Locomotor and Hippocampal Processing Converge in the Lateral Septum

Article

Locomotor and Hippocampal Processing Converge in the Lateral Septum Highlights

Authors

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Lateral septum (LS) firing is correlated with locomotor speed and acceleration

Hannah S. Wirtshafter, Matthew A. Wilson

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LS neurons modulate their firing during cue and reward throughout a conditioning task

Correspondence

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Cue and/or reward cells are associated with HPC activity and movement cells are not

In Brief

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LS movement information and HPC-LS coordination may be used downstream by the VTA

Wirtshafter & Wilson, 2019, Current Biology 29, 1–16 October 7, 2019 ª 2019 Elsevier Ltd. https://doi.org/10.1016/j.cub.2019.07.089

[email protected]

Recording from hippocampus (HPC) and lateral septum, Wirtshafter and Wilson show that the LS contains a locomotor spiking code that is not HPC associated. Markers of HPC activity, such as theta, SWR, and spatial correlates, are found in an overlapping group of cells. The convergence of this activity may be used downstream for VTA reward processing.

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Current Biology

Article Locomotor and Hippocampal Processing Converge in the Lateral Septum Hannah S. Wirtshafter1,2,4,5,* and Matthew A. Wilson1,2,3 1Department

of Biology, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 3Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 4Twitter: @aheadofthenerve 5Lead Contact *Correspondence: [email protected] https://doi.org/10.1016/j.cub.2019.07.089 2Picower

SUMMARY

The lateral septum (LS) has been implicated in anxiety and fear modulation and may regulate interactions between the hippocampus and regions, such as the VTA, that mediate goal-directed behavior. In this study, we simultaneously record from cells in the LS and the hippocampus during navigation and conditioning tasks. In the LS, we identify a speed and acceleration spiking code that does not map to states of anticipation or reward. Additionally, we identify an overlapping population of LS cells that change firing to cue and reward during conditioning. These cells display sharp wave ripple and theta modulation, spatial firing fields, and responses similar to the hippocampus during conditioning. These hippocampus-associated cells are not disproportionately speed or acceleration modulated, suggesting that these movement correlates are not hippocampally derived. Finally, we show that LS theta coordination is selectively enhanced in hippocampus-associated LS cells during navigation behavior that requires working memory. Taken together, these results suggest a role for the LS in transmitting spatial and contextual information, in concert with locomotor information, to downstream areas, such as the VTA, where value weighting may take place. INTRODUCTION The lateral septum (LS) has been implicated in a broad range of context-dependent behavioral responses, including involvement in the regulation of feeding [1, 2], addiction [3], anxiety [4–6], exploration [7], defensive behaviors [8], and locomotion [9]. The wide range of functional hypotheses are matched by the heterogeneity of the septum’s connections: the LS projects to multiple brain regions involved in arousal and motivation, including the ventral tegmental area (VTA) and hypothalamus, and receives projections from the brainstem and all hippocampal cornu ammonis (CA) areas [10, 11]. It has been hypothesized that one function of the LS is to serve as an intermediary between

