scholarly journals A Model of Spatial Cell Development in Rat Hippocampus Based on Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Naigong Yu ◽  
Hejie Yu ◽  
Yishen Liao ◽  
Zongxia Wang ◽  
Ouattara Sie
Keyword(s):  
Neural Network ◽  
Development Process ◽  
Information Source ◽  
Cell Development ◽  
Place Cells ◽  
Rat Hippocampus ◽  
Cell Group ◽  
Grid Cells ◽  
Juvenile Period ◽  
Spatial Cell

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.

scholarly journals A Brain-Inspired Adaptive Space Representation Model Based on Grid Cells and Place Cells

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Kun Han ◽  
Dewei Wu ◽  
Lei Lai
Keyword(s):  
Intelligent Agents ◽  
Autonomous Navigation ◽  
Spatial Representation ◽  
Information Source ◽  
Grid Cell ◽  
Place Cells ◽  
Space Representation ◽  
Generation Model ◽  
Grid Cells ◽  
Representation Model

Grid cells and place cells are important neurons in the animal brain. The information transmission between them provides the basis for the spatial representation and navigation of animals and also provides reference for the research on the autonomous navigation mechanism of intelligent agents. Grid cells are important information source of place cells. The supervised learning and unsupervised learning models can be used to simulate the generation of place cells from grid cell inputs. However, the existing models preset the firing characteristics of grid cell. In this paper, we propose a united generation model of grid cells and place cells. First, the visual place cells with nonuniform distribution generate the visual grid cells with regional firing field through feedforward network. Second, the visual grid cells and the self-motion information generate the united grid cells whose firing fields extend to the whole space through genetic algorithm. Finally, the visual place cells and the united grid cells generate the united place cells with uniform distribution through supervised fuzzy adaptive resonance theory (ART) network. Simulation results show that this model has stronger environmental adaptability and can provide reference for the research on spatial representation model and brain-inspired navigation mechanism of intelligent agents under the condition of nonuniform environmental information.

A MODEL OF GRID CELLS BASED ON A TWISTED TORUS TOPOLOGY

2007 ◽  
Vol 17 (04) ◽  
pp. 231-240 ◽  
Author(s):  
ALEXIS GUANELLA ◽  
DANIEL KIPER ◽  
PAUL VERSCHURE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Entorhinal Cortex ◽  
Firing Frequency ◽  
Place Cells ◽  
Grid Cells ◽  
Medial Entorhinal Cortex ◽  
Triangular Grids ◽  
Multiple Regions ◽  
Robust To Noise

The grid cells of the rat medial entorhinal cortex (MEC) show an increased firing frequency when the position of the animal correlates with multiple regions of the environment that are arranged in regular triangular grids. Here, we describe an artificial neural network based on a twisted torus topology, which allows for the generation of regular triangular grids. The association of the activity of pre-defined hippocampal place cells with entorhinal grid cells allows for a highly robust-to-noise calibration mechanism, suggesting a role for the hippocampal back-projections to the entorhinal cortex.

scholarly journals Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Yedidyah Dordek ◽  
Daniel Soudry ◽  
Ron Meir ◽  
Dori Derdikman
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Hexagonal Lattice ◽  
Grid Cell ◽  
Principal Component ◽  
Component Analysis ◽  
Place Cell ◽  
Place Cells ◽  
Grid Cells ◽  
Spacing Ratio

Many recent models study the downstream projection from grid cells to place cells, while recent data have pointed out the importance of the feedback projection. We thus asked how grid cells are affected by the nature of the input from the place cells. We propose a single-layer neural network with feedforward weights connecting place-like input cells to grid cell outputs. Place-to-grid weights are learned via a generalized Hebbian rule. The architecture of this network highly resembles neural networks used to perform Principal Component Analysis (PCA). Both numerical results and analytic considerations indicate that if the components of the feedforward neural network are non-negative, the output converges to a hexagonal lattice. Without the non-negativity constraint, the output converges to a square lattice. Consistent with experiments, grid spacing ratio between the first two consecutive modules is −1.4. Our results express a possible linkage between place cell to grid cell interactions and PCA.

scholarly journals Path integration in place cells of developing rats

2018 ◽  
Vol 115 (7) ◽  
pp. E1637-E1646 ◽  
Author(s):  
Tale L. Bjerknes ◽  
Nenitha C. Dagslott ◽  
Edvard I. Moser ◽  
May-Britt Moser
Keyword(s):  
Path Integration ◽  
Grid Cell ◽  
Cell System ◽  
Place Cells ◽  
Postnatal Life ◽  
Motion Information ◽  
Grid Cells ◽  
Adult Rats ◽  
Self Motion ◽  
Made In

