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ARTICLE Doi:10.1038/Nature14622 ARTICLE doi:10.1038/nature14622 Speed cells in the medial entorhinal cortex Emilio Kropff1,2, James E. Carmichael1{, May-Britt Moser1 & Edvard I. Moser1 Grid cells in the medial entorhinal cortex have spatial firing fields that repeat periodically in a hexagonal pattern. When animals move, activity is translated between grid cells in accordance with the animal’s displacement in the environment. For this translation to occur, grid cells must have continuous access to information about instantaneous running speed. However, a powerful entorhinal speed signal has not been identified. Here we show that running speed is represented in the firing rate of a ubiquitous but functionally dedicated population of entorhinal neurons distinct from other cell populations of the local circuit, such as grid, head-direction and border cells. These ‘speed cells’ are characterized by a context-invariant positive, linear response to running speed, and share with grid cells a prospective bias of 50–80 ms. Our observations point to speed cells as a key component of the dynamic representation of self-location in the medial entorhinal cortex. Grid cells in the medial entorhinal cortex (MEC) are unique in their Experimental control of running speed spatial code1,2. Unlike other place-modulated neurons, their population We began by recording neuronal activity under strict experimental firing pattern not only repeats periodically within a given envir- control of the animal’s running speed. Rats traversed a 4 m long linear onment1, but also seems to apply equally to all explored environments3, track with their body confined inside a computer-driven bottomless reflecting the uniformity of space despite the unevenness of contextual frame that was moved along the track at a pre-set speed (an experi- details. This property makes grid cells ideal candidates for a path integ- mental car similar in concept to a ‘Flintstones’ car’; Fig. 1a). Since the ration-based representation of space1,4–11. In such a scheme, running car had no floor, it compelled the animal to engage in natural loco- speed is integrated across short time windows to obtain the instant- motion at the experimenter-determined speed in order to reach the aneous displacement of the animal, which, in conjunction with head- end of the track, where a food reward was delivered. During running, direction input, is used to update the representation of the animal’s cells were recorded across all layers of the MEC (Extended Data position. Any path integration mechanism thus requires running speed Fig. 1). as a major input. However, while speed correlates marginally with In initial experiments, we either trained rats to run fast on one half entorhinal theta frequency12 and the firing rate of some grid cells13,14, of the track and slow on the other, with a sharp transition in the the existence and nature of a reliable and locally available speed signal middle (382 cells, three rats), or speed was increased proportionally has remained unclear. The aim of the present study was to determine to the distance from one of the track ends (282 cells, two rats). While whether speed is represented in separate classes of MEC cells. spatial maps were not disrupted (Extended Data Fig. 2), the firing rate Figure 1 | Speed-responsive MEC cells in a linear a 4 m d Mesh 0.18 task. a, Bottomless car. b, Mean firing rate (green) 200 and running speed (grey) as a function of Obs. position for three representative speed cells in the Shuff. MEC. Left and middle, linear speed protocol; 100 right, step protocol. Pearson correlations between Car Cell count Parking instantaneous running speed and firing rate are sensor Motor indicated. c, Representative speed cells during 0 decelerating and accelerating events of the step –0.4 0 0.4 b Firing rate (Hz) Running speed (cm s–1) protocol (left and right subpanels, respectively). Speed (cm s Speed score 0.72 0.67 40 0.61 Top, firing rate (red, left axis) and running speed 40 10 5 e 50 20 (grey, right axis) as a function of time relative to the event onset. Bottom, spike raster plots. –1 5 d, Distributions of observed speed scores (purple ) Firing rate (Hz) 0 0 0 0 0 0 042 042 042 curve, ‘Obs.’) and 100 shuffles per cell (grey bars, c Position in track (m) Position in track (m) Position in track (m) Speed (cm s ‘Shuff.’; counts normalized by number of 10 50 100 50 Firing rate (Hz) shuffles) for all cells in the four-speed experiment. 