Ultimate Source of the Neural Speed Signal: Motor or Sensory or Both?
The motor circuitry directly upstream of the MLR (Garcia-Rill, 1986)
seems to contain robust speed coding as well (Fig. 2A). Rate codes for
speed have been reported in the motor cortex (Leinweber et al., 2017),
striatum (Kim et al., 2014) and substantia nigra pars compacta (Barter
et al., 2015), whereas theta and gamma oscillatory temporal codes have
been reported in both motor cortex (von Nicolai et al., 2014) and
striatum (Masimore et al., 2005; von Nicolai et al., 2014; but see Lalla
et al., 2017). Additionally, optogenetic activation of striatal
populations encoding speed modulates downstream MLR signaling (Roseberry
et al., 2016) as well as locomotion (Bartholomew et al., 2016; Roseberry
et al., 2016) (Fig. 1C). Taken together, this evidence suggests that
internally-generated motor commands give rise to the speed signals
utilized in the hippocampus and MEC for spatial representation
maintenance through an efference copy-like mechanism. Corroborating this
hypothesis, removing motor and/or proprioceptive cues by passively
moving an animal in a clear cart around an already-run track diminishes
speed modulations of grid cell firing rate as well as MEC and
hippocampal theta power and frequency (Terrazas et al., 2005; Winter et
al., 2015). Moreover, motor cues might also be sufficient to some degree
for the maintenance of theoretically speed-dependent internal spatial
representations, as animals running on a running wheel (i.e., without a
true ‘environment’ to navigate) have been reported to still show
hippocampal place sequences.
However, the original premise of dead reckoning maintains two possible
sources from which speed information can be derived: externally- or
internally-generated cues. While efference copies might represent the
latter, the former is most likely represented by sensory systems
encoding information such as changes in optic or tactile flow. Logic and
intuition thus demand that these types of informational streams should
be seriously examined as an alternative origin of hippocampal-entorhinal
speed signaling. Indeed, as discussed below, movement speed is
directly encoded in the sensory systems.
Optic flow speed seems to be encoded by LGN and primary visual cortical
cells (Roth et al., 2016; Saleem et al., 2013; Eriksen et al., 2014; but
see Niell and Stryker, 2010), while specialized cells exist in rodent
barrel cortex that encode the speed at which whiskers drag along the
ground (Chorev et al., 2016). A preliminary study has also reported the
presence of hippocampal-entorhinal-like “speed-responsive”
interneurons in the barrel cortex (Long and Zhang, 2018), inviting
further investigation of this possibility. Active functioning of sensory
systems seems necessary for speed signal generation as well, as complete
darkness has recently been shown to disrupt speed modulation of MEC
theta and grid cell activity in addition to other features of the grid
cell network (Chen et al., 2016). And while it remains less
well-investigated than the motor circuitry discussed in this review,
there also seems to be at least one possible circuit with consistently
reported speed encoding that might be able to transmit sensory-derived
speed information to the hippocampal-entorhinal complex: the visual
cortical areas project to posterior parietal cortex (Wilber et al.,
2017; Yang et al., 2017; Miller and Vogt, 1984), which projects to the
postrhinal complex (Furtak et al., 2012; Burwell and Amaral, 1998), and
onto the hippocampus and MEC (Burwell and Amaral, 1998; Agster and
Burwell, 2009) (Fig. 2B).
If both sensory- and motor-derived estimates of speed are indeed
required to eventually generate speed signaling in the
hippocampal-entorhinal complex, the two informational streams must at
some point interact and influence each other to give rise to a unified
speed signal. Evidence for a kind of comparison or reconciliation
process has already emerged in the early visual system: Studies
investigating responses to incongruent visual and running speed have
noted either mismatch-based (Keller et al., 2012; Roth et al., 2016) or
integrative responses (Saleem et al., 2013; Roth et al., 2016), with
implications for the downstream place cell network (Chen et al., 2013).
Despite these findings, a compelling argument can be made for a somewhat
deterministic influence of the motor system over sensory information in
speed signal generation. Locomotion influences general and
speed-specific sensory cortical processing through an
efference-copy-like mechanism (Niell and Stryker, 2010; Ayaz et al.,
2013; Eriksen et al., 2014; Schneider et al., 2014; Zhou et al., 2014;
Dadarlat and Stryker, 2017). The motor cortex and MLR have been
implicated in mediating these changes by acting through direct
innervation of sensory areas (Schneider et al., 2014; Leinweber et al.,
2017) and ascending basal forebrain projections (Pinto et al., 2013; Fu
et al., 2014; Lee et al., 2014), respectively. The MLR might in turn be
dependent upon the basal ganglia or other higher motor planning regions
to mediate these changes (Roseberry et al., 2016). Moreover, various
classes of units throughout the visual system are tuned to running speed
and remain so in the absence of visual input (Fu et al., 2014; Pakan et
al., 2016; Roth et al., 2016; Eriksen et al., 2014; Saleem et al., 2013;
Christensen and Pillow, 2017), while M2 axons in V1 have “predictive”
activity ramp-ups that precede locomotion initiation, lead similar
responses by V1 cells, and also scale with running speed (Leinweber et
al., 2017). A similar M2 projection to auditory cortex has been shown to
carry an efference copy that precedes locomotion and inhibits local
responses to auditory stimuli (Schneider et al., 2014; Fig 2). Lastly,
initial reports claim that layer V contains the highest share of
speed-tuned neurons in V1, whereas layer IV had the smallest, suggesting
that the visually-derived speed signal may derive more strongly from
other cortical inputs rather than from raw sensory inputs coming into
layer IV from the LGN (Christensen and Pillow, 2017). Together, this
evidence strongly suggests that an efference copy of the motor-derived
speed signal arrives in sensory cortices through multiple pathways
before a sensory-derived speed estimate can be made and thus influences that
sensory-based estimate.
It seems unlikely, however, that the motor system completely dominates
the sensory system’s speed signal determination; instead, the speed
signal that ends up in the hippocampal-entorhinal complex is probably
derived from some combination of the two sources. Recent findings have
begun to strongly support this more nuanced view. Predictive
motor-related signals from M2 can be modified after locomotion onset to
reflect visual flow or the expected changes in visual flow based on the
visual scene before locomotion onset (Leinweber et al., 2017).
Furthermore, the tuning of MEC speed cells is retained in the dark with
reduced specificity (Perez-Escobar et al., 2016) and can more faithfully
reflect either visual or locomotive inputs during bidirectional
manipulation of the gain between visual flow and running speed in a
virtual reality environment (Campbell et al., 2018). Lastly, in a recent
experiment examining MEC spatial encoding in the vertical plane, both
rate and temporal speed signals were altered, a finding the authors
attributed to a likely change in both the incoming sensory input and
efference copies (Casali et al., 2019). Further investigation is thus
required to fully elucidate the mechanisms by which sensory and motor
input combine to create a unified speed signal, while carefully tracking
the precise prospective coding latency in each relevant brain region.