https://www.cogsci.ucsd.edu/~sereno/107B-201/readings/08.10-ConjunctiveRepEnto.pdf

Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex

The invention provides a man-machine recognition method and device and a behavior characteristic data collecting method and device. The man-machine recognition method includes the steps that responding the man-machine recognition request sent by a client side, first operation used for man-machine recognition is rendered to be sent to the client side for displaying; second operation which is submitted by the client side and triggered by operation track represented by the first operation is received; according to a preset behavior characteristic model, man-machine recognition is carried out on a triggering body of the second operation. In the embodiment, accuracy of human-machine recognition can be improved, and user experience in the man-machine recognition process can be improved.

Man-machine recognition method and device and behavior characteristic data collecting method and device

The invention provides a robot navigation map construction method based on rat brain hippocampus spatial cells. According to an information transfer loop of spatial navigation related cells in a hippocampus structure of a mammal, a robot acquires a current autonomic movement clue and color depth image information through environment exploration. The autonomic movement clue progressively forms coding to the spatial environment through path integration and characteristic extraction of the spatial cells in the hippocampus structure. The place field of place cells is gradually formed in an exploration process and furthermore covers the whole spatial environment, thereby forming a cognitive map. Meanwhile, Kinect acquires color depth image information of a view scene right in front of the current position as an absolute reference for performing closed-loop detection on the path and correcting a path integration error. At a closed-loop point, a system resets spatial cell discharging activity and performs correction on the path integration error. Nodes on a final navigation map comprise place cell mass coding information, corresponding visual cues and place topological relationships.

Robot navigation map construction method based on rat brain hippocampus spatial cells

Described is a robotic system for detecting obstacles reliably with their ranges by a combination of two-dimensional and three-dimensional sensing. In operation, the system receives an image from a monocular video and range depth data from a range sensor of a scene proximate a mobile platform. The image is segmented into multiple object regions of interest and time-to-contact (TTC) value are calculated by estimating motion field and operating on image intensities. A two-dimensional (2D) TTC map is then generated by estimating average TTC values over the multiple object regions of interest. A three-dimensional TTC map is then generated by fusing the range depth data with image. Finally, a range-fused TTC map is generated by averaging the 2D TTC map and the 3D TTC map.

System and method for achieving fast and reliable time-to-contact estimation using vision and range sensor data for autonomous navigation

The invention discloses an indoor localization method based on a foot inertia sensor According to the indoor localization method, a framework of a grid cell path integrating method integrated and simulated on the basis of extended Kalman filter estimation is used; the information of localization positions and directions is obtained by calculating data of the inertia sensor; a plurality of condition judging criteria are used by zero-velocity correction for judging whether the feet touch the ground or not; the current velocity is adjusted to zero and the Kalman filter estimation is triggered when the conditions are met; the calculated nonlinear error increase of the velocity and the position information are corrected; then the velocity information is used as input and a grid cell path integration method is used for further eliminating drift error of the position information

Indoor localization method based on foot inertia sensor

The invention discloses a robot simulated navigation method based on rat brain-hippocampal navigation, and belongs to the technical field of robot motion navigation. The method is characterized by that a robot CPU (Central Processing Unit) predefines a grid cell layer cell layer G and a place cell layer P by imitating rat brain and hippocampal tissues, motion information including a motion direction phi and a velocity v is collected when a robot explores an environment, the collected motion information is input into the grid cell layer, the motion information is integrated by using a shock interference model in the grid cell layer to form a grid cell layer response, a connection weight value W of the grid cell layer cell layer G and a place cell layer P is calculated by using a neural network algorithm to form a location cell-grid cell response network, and the robot perception for a space namely is stored in the form of a neural network response. The robot controls a motion decision module to implement motion decision-making according to space information stored in a cognitive map, and the navigation method disclosed by the invention independently obtains a better spatial cognitive effect during an exploration motion of the robot, and can be applied to industrial robots, service robots and the like.

Robot simulated navigation method based on rat brain-hippocampal navigation

The invention relates to a handwriting digital recognition method based on improved distance core principal component analysis, and belongs to the field of pattern recognition and artificial intelligence The handwriting digital recognition method is characterized in that an MNIST data base is used as a test object, a sample is first subjected to binaryzation preprocessing, processed sample data are mapped to a core feature space through a distance core, a K mean value cluster is used for dividing a training sample set into N kinds of secondary sets in the core feature space, then the feature vector of a core matrix in a core space to acquire a transformational matrix which is used for feature extraction, an extracted feature data set is put into a support vector machine to establish a training model, and finally the model is used for forecasting recognition rate of test samples According to the handwriting digital recognition method, handwriting digital recognition rate can be well improved, operation time is lowered, and the problem that a large sample is large in computing cost is solved

Handwriting digital recognition method based on improved distance core principal component analysis

The invention relates to a method for analyzing function of a mouse hippocampus structure in an environment cognition process, and belongs to the technical field of bionics. The method comprises the following steps of establishing a mathematic model of the mouse hippocampus structure through a mathematic method to simulate the cognition process of a mouse on an unknown environment; through the established model, reflecting the biological functions of four types of cognition cells of the hippocampus structure. The method has the advantages that by considering the four types of cells of the hippocampus structure with the cognition functions on the environment, when the mouse enters the environment again, the current position and environment boundary of the mouse can be judged according to the activation of the position cells and the boundary cells; the method can be applied to the fields of robot navigation, medical therapy, physiology, and the like.

Method for analyzing function of mouse hippocampus structure in environment cognition process

The invention relates to a light-stream-based obstacle avoiding method of a mobile robot based, belonging to the technical field of navigation and obstacle avoiding application of robots The method is characterized by comprising the following steps: obtaining an image sequence through an obstacle-avoiding camera on the robot, and calculating the light streams of front and back frames of an image; when the amplitudes of the light streams exceed a threshold value, calculating TTC (Time To Contact) according to the light streams; establishing a 1*N OM (Obstacle Map) according to TTC; then, setting a threshold Tau, extracting a continuous column of the OM with the element value being less than Tau as a candidate safe region; calculating the weight of each region, taking the region with the great weight as the optimal region, and calculating the local centroid as the optimal forward direction; and finally, controlling the robot to turn to the optimal forward direction so as to avoid obstacles According to the method provided by the invention, the robot can analyze distribution of obstacles in an unknown and complex environment with lots of barriers and can move towards the safest direction, so that the method can be used for real-time obstacle-avoidance of robots

Light-stream-based obstacle avoiding method of mobile robot

The entorhinal cortex (EC) plays a pivotal role for spatial representation and navigation in rat. Understanding how information is processed in the entorhinal cortex is essential to help us know the self-motion-based navigation. This article introduces the connection of entorhinal cortex and hippocampus, main cells in entorhinal cortex and their interactions, then put forward one model of the entorhinal cortex. The structure of this model is consistent with head direction cells and grid cells in entorhinal cortex, simulating grid cells firing fields very well.

The computational model of entorhinal cortex

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