spatial information (received from the hippocampus) and behavioral response in order to contribute to goal-directed trajectory planning [12–14]. Previous lesion and pharmacological work has suggested that afferents from the hippocampus to the lateral septum are necessary for behavioral responses to context-dependent associations in navigational and conditioning tasks [15–19], and a pathway from the hippocampus to the VTA, through the caudodorsal LS, is essential for linking context and place information with reward [11, 20]. Although the hippocampus likely contributes spatial context discrimination [21–25] and spatial coding [26–28] to the VTA [29], the role of the LS is less established. It has been hypothesized that the LS may be involved in representing other factors, such as non-spatial context, goals, rewards, or actions [11, 20, 30]. In this study, we sought to determine LS involvement in rewarded navigation and non-navigation tasks that engage both the hippocampus [29, 31–35] and VTA [29, 36]. By simultaneously recording from the caudodorsal LS and CA1 region of the hippocampus, we determined the differential contribution of spatial and non-spatial factors to LS signaling during goal-directed behavior. We hypothesized that, during the spatial navigation task, LS firing would reflect interactions between the hippocampus and VTA that have been previously observed in the VTA during navigation [29], such as increased firing during periods of high-intensity hippocampal firing (sharp wave ripples [SWRs]), theta phase locking, and a spatial firing bias. Although these correlates were present, we additionally demonstrated that the LS contains a population of cells whose firing rate is linearly correlated with a rat’s running speed and/or acceleration. We show that these correlates are present during exploration and passive movement and are not dependent on the presence of reward. We then used a conditioned approach task to determine whether there was a non-locomotive component to LS firing and whether speed and acceleration might be related to reward and motivation. We identified a population of LS cells that changes firing rate during presentation of cue and/or reward. This population overlapped with the movement-modulated cells, but in cells that responded in both tasks, there was no consistent relationship between speed and/or acceleration and cue and/or reward correlates. This led us to conclude that speed- and acceleration-correlated firing did not map onto a state of anticipation or reward. Cells that showed cue or reward modulation had firing characteristics that mirrored many aspects of hippocampal Current Biology 29, 1–16, October 7, 2019 ª 2019 Elsevier Ltd. 1

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activity, including broad spatial fields, theta-modulated firing, responses on the conditioning task similar to hippocampal responses on the same task, and increased firing during SWRs. This similarity to hippocampal firing suggests that LS cue and reward firing is hippocampally associated. Speed- and acceleration-correlated firing was observed in cells that showed no hippocampal association, suggesting that speed and acceleration correlates are not hippocampally dependent. Finally, we demonstrated that cue and reward LS cells show stronger phase locking to hippocampal theta during segments of the maze that have a high working memory load and that phase locking increases when the animal is about to make a correct choice. Taken together, these results suggest a role of the LS in communicating a conjunction of multiple factors, including spatial information from the hippocampus and movement information, to downstream targets, such as the VTA. This information from the LS could then be used by target areas for value weighing, reward seeking, or determining consequence and outcomes of choices. This view may provide additional insight into many of the proposed functions of the septum. RESULTS Lateral Septum Firing Is Correlated with Speed and Acceleration during a Spatial Task We recorded the activity of 454 caudodorsal LS units (Figures 1A and 1B) in 6 male Long Evans rats during multiple sessions on multiple tasks (Table S1). The caudodorsal region of the LS receives a large innervation from the hippocampus [10] and is involved in a hippocampus to VTA circuit that links context with reward [11]. We first sought to determine the contribution of the LS in a hippocampally-dependent navigational task. In this task, rats ran on a double-sided T-maze [31]. Rats were forced on one side of a ‘‘T’’ to a ‘‘forced arm’’ and then, following a run down a center stem, had to choose and run down the same side of a ‘‘choice arm’’ on the opposite ‘‘T’’ to get rewarded (Figure S1; STAR Methods). This maze has been used previously to characterize activity in the prefrontal cortex [31, 32], hippocampus [29, 31–33], and VTA [29]. This allowed us to examine locomotor and spatial correlates in the LS in comparison with previous work in the hippocampus and other regions receiving hippocampal input. Additionally, the LS is a pathway from the hippocampus to the VTA, and both areas have shown taskdependent changes during navigation on this maze [29, 31–33]. Previous work demonstrated that lesions of the LS cause locomotor hyperactivity [37–39] and that the LS is involved in locomotor behavior [9, 40]. Motivated by this work, we examined LS unit firing during running behavior on the maze. We found that approximately 45% (205/451) of recorded LS units had spiking rates that were linearly correlated (p < 0.05; linear regression F-test) with either positive or negative acceleration: 133 units were significantly linearly correlated with negative acceleration; 142 units with positive acceleration; and 70 units with both negative and positive acceleration. (The total number of units in this result is lower than the number of units recorded, as a small number of units fired too sparsely to be able to draw a correlation across accelerations.) About 75% of correlated units had a positive correlation with the magnitude of acceleration (Figures 1C 2 Current Biology 29, 1–16, October 7, 2019

