DOI: 10.5061/dryad.8cz8w9gnj
title: Entorhinal-retrosplenial circuits for allocentric-egocentric transformation of boundary coding

description: Spatial navigation requires landmark coding from two perspectives, relying
 on viewpoint-invariant and self-referenced representations. The brain
 encodes information within each reference frame, but their interactions
 and functional dependency remains unclear. Here we investigate the
 relationship between neurons in rat retrosplenial cortex (RSC) and
 entorhinal cortex (MEC) that increase firing near boundaries of space.
 Border cells in RSC specifically encode walls, but not objects, and are
 sensitive to the animal’s direction to nearby borders. These egocentric
 representations are generated independent of visual or whisker sensation,
 but depend on inputs from MEC that contains allocentric spatial cells.
 Pharmaco- and optogenetic inhibition of MEC cells led to a disruption of
 border coding in RSC, but not vice versa, indicating
 allocentric-to-egocentric transformation. Finally, RSC border cells fire
 prospective to the animal’s next motion, unlike those in MEC, revealing
 the MEC-RSC pathway as an extended border coding circuit that implements
 coordinate transformation to guide navigation behavior.
description: For tetrode recordings, rats were unilaterally implanted with a
 hyperdrive that contained 28 individually adjustable tetrodes made from
 17-μm polyimide-coated platinum-iridium (90-10%; California Fine Wire;
 plated with gold to impedances below 150 kΩ at 1 kHz). The tetrode bundle
 consisted of 30-gauge stainless steel cannulae, soldered together in a
 14x2 rectangular shape for recordings of the entire RSC, 7x4 for anterior
 RSC. Tetrodes were implanted alongside the anteroposterior axis, starting
 at (AP) -2.5 mm posterior from bregma until -4 mm to -6.5 mm, (ML) 0.8 mm
 lateral from the midline, (DV) 1.0 mm below the dura, and at a 25° angle
 in a coronal plane pointing to the midline in order the get underneath the
 superior sagittal sinus. For animals with id #50, #97, #167 and #224, the
 electrode was implanted in the right hemisphere, while for animals
 #246-#294 it was implanted in the left hemisphere. All
 data processing steps were performed in MatLab (MathWorks). Neural signals
 were acquired and amplified using two 64-channel RHD2164 headstages (Intan
 technologies), combined with an OpenEphys acquisition system, sampling
 data at 15 kHz. Neuronal spikes were detected by passing a digitally
 band-pass filtered LFP (0.6-6 kHz) through the 'Kilosort'
 algorithm to isolate individual spikes and assign them to separate
 clusters based on waveform properties
 (https://github.com/cortex-lab/KiloSort). Clusters were manually checked
 and adjusted in autocorrelograms and for waveform characteristics in
 principal component space to obtain well-isolated single units, discarding
 any multi-unit or noise clusters. Tetrodes were moved a minimum distance
 of 80 µm between recording days to find a new set of neurons for the next
 recording session.
description: We refer the reader to the associated paper for details on
 behavioural data acquisition, as well as a detailed account on how data
 vectors were generated. These data files include the following data
 variables for all individual neurons: <strong>Animal
 ID:</strong> animal identifier that corresponds with those mentioned
 in figure legends of the paper. <strong>Session
 ID:</strong> session identifier that corresponds to the recording
 day number. 
 <strong>EMD scores:</strong> Earth Mover's Distance
 (EMD), the main metric for border cell classification. <strong>Global
 FR:</strong> Overall spiking rate in spikes/sec, calculated as the
 total number of spikes, divided by the duration of a recording
 session. 
 <strong>Spatial rate map:</strong> Firing rate map in 2D
 space. 
 <strong>Boundary rate maps:</strong> Firing rate map
 in egocentric border space, with bins corresponding to the angle (3rd
 dimension) and distance (4th dimension) of a boundary relative to the
 animal's position. <strong>Spatial
 correlations:</strong> Pearson's correlation between the
 spatial rate maps of the first and last regular recording session on a
 given day. 
 <strong>Allocentric MVL:</strong> Mean Vector Length (MVL)
 values in allocentric head-direction space. <strong>Allocentric MVL
 stat:</strong> MVL statistic, corresponding to the percentile of the
 MVL relative to a 1000-fold time-shuffled null-distribution.
 <strong>Egocentric
 MVL:</strong> MVL values of angles in egocentric boundary
 space. 
 <strong>Egocentric MVL stat:</strong> MVL
 statistic, corresponding to the percentile of the MVL relative to a
 1000-fold time-shuffled null-distribution.