Heading-vector navigation based on head-direction cells and path integration

Hippocampus ◽  
2009 ◽  
Vol 19 (5) ◽  
pp. 456-479 ◽  
Author(s):  
John L. Kubie ◽  
André A. Fenton
Keyword(s):  
Path Integration ◽  
Head Direction ◽  
Head Direction Cells ◽  
Vector Navigation

Anticipatory Time Intervals of Head-Direction Cells in the Anterior Thalamus of the Rat: Implications for Path Integration in the Head-Direction Circuit

1997 ◽  
Vol 78 (1) ◽  
pp. 145-159 ◽  
Author(s):  
Hugh T. Blair ◽  
Brian W. Lipscomb ◽  
Patricia E. Sharp
Keyword(s):  
Firing Rate ◽  
Path Integration ◽  
Time Interval ◽  
Head Direction ◽  
Time Intervals ◽  
Head Direction Cells ◽  
Directional Tuning ◽  
Anterior Thalamus ◽  
The Future ◽  
Tuning Function

Blair, Hugh T., Brian W. Lipscomb, and Patricia E. Sharp. Anticipatory time intervals of head-direction cells in the anterior thalamus of the rat: implications for path integration in the head-direction circuit. J. Neurophysiol. 78: 145–159, 1997. Head-direction cells are neurons that signal a rat's directional heading in the horizontal plane. Head-direction cells in the anterior thalamus are anticipatory, so that their firing rate is better correlated with the rat's future head direction than with the present or past head direction. We recorded single-unit activity from head-direction cells in the anterior thalamus of freely moving rats. We measured the time interval by which each individual cell anticipated the rat's future head direction, which we refer to as the cell's anticipatory time interval (ATI). Head-direction cells in the anterior thalamus anticipated the rat's future head direction by an average ATI of ∼17 ms. However, different anterior thalamic cells consistently anticipated the future head direction by different ATIs ranging between 0 and 50 ms. We found that the ATI of an anterior thalamic head-direction cell was correlated with several parameters of the cell's directional tuning function. First, cells with long ATIs sometimes appeared to have two peaks in their directional tuning function, whereas cells with short ATIs always had only one peak. Second, the ATI of a cell was negatively correlated with the cell's peak firing rate, so that cells with longer ATIs fired at a slower rate than cells with shorter ATIs. Third, a cell's ATI was correlated with the width of its directional tuning function, so that cells with longer ATIs had broader tuning widths than cells with shorter ATIs. These relationships between a cell's ATI and its directional tuning parameters could not be accounted for by artifactual broadening of the tuning function, which occurs for cells that fire in correlation with the future (rather than present) head direction. We found that when the rat's head is turning, the shape of an anterior thalamic head-direction cell's tuning function changes in a systematic way, becoming taller, narrower, and skewed. This systematic change in the shape of the tuning function may be what causes anterior thalamic cells to effectively anticipate the rat's future head direction. We propose a neural circuit mechanism to account for the firing behavior we have observed in our experiments, and we discuss how this circuit might serve as a functional component of a neural system for path integration of the rat's directional heading.

scholarly journals Middle-scale navigation: the vertebrate case

1996 ◽  
Vol 199 (1) ◽  
pp. 163-164
Author(s):  
DF Sherry
Keyword(s):  
Path Integration ◽  
Brain Regions ◽  
Place Cells ◽  
Cognitive Map ◽  
Head Direction ◽  
Neural Basis ◽  
Head Direction Cells ◽  
Complete Rejection ◽  
Neural Bases

Few ideas have had a greater impact on the study of navigation at the middle scale than the theory of the cognitive map. As papers in this section show, current views of the cognitive map range from complete rejection of the idea (Bennett, 1996) to new proposals for the behavioural and neural bases of the cognitive map (Gallistel and Cramer, 1996; McNaughton et al. 1996). The papers in this section also make it clear that path integration has taken centre stage in theorizing about navigation at the middle scale. Path integration is the use of information generated by locomotion to determine the current distance and direction to the origin of the path. Etienne (1980) provided one of the first experimental demonstrations of path integration by a vertebrate, and in this section Etienne et al. (1996) describe recent research with animals and humans on the interaction between path integration and landmark information. Path integration is also the fundamental means of navigation in the model described by Gallistel and Cramer (1996). McNaughton et al. (1996) suggest that the neural basis of path integration is found in the place cells and head direction cells of the hippocampus and associated brain regions.

scholarly journals Head-Direction drift in rat pups is consistent with an angular path-integration process

2017 ◽  
Author(s):  
Gilad Tocker ◽  
Eli Borodach ◽  
Tale L. Bjerknes ◽  
May-Britt Moser ◽  
Edvard I. Moser ◽  
...  
Keyword(s):  
Random Walk ◽  
Path Integration ◽  
Open Loop ◽  
Head Direction ◽  
Integration Process ◽  
Neural Basis ◽  
Head Direction Cells ◽  
Rat Pups ◽  
Sense Of Direction ◽  
Eye Opening

SummaryThe sense of direction is a vital computation, whose neural basis is considered to be carried out by head-direction cells. One way to estimate head-direction is by integrating head angular-velocity over time. However, this process results in error accumulation resembling a random walk, proportional to , which constitutes a mark for a path integration process. In the present study we analyzed previously recorded data to quantify the drift in head-direction cells of rat pups before and after eye-opening. We found that in rat pups before eye-opening the drift propagated as a random walk, while in rats after eye-opening the drift was lower. This suggests that a path-integration process underlies the estimation of head-direction, such that before eye-opening the head-direction system runs in an open-loop manner and accumulates error. After eye-opening, visual-input, such as arena shape, helps to correct errors and thus compute the sense of direction accurately.

scholarly journals An Oscillatory Network Model of Head Direction, Spatially Periodic Cells and Place Cells Using Locomotor Inputs

2016 ◽  
Author(s):  
Karthik Soman ◽  
Vignesh Muralidharan ◽  
V. Srinivasa Chakravarthy
Keyword(s):  
Path Integration ◽  
Place Cells ◽  
Border Cells ◽  
Head Direction ◽  
Grid Cells ◽  
Head Direction Cells ◽  
Modeling Approach ◽  
Spatially Periodic ◽  
Locomotor Rhythms ◽  
Oscillatory Network

AbstractWe propose a computational modeling approach that explains the formation of a range of spatial cells like head direction cells, grid cells, border cells and place cells which are believed to play a pivotal role in the spatial navigation of an animal. Most existing models insert special symmetry conditions in the models in order to obtain such symmetries in the outcome; our models do not require such symmetry assumptions. Our modeling approach is embodied in two models: a simple one (Model #1) and a more detailed version (Model #2). In Model #1, velocity input is presented to a layer of Head Direction cells, with no special topology requirements, the outputs of which are presented to a layer of Path Integration neurons. A variety of spatially periodic responses resembling grid cells, are obtained using the Principal Components of Path Integration layer. In Model #2, the input consists of the locomotor rhythms from the four legs of a virtual animal. These rhythms are integrated into the phases of a layer of oscillatory neurons, whose outputs drive a layer of Head Direction cells. The Head Direction cells in turn drive a layer of Path Integration neurons, which in turn project to two successive layers of Lateral Anti Hebbian Networks (LAHN). Cells in the first LAHN resemble grid cells (with both hexagonal and square gridness), and border cells. Cells in the second LAHN exhibit place cell behaviour and a new cell type known as corner cell. Both grid cells and place cells exhibit phase precession in 1D and 2D spaces. The models outline the neural hierarchy necessary to obtain the complete range of spatial cell responses found in the hippocampal system.

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