and 1F), and about 25% were negatively correlated with the magnitude of acceleration (Figures 1D and 1F). However, it was possible to have drastically different slopes of best fit lines for positive and negative acceleration or for a cell to only have a correlation with positive or negative acceleration (Figures 1E– 1G). The mean r2 of significant correlations with negative acceleration was 0.35 ± 0.15, and the mean r2 of significant correlations with positive acceleration was 0.41 ± 0.18. These means were significantly different (two sample t test; t(273) =
2.5; p < 0.05), as were the r2 distributions for correlations with positive and negative acceleration (Kolmogorov-Smirnov ‘‘KS’’ test; p < 0.05; Figures 1H and 1I). We have thus described a population of LS cells that can be either positively or negatively modulated by acceleration and that may be differently modulated to positive or negative acceleration. We additionally found that a subset of LS electrodes showed local field potentials (LFPs) with high-amplitude bursts in theta range (7–12 Hz) strongly correlated with acceleration (max correlation = 0.32; correlation coefficient 0.19; p < 0.001; Figure S2). The signal was observed on only the most ventral electrodes (>5 mm from skull surface) and appeared to span periods of both positive and negative acceleration. This LFP correlation may indicate that acceleration information is not computed in the LS but comes from an external input. An overlapping population of LS units had firing rates linearly correlated with running speed (p < 0.05; linear regression F-test). About 59% (270/454) of total LS units were linearly correlated with speed. The majority of speed-correlated units (64% of correlated units; 173/270) increased firing with running speed (Figures 2A, 2C, and 2D), and 36% of correlated units (97/270 correlated units) had a negative correlation with speed (Figures 2B, 2D, and 2E). The mean r2 for a linear speed correlation for all LS cells was 0.40 ± 0.01: cells with a significant correlation to speed had a mean r2 of 0.59 ± 0.01, and non-significantly correlated cells had an r2 mean of 0.12 ± 0.01 (Figures 2D and 2E). Out of all recorded LS cells, 35% (157/451) were significantly correlated with both speed and positive and/or negative acceleration. 71% (318/451) of cells had at least one significant correlation with acceleration or speed, and 29% (133/451) were not significantly correlated with either acceleration or speed (Figure 2F). On average, a cell was about 3.83 more likely to be correlated with a speed cell if it was correlated with acceleration and vice versa (Fisher’s exact test; p < 0.005; n = 451). It was possible for a cell to have a highly significant correlation with speed and no correlation with positive or negative acceleration and vice versa (Figure S3). We next attempted to determine whether the LS firing correlated with speed or acceleration was correlated to an underlying behavioral state, such as motivation or receipt of reward. Prior to task training, naive animals explored the track without food reward or task demands, and we also examined LS firing during exploration on an additional novel track. LS cell firing correlations to speed and acceleration during this exploratory period were then calculated. In this non-rewarded exploration, 51% of LS cells (36/70) had a correlation with speed (Figures 3A and 3C), which is not significantly different from the 59% of correlated cells in the rewarded condition (Pearson’s chisquare test; X2(1, n = 524) = 1.61; p > 0.05). In exploration,

