[From Grid Cells to Place Cells:A Gauss Distribution Activation Function Model].

It has been found that in biological studies,the simple linear superposition mathematical model cannot be used to express the feature mapping relationship from multiple activated grid cells' grid fields to a single place cell's place field output in the hippocampus of the cerebral cortex of rodents.To solve this problem,people introduced the Gauss distribution activation function into the area.We in this paper use the localization properties of the function to deal with the linear superposition output of grid cells' input and the connection weights between grid cells and place cells,which filters out the low activation rate place fields.We then obtained a single place cell field which is consistent with biological studies.Compared to the existing competitive learning algorithm place cell model,independent component analysis method place cell model,Bayesian positon reconstruction method place cell model,our experimental results showed that the model on the neurophysiological basis can not only express the feature mapping relationship between multiple activated grid cells grid fields and a single place cell's place field output in the hippocampus of the cerebral cortex of rodents,but also make the algorithm simpler,the required grid cells input less and the accuracy rate of the output of a single place field higher.

A high-sensitivity rotatable 3D displacement sensor.

Aiming at the problems of low sensitivity and low accuracy caused by the displacement transfer mechanism of three displacement sensors used simultaneously in the 3D displacement monitoring of seismic isolation bearings, the paper has proposed a high-sensitivity rotatable 3D displacement sensor. The sensor adds through holes on the surface of the equal-strength cantilever beam to form a cross beam, which increases the bending strain on the beam surface to improve the sensitivity. By adding a gyroscope and a mechanical rotation structure, a single sensor can measure the 3D displacement at the same time, reducing the adverse effects displacement transmission mechanism on the accuracy of the measurement. ANSYS software was used to simulate and optimize the parameters of the size of through-hole of the sensor beam to determine the appropriate size and location of the through-hole. Finally, the sensor was developed and its static characteristics and displacement measurement performance in static and dynamic 3D space were tested based on the simulation results. The test results have shown that the sensor has a sensitivity of 16.29 mV/mm and an accuracy of 0.9% in the range of 0-160 mm. Its static and dynamic 3D spatial displacement measurement errors are less than 2 mm, which can meet the accuracy requirements of 3D displacement measurement and sensitivity for structural health monitoring of seismic isolation bearings.

A Cognitive Model Based on Neuromodulated Plasticity.

Associative learning, including classical conditioning and operant conditioning, is regarded as the most fundamental type of learning for animals and human beings. Many models have been proposed surrounding classical conditioning or operant conditioning. However, a unified and integrated model to explain the two types of conditioning is much less studied. Here, a model based on neuromodulated synaptic plasticity is presented. The model is bioinspired including multistored memory module and simulated VTA dopaminergic neurons to produce reward signal. The synaptic weights are modified according to the reward signal, which simulates the change of associative strengths in associative learning. The experiment results in real robots prove the suitability and validity of the proposed model.