MAIN TEXT
Reliable and adaptable navigational capabilities are essential for nearly all animal species. Animals often must take complicated paths through their environments and move at a wide range of speeds. Despite this, most species are remarkably successful at navigating complex environments while simultaneously perceiving sensory stimuli that might alert them to rewards or predators. Contemplating how animals might possess these impressive navigational abilities, Darwin suggested a strategy he termed “dead reckoning”. The theory proposed that by combining internal and external motion cues to continuously estimate speed and direction, animals could adequately track their current position relative to a starting point (Darwin, 1873; Barlow 1964). Dead reckoning is now commonly referred to as path integration and has taken on a somewhat more restricted definition, focused primarily on the use of internally generated (idiothetic) neural signals (Whishaw et al., 2001; Whishaw and Wallace, 2003; Etienne and Jeffery, 2004; Buzsáki 2005; McNaughton et al., 2006; Buzsáki and Moser, 2013; Chrastil 2013; Geva-Sagiv et al., 2015; Finkelstein et al., 2016; Igarashi 2016; Grieves and Jeffery, 2017; Moser et al., 2017). Mammals were first confirmed to utilize path integration in navigation nearly forty years ago (Mittelstaedt and Mittelstaedt, 1980), and multiple brain regions have since been implicated in this function (McNaughton et al., 1996; 2006; Whishaw et al., 1997; 2001; Whishaw and Wallace, 2003; Etienne and Jeffery, 2004; Parron and Save, 2004; Nitz 2006; Wolbers et al., 2007; Moser et al., 2008; 2017; Whitlock et al., 2012; Wilber et al., 2017).
How do neurons computationally represent direction and speed, as required by path integration theories? In rodents, Taube and colleagues have found head direction cells: assemblies of neurons, residing in many navigationally important regions. These neurons integrate vestibular, proprioceptive and other meaningful input to fire only when the animal’s head points in a preferred orientation (Taube et al., 1990a; b; Stackman et al., 2002; Peyrache et al., 2015). A number of reviews cover head direction in exquisite detail (Sharp et al., 2001; Taube 2007; Yoder and Taube, 2014; Grieves and Jeffery, 2017; Moser et al., 2017; Campbell and Giocomo, 2018), and here we will focus on the neural representation and control of linear running speed. Neural activity patterns associated with locomotion have been studied in a variety of mammals and brain regions for decades (e.g., Green and Arduini, 1954), yielding a multitude of observations that can sometimes be difficult to reconcile. The present review aims to synthesize these wide-ranging findings with the goal of providing a clearer understanding of the mechanisms underlying mammalian speed encoding. We also highlight some of the critical questions that still need to be answered to paint a comprehensive picture of how neural codes for running speed enable successful spatial navigation.
Running speed plays a central role in broader theories of spatial cognition. The known circuitry of the brain’s so-called ‘cognitive map’ is formed most prominently by two cell types: hippocampal place cells and entorhinal grid cells. Place cells are pyramidal cells in areas CA1 and CA3 of the hippocampus that selectively fire in one (or sometimes two) locations within a given environment (O’Keefe and Dostrovsky, 1971; Wilson and McNaughton, 1993; Moser et al., 2008; Grieves and Jeffery, 2017). Medial entorhinal cortical (MEC) grid cells (stellate and pyramidal cells in layers 2 & 3) fire in a similar but repeating manner such that their firing fields produce a tessellating geometric grid over a given environment (Fyhn et al., 2004; Hafting et al., 2005; Moser et al., 2008; Grieves and Jeffery, 2017). For spatially invariant representations to be continuously updated in a manner consistent with the subject’s movement, the place cell-grid cell network must have access to speed information among other self-motion metrics (Moser et al., 2008; 2017; McNaughton et al., 2006). We begin the present review with a discussion of how speed information appears to be encoded in these two structures before shifting to an examination of the upstream circuitry and computations that may provide this network with speed-modulated inputs.