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Figure 1. The LS Contains Cells whose Firing Rates Correlate with Positive and Negative Acceleration (A) Brain section from implanted rat, showing the lateral septum and electrolytic lesions made after recording. The red arrows mark lesions at the tetrode tips. (B) Schematic of the lateral septum. Red shaded area indicates area where about 95% of lesions were seen. Red Xs indicate the location of three other lesions. (C) An example of a cell with a negative correlation to negative acceleration and a positive correlation to positive acceleration (an overall positive correlation with the magnitude of acceleration). The method of determining correlation is analogous to determining spatial firing fields and is as follows: acceleration per time (top left) and spike count were then determined as a function of acceleration (top right). Spike count per acceleration was than divided by time per acceleration (middle panel; see STAR Methods for more details). A cell was considered correlated to acceleration if the p value for a linear correlation to positive or negative acceleration was <0.05. In this example, the p value for the positive correlation is <0.0001 (r2 = 0.87; F(1,17) = 118), and the p value for the negative correlation is <0.0001 (r2 = 0.93; F(1,17) = 241). The lower panel shows an example period of acceleration and a corresponding raster plot. (D) Same as (C) but an example of a cell with a positive correlation to negative acceleration and a negative correlation to positive acceleration (an overall negative correlation to the magnitude of acceleration). The p value for positive correlation <0.0001 (r2 = 0.58; F(1,29) = 41.9); the p value for negative correlation <0.0001 (r2 = 0.56; F(1,28) = 36.2). (E) Same as (C) and (D) but an example of a cell with a significant linear correlation with negative acceleration (p < 0.001; r2 = 0.48; F(1,22) = 21.9) and no linear correlation with positive acceleration (p > 0.5; r2 =
0.04; F(1,22) = 0.05). (F) Acceleration correlations for all LS cells in one session. Correlates are computed as in (C). Significant linear correlations are marked with a red best fit line; nonsignificant are marked in black. The same cells and recording session are depicted in Figure 2C. In some sessions, LFP magnitude was also correlated with the magnitude of acceleration; see Figure S2. (G) Distribution of difference ratios between slopes for positive and negative acceleration. A slopes difference ratio was calculated for each cell, wherein a difference ratio of 0 means the absolute value of positive and negative slopes are the same and a value 1 means they are highly different. The difference ratio for the cell depicted in (B) is <0.01, the cell in (C) is 0.09, and the cell in (D) is 0.89. (H) Mean r2 for all acceleration correlations. Error bars represent SE. Hatched bars signify relationships with negative acceleration; non-hatched bars are relationships with positive acceleration. Each color represents a group: blue is all cells; purple is significantly correlated cells; and green is non-correlated cells. The only significant within-group difference is between the mean r2 values of cells significantly correlated with negative and positive acceleration (two sample t test; t(273) =
2.5; p < 0.0001). All across-group comparisons are significant (two sample t test; all p < 0.0001). (I) Distribution of r2 for all cells (top panels), compared with distributions for significantly correlated and non-correlated cells (bottom panels). Left panels are negative acceleration; right panels are positive acceleration. All distributions of significantly correlated cells are different than distributions of non-correlated cells (all KS test; p < 0.0005).

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Figure 2. The LS Contains Cells whose Firing Rates Correlate with Running Speed (A) Example of an LS cell whose firing is positively correlated with speed. Correlations were found similarly to acceleration (see C and STAR Methods): the amount of time spent at each speed was calculated (top left) and used to normalize the spike count per speed (top right). Spike rate was then calculated and plotted against a (middle), and cell was considered correlated if p < 0.05. In this example, r2 = 0.98, p < 0.0001, F(1,19) = 836. The lower panel shows an example period of acceleration and a corresponding raster plot. (B) Same as (A) but an example of an LS cell whose firing rate is negatively correlated with speed (r2 = 0.87; p < 0.0001; F(1,30) = 202). (C) Speed correlations for all LS cells in one session. Correlations are computed as in (A). The same recording session and cells are represented as in Figure 1F. Significant linear correlations are marked with a red best fit line; non-significant are marked in black. (legend continued on next page)

