Passive Transport Disrupts Grid Signals in the Parahippocampal Cortex

Abstract

Navigation is usually thought of relative to landmarks, but neural signals representing space also use information generated by an animal's movements. These signals include grid cells, which fire at multiple locations, forming a repeating grid pattern. Grid cell generation depends upon theta rhythm, a 6-10 Hz electroencephalogram (EEG) oscillation that is modulated by the animals' movement velocity. We passively moved rats in a clear cart to eliminate motor related self-movement cues that drive moment-to-moment changes in theta rhythmicity. We found that passive movement maintained theta power and frequency at levels equivalent to low active movement velocity, spared overall head-direction (HD) cell characteristics, but abolished both velocity modulation of theta rhythmicity and grid cell firing patterns. These results indicate that self-movement motor cues are necessary for generating grid-specific firing patterns, possibly by driving velocity modulation of theta rhythmicity, which may be used as a speed signal to generate the repeating pattern of grid cells.

Keywords: grid cell; head-direction cell; passive transport; self-movement cues; theta rhythm.

Fig. 1

(A) Representative grid cell response to Active 1 (left column), Passive (middle column), and Active 2 (right column) sessions. Row 1: rat path and individual spikes (red dots). Row 2: smoothed firing rate map. Row 3: autocorrelation map. Row 4: polar plot of firing rate by direction. M = mean firing rate in spikes/s, P = peak firing rate in spikes/s, Grid = grid score, r = mean vector length. (B) Data for grid score (top left), mean firing rate (top right), within-session location stability (middle left), smoothed rate map cross correlation (middle right), mean vector length (bottom left), and a histogram showing the change in mean vector length between Active 1 and Passive sessions (bottom right). Data are represented as mean ± SEM. Asterisks indicate difference from Active 1 and Active 2. *** = p < 0.001. See also Fig. S4 and S5.

Fig. 2

Representative grid cell responses to Active 1, Passive, and Active 2 sessions as in Fig. 1.

Fig. 3

(A) Representative HD cell responses to Active 1, Passive, and Active 2 sessions. Columns are the same as in Fig. 1. Each row is a different cell’s polar plot of firing rate by direction. r = mean vector length. P = peak firing rate in spikes/s. (B) Data for mean vector length (top left), within-session directional stability (top right), mean firing rate (bottom left), and directional firing cross correlation (bottom right). Data are represented as mean ± SEM. Asterisks indicate difference from Active 1 and Active 2 unless otherwise indicated. * = p < 0.05; *** = p < 0.001.

Fig. 4

(A) Representative conjunctive grid × HD cell response to Active 1, Passive, and Active 2 sessions. Rows, columns, and abbreviations are the same from Fig. 1. (B) Data for grid score (top left), mean firing rate (top right), mean vector length (bottom left), and within-session directional stability (bottom right). Data are represented as mean ± SEM. Asterisks indicate difference from Active 1 and Active 2. * = p < 0.05.

Fig. 5

(A) Local field potential displaying theta rhythmicity from Active 1 (left), Passive (middle), and Active 2 (right) sessions. (B) Data for theta ratio across sessions (left) and as a function of linear velocity (right). (C) Data for frequency across sessions (left) and as a function of linear velocity (right). (D) Data for mean firing rate of grid cell (left) and HD cell (right) activity as a function of linear velocity. Data are represented as mean ± SEM. Asterisks indicate difference from Active 1 and Active 2. ** = p < 0.01, *** = p < 0.001.