An Energy Model of Place Cell Network in Three Dimensional Space

• Published in: Frontiers in neuroscience Vol. 12; p. 264
• Main Authors: Wang, Yihong, Xu, Xuying, Wang, Rubin
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 25.04.2018; Frontiers Media S.A
• Subjects: Animal cognition; Computer simulation; Energy; energy coding; Kalman filters; locating system; Metric system; Neural coding; Neural networks; Neurons; Neuroscience; place cells; Science; spatial selectivity; three dimensional space; China; spatial selectivity; locating system; energy coding; place cells; three dimensional space
• ISSN: 1662-4548; 1662-453X; 1662-453X
• DOI: 10.3389/fnins.2018.00264

Place cells are important elements in the spatial representation system of the brain. A considerable amount of experimental data and classical models are achieved in this area. However, an important question has not been addressed, which is how the three dimensional space is represented by the place cells. This question is preliminarily surveyed by energy coding method in this research. Energy coding method argues that neural information can be expressed by neural energy and it is convenient to model and compute for neural systems due to the global and linearly addable properties of neural energy. Nevertheless, the models of functional neural networks based on energy coding method have not been established. In this work, we construct a place cell network model to represent three dimensional space on an energy level. Then we define the place field and place field center and test the locating performance in three dimensional space. The results imply that the model successfully simulates the basic properties of place cells. The individual place cell obtains unique spatial selectivity. The place fields in three dimensional space vary in size and energy consumption. Furthermore, the locating error is limited to a certain level and the simulated place field agrees to the experimental results. In conclusion, this is an effective model to represent three dimensional space by energy method. The research verifies the energy efficiency principle of the brain during the neural coding for three dimensional spatial information. It is the first step to complete the three dimensional spatial representing system of the brain, and helps us further understand how the energy efficiency principle directs the locating, navigating, and path planning function of the brain.