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Intelligent Design in Animal Self-Location and Navigation

Eric Cassell
Photo: Zebrafish, by Oregon State University, CC BY-SA 2.0 , via Wikimedia Commons.

While much has been learned about animal navigation methods (see my book Animal Algorithms), not as much is known about how different animals actually determine a reference to the location of “self,” and how they use that information to navigate. It has been known for some time that mammalian brains include basic mechanisms for locating self. These include neurons that are so-called “place cells,” “grid cells,” and head-direction cells.1

Primarily in the Hippocampus

In mammals the self-locating neuron networks are found primarily in the hippocampus. It is theorized that these networks provide support for the ability of animals to form cognitive maps. The initial studies in mammals focused on rats and mice, and identified primarily “static” two-dimensional self-locating mechanisms. More recent studies have been conducted with bats during flight. In that case the self-location is three-dimensional in space. Even more intriguing is that the bat mechanism can be applied on a time continuum, representing past, present, and future. The authors of one study conclude, “These results reveal a positional representation in flying bats that extends along a continuum of space and time and could support a representation of remembered paths.”2 The mechanism may also be the source of a predictive map used in navigating flight paths.

An open question is whether such mechanisms exist in more ancient brain regions of other animals. A new study has identified a self-location mechanism in zebrafish.3 The study found a self-location mechanism in the fish hindbrain, which is the region that controls coordinated physical movements (action patterns) associated with orienting, feeding, and escape mechanisms. The specific function identified in the zebrafish is to enable positional homeostasis, which is a challenge since fish typically have to deal with currents in maintaining a constant position. Fish (as well as some other animals) are able to estimate velocity based on optical flow, which is the rate at which visual objects appear to move. Mathematically, position can be obtained by integrating velocity over time. 

Analogous to an Electronic Circuit

One finding from the study is that, “Fish integrate visual flow into a representation of location change and correct for unintended location changes.” There are a number of other significant findings from the Yang study. One is that it is a complex distributed neural network within the brain, meaning it is not restricted to a small number of proximate neurons. The authors also describe this as a “circuit,” analogous to an electronic circuit. The network represents a classical closed loop engineering control system, where feedback is used to adjust and maintain a position. Another finding is that fish have the ability to store locations in memory for 15 to 20 seconds.

Taking a step back and assessing the significance of these recent findings, several observations can be made. One is that they provide more evidence that animal movement and navigation behaviors involve complex algorithms. Some include methods for performing or mimicking mathematical calculations. The algorithms appear to involve complex neural networks or circuits. All of these observations provide more evidence for the engineering design of these behaviors.


  1. “Spatial representation in the hippocampal formation: a history,” Moser et al., Nature Neuroscience, Vol. 20, 1448-1464, November 2017.
  2. “Nonlocal spatiotemporal representation in the hippocampus of freely flying bats,” Nicholas M. Dotson and Michael M. Yartsev, Science, 373, 242-247, July 9, 2021.
  3. “A brainstem integrator for self-positional homeostasis in zebrafish,” Yang et al., Cell 185, 5011-5027, December 22, 2022.