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41% (28/70) were significantly correlated with acceleration, which is also not significantly different from the 45% during rewarded trials (Figures 3A and 3D; Pearson’s chi-square test; X2(1, n = 521) = 2.13; p > 0.05). The proportion of cells modulated by negative or positive acceleration during exploration is also not significantly different than during rewarded trials (Pearson’s chi-square test; for negative acceleration: X2(1, n = 233) < 0.01, p > 0.05; for negative acceleration: X2(1, n = 233) = 0.83, p > 0.05; Figure 3B). After finding that movement-correlated firing was not dependent on the presence of reward, we sought to determine whether this firing required self-generated movement. We passively moved the animals by placing them, unrestrained, on top of a moving cart that was rolled linearly or they were rotated, unrestrained, atop a lazy Susan. During unrestrained passive movement, there was maintenance of the speed correlate in 65% of cells (28/43; Figures 3A and 3E; Pearson’s chi-square test; X2(1, n = 497) = 0.52; p > 0.05). However, the number of acceleration-modulated cells was greatly diminished. Although acceleration correlates were present, they composed only 23% (10/43) of the population, which is significantly different than the 45% observed in rewarded trials (Figures 3A and 3F; Pearson’s chisquare test; X2(1, n = 494) = 7.87; p % 0.005), controlled for the higher maximum acceleration during passive movement. The LS firing correlation with speed or acceleration thus does not require the presence of reward, but the acceleration component may require self-generated movement. In order to evaluate the information contained in simultaneously recorded LS ensembles, we used Bayesian decoding techniques analogous to the decoding of position using place cells [41] (see STAR Methods), and the decoding of speed and acceleration was accurate to levels significantly better than chance as assessed by two different methods (Figure S4). We additionally used decoding to determine whether speed and acceleration coding followed or preceded movement. We found a consistent improvement in speed decoding when spikes were shifted forward in time (meaning firing preceded movement; Figure S5A), although we were unable to determine the exact latency. To limit the contribution of starting and stopping behavior, we restricted the analysis to velocities between 10 cm/s and 30 cm/s, and decoding remained improved with a forward shift in spiking (Figure 3G). For acceleration decoding, we saw a slight improvement in decoding when firing was shifted backward in time (meaning firing initially followed movement; Figure S5B), and this effect was stronger when we restricted analysis to intermediate acceleration amplitudes (<20 cm/s2 and >100 cm/s2) that limited the contribution of starting and stopping of movement (Figure 3H). This leads us to conclude that LS acceleration firing follows movement and LS speed firing precedes movement.

In a Conditioned Approach Task, the Lateral Septum Represents Cue and Reward, and These Representations Do Not Map onto Speed and Acceleration Firing Due to the nature of the navigation task, periods of changing speed and acceleration often coincided with reward approach and retrieval. We hypothesized that speed and acceleration firing might correlate with periods of reward anticipation and acquisition. To test this, we introduced a non-spatial conditioned approach task. In this task, an intertrial interval was followed by 8 s of simultaneous light and sound cue without reward followed by 8 s of access to liquid reward through a spout as the cues continued. After the 8 s reward period, the spout administering reward was withdrawn and the cues were turned off (Figures 4A and S6A). Although this task is not LS or hippocampus dependent, animals with LS lesions do show altered behavior on cued tasks [12, 15, 18, 20, 38, 42–44], and cells in the hippocampus do respond to different phases of conditioning [34, 45–49]. The animals learned this task within one to two training sessions and moved very little during the intertrial interval and instead waited by the retractable reward spout. We hypothesized that the cue period might represent a period of anticipation, such as a period of active approach in the maze, and thus might be linked with speed-correlated cells. Conversely, the reward period might correlate with the initiation of acquisition and thus might be linked to acceleration firing. In order to ensure that we were comparing speed and acceleration cells found on the maze to cells firing in the conditioned approach task, we continuously recorded while first running the animals on the maze and then waiting at least 45 min and finally running the animals on the conditioned approach task. This allowed us to track the same cells throughout both tasks. We found that approximately 66% of LS cells (188/287) changed firing more than 20% during the cue period compared to the preceding intertrial period (throughout this study, we set a 20% threshold to classify cells that respond to a stimulus, as a 20% threshold has been used previously to classify place cell firing fields) [50, 51]. In the cue period, approximately 59% (111/188) of responding cells decreased firing and 41% (77/ 188) increased firing (Figure 4B). In the reward period, about 66% of LS cells (189/287) changed firing more than 20% compared to the preceding intertrial period, with about 50% of cells (94/188) decreasing firing and 50% (94/188) increasing firing. Across all cells, a cell was 5.13 more likely to change firing to cue if it changed firing to reward or vice versa (Fisher’s exact test; p < 0.005; n = 287). We then sought to determine whether the cells that changed firing during cue and/or reward periods mapped onto cells that had correlations with speed and/or acceleration. Cueand reward-modulated cells (n = 211) did not have significantly

