In neuromorphic computing, the coding method of spiking neurons serves as the foundation and is crucial for various aspects of network operation. Existing mainstream coding methods, such as rate coding and temporal coding, have different focuses, and each has its own advantages and limitations. This paper proposes a novel coding scheme called activeness coding that integrates the strengths of both rate and temporal coding methods. It encompasses precise timing information of the most recent neuronal spike as well as the historical firing rate information. The results of basic characteristic tests demonstrate that this encoding method accurately expresses input information and exhibits robustness. Furthermore, an unsupervised learning method based on activeness-coding triplet spike-timing dependent plasticity (STDP) is introduced, with the MNIST classification task used as an example to assess the performance of this encoding method in solving cognitive tasks. Test results show an improvement in accuracy of approximately 4.5%. Additionally, activeness coding also exhibits potential advantages in terms of resource conservation. Overall, activeness offers a promising approach for spiking neural network encoding with implications for various applications in the field of neural computation.

Physiological experiments have shown that rats and other mammals can form neural representations of the spatial environment after exploring an unfamiliar environment, thus achieving goal-directed navigation. This spatial cognitive ability benefits from parallel processing of sensory information in rat brain networks, where the hippocampus structure is thought to be a key brain region for forming episodic memory and environmental cognition, while the prefrontal cortex receives reward information about spatiotemporal events and makes decisions. These two brain regions interact, ultimately producing goal-directed behavior and sequence planning. In order to simulate the cognitive and decision-making abilities of rats in a spatial environment, a model based on the interaction between the hippocampus and the prefrontal cortex is proposed. The model realizes goal-oriented navigation through reward propagation and forward replay mechanism. The experimental results show that the bionic navigation model can simulate the cognitive and decision-making ability of rats, which is consistent with the results of physiological studies. It provides a reference for understanding the operation mechanism of brain in cognitive process.

Physiological studies have shown that the hippocampal structure of rats develops at different stages, in which the place cells continue to develop during the whole juvenile period of rats and mature after the juvenile period. As the main information source of place cells, grid cells should mature earlier than place cells. In order to make better use of the biological information exhibited by the rat brain hippocampus in the environment, we propose a position cognition model based on the spatial cell development mechanism of rat hippocampus. The model uses a recurrent neural network with parametric bias (RNNPB) to simulate changes in the discharge characteristics during the development of a single stripe cell. The oscillatory interference mechanism is able to fuse the developing stripe waves, thus indirectly simulating the developmental process of the grid cells. The output of the grid cells is then used as the information input of the place cells, whose development process is simulated by BP neural network. After the place cells matured, the position matrix generated by the place cell group was used to realize the position cognition of rats in a given spatial region. The experimental results show that this model can simulate the development process of grid cells and place cells, and it can realize high precision positioning in the given space area. Moreover, the experimental effect of cognitive map construction using this model is basically consistent with the effect of RatSLAM, which verifies the validity and accuracy of the model.