way. The experimental results suggest that PCN is able to handle the multimodal concept acquisition and binding effectively.

A Self-Organizing Incremental Neural Network based on local distribution learning.

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data.

Perception Evolution Network Based on Cognition Deepening Model--Adapting to the Emergence of New Sensory Receptor.

The proposed perception evolution network (PEN) is a biologically inspired neural network model for unsupervised learning and online incremental learning. It is able to automatically learn suitable prototypes from learning data in an incremental way, and it does not require the predefined prototype number or the predefined similarity threshold. Meanwhile, being more advanced than the existing unsupervised neural network model, PEN permits the emergence of a new dimension of perception in the perception field of the network. When a new dimension of perception is introduced, PEN is able to integrate the new dimensional sensory inputs with the learned prototypes, i.e., the prototypes are mapped to a high-dimensional space, which consists of both the original dimension and the new dimension of the sensory inputs. In the experiment, artificial data and real-world data are used to test the proposed PEN, and the results show that PEN can work effectively.