(D) Distribution of slopes for best fit lines for linear correlations with speed. A slope of 0 is marked with a dashed red line. About 64% of cells significantly correlated with speed have positive slopes, compared with 60% of cells overall, which is not significantly different (two sample t test; t(722) = 1.11; p > 0.05). This graph is truncated for viewing but contains over 95% percent of slopes. (E) Mean r2 for all velocity correlations. All differences in means are significant (two-sample t test; p < 0.0005). (F) Distribution of r2 for all cells (top panel) compared with distributions for significantly correlated and non-correlated cells (bottom panel). (G) Diagram showing the populations of cells correlated with speed and acceleration. The majority of cells correlated with speed are also correlated with acceleration and vice versa. 29% of cells have correlations with neither speed nor acceleration. See also Figure S3.

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different populations of speed- and/or acceleration-modulated cells as compared to non-cue and non-reward cells (one-way ANOVA; f(5,18) = 0.17; p > 0.05). Conversely, speed-correlated cells were not significantly more cue and reward modulated than acceleration-correlated cells (one-way ANOVA; f(3,8) = 0.46; p > 0.05; Figures 4C and 4D). (Cells that were neither speed nor acceleration modulated do have a significantly different distribution of cue and reward modulation: including them in the ANOVA results in a p < 0.05 [f(4,10) = 4.62; data not shown]). We therefore concluded that, although there is substantial overlap between movement-modulated cells and cue- and reward-modulated cells (Figure 4E), speed- and acceleration-correlated firing did not appear to be related to activity induced by reward or reward-predictive cues. Cue and Reward Cells Display an Increased Firing Rate during Ripples, Theta Phase Locking, Spatially Modulated Firing Fields, and Responses Similar to the Hippocampus CA1 on the Conditioning Task Given the hippocampus’ role in spatial navigation and its connections to the septum, we sought to determine whether septal firing on the navigational task and conditioning task was correlated with hippocampal activity. During LS recording in the maze and the subsequent conditioned approach task, we also recorded CA1 hippocampus principle cells (n = 124 units after removing cells with an intertrial firing rate less than 1.00 Hz during conditioning; the number of interneurons collected was insufficient for analysis). Consistent with previously published work during conditioning tasks [52, 53], we found that CA1 hippocampus units changed firing rate during cue and reward periods (Figure 5A), with 74% (92/124) of cells changing firing rate during the cue period (51% reduced firing rate and 23% increased firing rate) and 68% (84/124) of cells changing firing rate during the reward period (30% reduced firing rate and 38% increased firing rate; all rates were compared to the preceding 8 s in the intertrial period). To determine whether cue and reward modulation was similar between the LS and hippocampus, we compared the distribution of firing rate changes during conditioning in both populations. Cells that changed firing rate during cue were similarly distributed in the LS and hippocampus (KS test; p > 0.05; Figures