Place cells in the hippocampus and grid cells in the medial entorhinal cortex rely on self-motion information and path integration for spatially confined firing. Place cells can be observed in young rats as soon as they leave their nest at around 2.5 wk of postnatal life. In contrast, the regularly spaced firing of grid cells develops only after weaning, during the fourth week. In the present study, we sought to determine whether place cells are able to integrate self-motion information before maturation of the grid-cell system. Place cells were recorded on a 200-cm linear track while preweaning, postweaning, and adult rats ran on successive trials from a start wall to a box at the end of a linear track. The position of the start wall was altered in the middle of the trial sequence. When recordings were made in complete darkness, place cells maintained fields at a fixed distance from the start wall regardless of the age of the animal. When lights were on, place fields were determined primarily by external landmarks, except at the very beginning of the track. This shift was observed in both young and adult animals. The results suggest that preweaning rats are able to calculate distances based on information from self-motion before the grid-cell system has matured to its full extent.

Grid Cells Lose Coherence in Realistic Environments

2021 ◽  
Author(s):  
Yifan Luo ◽  
Matteo Toso ◽  
Bailu Si ◽  
Federico Stella ◽  
Alessandro Treves
Keyword(s):  
Short Range ◽  
Short Range Order ◽  
Grid Cell ◽  
Place Cells ◽  
Grid Cells ◽  
Lateral Connectivity ◽  
Cell Pattern ◽  
In The Wild ◽  
Freely Moving ◽  
Adaptation Model

Spatial cognition in naturalistic environments, for freely moving animals, may pose quite different constraints from that studied in artificial laboratory settings. Hippocampal place cells indeed look quite different, but almost nothing is known about entorhinal cortex grid cells, in the wild. Simulating our self-organizing adaptation model of grid cell pattern formation, we consider a virtual rat randomly exploring a virtual burrow, with feedforward connectivity from place to grid units and recurrent connectivity between grid units. The virtual burrow was based on those observed by John B. Calhoun, including several chambers and tunnels. Our results indicate that lateral connectivity between grid units may enhance their “gridness” within a limited strength range, but the overall effect of the irregular geometry is to disable long-range and obstruct short-range order. What appears as a smooth continuous attractor in a flat box, kept rigid by recurrent connections, turns into an incoherent motley of unit clusters, flexible or outright unstable.

Review and Applications of Machine Learning and Artificial Intelligence in Engineering

2020 ◽  
pp. 1-12
Author(s):  
Melda Yucel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Development Process ◽  
Current Trend ◽  
Engineering Applications ◽  
The Past ◽  
Applications Of Machine Learning ◽  
Basic Features

This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.

Software Fault Prediction Using Deep Learning Algorithms

2019 ◽  
Vol 10 (4) ◽  
pp. 1-19
Author(s):  
Osama Al Qasem ◽  
Mohammed Akour
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Detection Rate ◽  
Development Process ◽  
Learning Algorithms ◽  
Fault Prediction ◽  
Software Faults ◽  
Software Fault Prediction ◽  
Development Lifecycle ◽  
Software Fault

Software faults prediction (SFP) processes can be used for detecting faulty constructs at early stages of the development lifecycle, in addition to its being used in several phases of the development process. Machine learning (ML) is widely used in this area. One of the most promising subsets from ML is deep learning that achieves remarkable performance in various areas. Two deep learning algorithms are used in this paper, the Multi-layer perceptrons (MLPs) and Convolutional Neural Network (CNN). In order to evaluate the studied algorithms, four commonly used datasets from NASA are used i.e. (PC1, KC1, KC2 and CM1). The experiment results show how the CNN algorithm achieves prediction superiority of the MLP algorithm. The accuracy and detection rate measurements when using CNN has reached the standard ratio respectively as follows: PC1 97.7% - 73.9%, KC1 100% - 100%, KC2 99.3% - 99.2% and CM1 97.3% - 82.3%. This study provides promising results in using the deep learning for software fault prediction research.

scholarly journals From grid cells to place cells with realistic field sizes

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181618 ◽  
Author(s):  
Torsten Neher ◽  
Amir Hossein Azizi ◽  
Sen Cheng
Keyword(s):  
Place Cells ◽  
Grid Cells
Sign in / Sign up

Export Citation Format

Share Document