0 Dashed line shows the 99th percentile of the rate 7142128 shuffled distribution (0.18). e, Tuning curves of five Speed group (cm s–1) speed –1 ) representative speed cells in d. f, Normalized Firing rate (Hz) 0 0 0 0 f 0.5 average firing rates (see Methods) for all 98 speed 10 10 cells in all four speed groups (means 6 s.e.m.). Run no. firing rate 0 0 Normalized 0 –2 0 2 –2 0 2 –2 0 2 –2 0 2 7142128 Time relative to acceleration onset (s) Time relative to acceleration onset (s) Speed group (cm s–1) 1Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Norwegian University of Science and Technology, Olav Kyrres gate 9, MTFS, 7491 Trondheim, Norway. 2Leloir Institute, IIBBA - CONICET, Buenos Aires, C1405BWE, Argentina. {Present address: Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire 03755, USA. 23 JULY 2015 | VOL 523 | NATURE | 419 G2015 Macmillan Publishers Limited. All rights reserved RESEARCH ARTICLE of some cells recorded in these protocols followed the speed profile curve using 2-cm-s21 bins from 0 to 50 cm s21. Again, many cells had (Fig. 1b), with fast transitions in firing rate at each change in running firing rates that increased linearly with speed (Fig. 2a and Extended speed (Fig. 1c). Data Figs 4a and 5). Instantaneous firing rate and running speed In order to disentangle running speed from the position of the exhibited considerable co-variation (Fig. 2b). In behaviourally animal, the same track segments were traversed, in separate experi- unfiltered data, as many as 51% of the neurons had a speed score that ments, at constant running speeds that alternated randomly from run passed the classification threshold determined by the 99th percentile to run between 7, 14, 21 and 28 cm s21. The majority of data in the of a distribution of shuffled data (Fig. 2c). This large proportion might bottomless car task (754 cells from ten rats) were collected with this reflect a difference in the network state between rest and active nav- four-speed protocol. To identify speed-responsive neurons, we calcu- igation rather than a genuine correlation between running speed and lated a speed score for each cell, defined as the Pearson product- firing rate, a problem that was not present in the bottomless car, where moment correlation between instantaneous firing rate and running resting periods were left out of the analysis. To overcome this issue, speed, on a scale from 21 to 1. Cells with speed scores higher than we redefined the speed score by filtering out static periods (speed the 99th percentile of a shuffled distribution (a value of 0.18) were ,2cms21). With this stricter criterion, used in all subsequent classified as speed cells. A total of 98 MEC cells (13%) passed this open-field analyses, the threshold defined by the 99th percentile of criterion (Fig. 1d, e), significantly more than expected by chance the shuffled distribution (0.18) was passed by 385 neurons, or 15% of (expected, 7.5 cells; P 5 10274). As a population, these cells showed all MEC cells (Fig. 2d and Extended Data Fig. 6f)—a percentage significant differences in normalized firing rate between all four almost identical to the estimate from the bottomless car. Slopes and blocks of constant running speed (Fig. 1f; Kruskal–Wallis and y intercepts of regression lines for firing rate as a function of speed Tukey–Kramer tests, P , 0.01). The slope and y intercept of linear were as dispersed as in the car (Extended Data Fig. 3a). Cells with regression lines for firing rate as a function of speed were highly similar properties were found in the hippocampus (Fig. 2e and dispersed across cells, with frequent cases of non-zero firing at very Extended Data Fig. 7b). Out of 964 hippocampal neurons that were low speed (Extended Data Fig. 3a; see examples in Fig. 1b, c, e). active in the open field, 96 cells (10%) passed the threshold deter- Negative, linear responses to speed were also observed, although only mined by the 99th percentile of the shuffled distribution (0.19) in a marginal sub-population of 16 cells (2%) that was not analysed (Fig. 2f). further (Extended Data Fig. 3b–d). Once we had established that speed modulation is similar in the open field and the bottomless car, we could investigate whether MEC Speed cells during free foraging speed cells form a population of their own. We compared their prop- Analyses in the bottomless car do not address directly the question of erties with those of grid, head-direction and border cells, classified how much overlap there is between the speed-cell population and respectively by their gridness score13,15, mean vector length16, and other entorhinal cell types, which have two-dimensional firing pat- border score17,18, with thresholds obtained by shuffling of spike times terns. Thus, we performed classical free-foraging recordings in a 1 m (Extended Data Fig. 6a). Out of 2,497 cells, 518 (21%) were classified wide square box, where 17 rats covered a wide range of instantaneous as grid cells, 398 (16%) as head-direction cells, and 99 (4%) as border speeds, typically from 0 to 50 cm s21.
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