Locating and navigation mechanism based on place-cell and grid-cell models

Abstract

Extensive experiments on rats have shown that environmental cues play an important role in goal locating and navigation. Major studies about locating and navigation are carried out based only on place cells. Nevertheless, it is known that navigation may also rely on grid cells. Therefore, we model locating and navigation based on both, thus developing a novel grid-cell model, from which firing fields of grid cells can be obtained. We found a continuous-time dynamic system to describe learning and direction selection. In our simulation experiment, according to the results from physiology experiments, we successfully rebuild place fields of place cells and firing fields of grid cells. We analyzed the factors affecting the locating accuracy. Results show that the learning rate, firing threshold and cell number can influence the outcomes from various tasks. We used our system model to perform a goal navigation task and showed that paths that are changed for every run in one experiment converged to a stable one after several runs.

Drugs Interfering with Muscarinic Acetylcholine Receptors and Their Effects on Place Navigation

“…The spatial pattern of their firing fields resembles a hexagonal grid. And the final type is represented by head direction cells, found in the Papez circuit, and whose activity is dependent on the inclination or direction of an individual’s head ( 49 , 53 , 58 – 60 ). The specific roles and mechanisms of function of these cells are not yet fully understood.…”

“…The specific roles and mechanisms of function of these cells are not yet fully understood. A recent study has proposed a model for spatial navigation based on cooperation between place cells and grid cells, in which place cells are responsible mainly for locating a goal, whereas grid cells are in charge of directing an individual toward that goal ( 60 ).…”

The Energy Coding of a Structural Neural Network Based on the Hodgkin–Huxley Model

“…And the collaboration of sodium channels and potassium channels contributes substantially to energy-efficient metabolism by minimizing the overlap of their respective ion fluxes (Alle et al, 2009 ). Owing to these operations, a novel neural coding theory, termed as neural energy coding, has been proposed in the field of neuroinformatics in recent years (Wang et al, 2006 , 2008 , 2016 , 2017 ; Wang and Wang, 2014 ; Wang R. et al, 2014 ; Wang Z. et al, 2014 ; Yan et al, 2016 ). This coding theory states that membrane potential of the neuron uniquely corresponds to its consumed neural energy, which enables us to transform the complex and highly nonlinear spiking pattern of membrane potentials into the distribution pattern of energy to deal with neural activity (Abbott, 2008 ; Wang et al, 2008 ; Wang and Wang, 2014 ; Wang R. et al, 2014 ; Wang Z. et al, 2014 ).…”

“…Thus, the neural information can be expressed as energy at the level of molecule, neuron, network, cognition, behavior, and their combination such that it can unify the neural models among various levels. (2) The neural energy can be combined with spiking pattern of membrane potentials to resolve the neural information (Wang et al, 2006 , 2008 , 2016 , 2017 ; Wang and Wang, 2014 ; Wang R. et al, 2014 ; Wang Z. et al, 2014 ; Kozma, 2016 ; Yan et al, 2016 ; Zheng et al, 2016 ). (3) Neural energy can describe the interaction of large-scale neurons referring to the interaction of multiple brain regions that cannot be achieved by any conventional coding theory (Wang et al, 2009 ; Vuksanović and Hövel, 2016 ; Zhang et al, 2016 ; Déli et al, 2017 ; Peters et al, 2017 ).…”

“…Nevertheless, neural energy resolves this issue (Wang and Wang, 2017 ). (5) Whether the neural model is based on a single neuron, neural populations, networks or behaviors and is linear or nonlinear, their dynamic response can describe the pattern of neural coding by energy superposition owing to the scalar property of energy (Wang et al, 2006 , 2008 , 2016 , 2017 ; Wang and Wang, 2014 ; Wang R. et al, 2014 ; Wang Z. et al, 2014 ; Yan et al, 2016 ; Zheng et al, 2016 ). Thus, the global information of functional brain activity can be acquired, which is not achieved by the other traditional coding theories.…”