5B and S6B). We then did analogous analysis on reward firing in the LS and hippocampus populations and also found a KS test p > 0.05. Therefore, LS and hippocampal cells appear to respond similarly to cue and reward on the conditioning task. After determining that LS cells were modulated similarly to hippocampal cells during cue and reward, we examined the firing of LS cells associated with hippocampal SWRs during maze running, rest, and the conditioning task. If a population of LS cells was strongly associated with hippocampal activity, we would expect these cells to change firing rate during, or shortly after, ripple detection in CA1. Consistent with previous studies [54], 47% (84/179) of LS cells increased their firing rate over 20% (mean increase was 80%) after ripple detection in CA1 (Figure 6A; although this threshold is relatively low compared to typical SWR modulation seen in the hippocampus, it is equivalent to the degree of SWR modulation observed in the VTA) [30]. Peak firing occurred approximately 35 ms after ripple onset (Figure 6A), a time course consistent with the LS’s position as an intermediary between the hippocampus and the VTA, which shows peak firing about 75 ms after ripple start [29]. If cells that increased their firing during ripples and cells that were cue and/or reward modulated were both hippocampally correlated, we would expect these populations to be heavily overlapping and not independent. Running cross tabs using Fischer’s exact test, we found that the odds of being ripple modulated in cuemodulated cells were 2.23 higher (p < 0.005; Fisher’s exact test; n = 179) than in non-cue-modulated cells. Similarly, if a cell was reward modulated, it was 3.23 more likely (p < 0.005; Fisher’s exact test; n = 179) to be a ripple-modulated cell than if it was not reward modulated. During locomotion, the hippocampus displays prominent theta rhythm (4–12 Hz), which has been hypothesized to contribute to the processing and encoding of information [33, 55–59], as well as coordinating interactions between the hippocampus and other areas [31, 60, 61]. In the LS, this coordination may be involved in motivated behavior [62], running speed regulation [9], and spatial mapping [40, 56]. LS coordination with hippocampal activity, such as seen during cue and reward, may be modulated by LS theta-rhythmic firing or spike phase locking to hippocampal theta, such as seen in the prefrontal cortex [31, 63],

Figure 3. Spiking Correlation with Speed and Acceleration Occurs in the Absence of Reward and Precedes and Follows Movement, Respectively (A) The proportion of cells modulated by speed and acceleration during rewarded trials, exploration, and passive movement. There is a significantly different distribution of acceleration-correlated cells during rewarded trials as compared to passive movement (Pearson’s chi-squared test; X2(1, n = 494) = 7.87; p = 0.005). (B) Out of acceleration-correlated cells, the proportion correlated with positive and negative acceleration during rewarded trials, exploration, and passive movement. All comparisons were not significant (Pearson’s chi-squared test; all p > 0.05). (C) Example of spiking correlations with speed during exploration, calculated as in Figure 2A. Significant correlations are marked with a red best fit line. (D) The same cells as in (C) during exploration but their correlations with acceleration, calculated as in Figure 1C. Significant correlations are marked with a red best fit line. (E) Examples of spiking correlations with speed during passive movement, calculated as in Figure 2A. Significant correlations are marked with a red best fit line. (F) The same cells as in (E) during passive movement but their correlations with acceleration, calculated as in Figure 1C. Significant correlations are marked with a red best fit line. (G) Decoding (see Figures S4A and S4C) speed with shifted spike trains to determine whether spiking precedes or follows movement, with SE bars. Spike train shift is indicated in seconds on the x axis. Mean error in speed decoding measured only for speeds between 10 cm/s and 30 cm/s (see Figure S5A for decoding with all speeds). Mean error in speed decoding relative to error at 0 shift, marked with a dotted line. Shown is mean error over 10 sessions of maze running and decoding. Change below zero is improvement in decoding. (H) Same as in (G), but error in acceleration decoding (see Figures S4B and S4D) measured only for accelerations with magnitudes between 20 cm/s2 and 100 cm/s2 (see Figure S5B for decoding with all accelerations). Shown is mean error over 9 sessions of maze running and decoding. Change below zero is improvement in decoding.

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Figure 4. During a Conditioning Task, LS Cells Change Firing during Cue and Reward (A) Schematic showing the details of the conditioned approach task. An intertrial period of 30–90 s is followed by an 8 s conditioned stimulus (CS) period with light and sound cue. With the cues still on, a reward spout is inserted into the cage for 8 s. At the end of the reward period, the spout is withdrawn and the cue turned off. (B) Examples of PSTHs of four LS cells that change firing during the cue and reward period of the conditioned approach task. Intertrial periods are marked in blue, the CS period is marked in green, and the reward period is marked in purple. All changes in firing rate during cue and reward are compared to the last 8 s in the intertrial period. (Upper left) The cell decreases firing during cue and increases firing during reward. (Upper right) Cell decreases firing during cue and slightly decreases firing during reward. (Lower left) Cell decreases firing during cue and reward. (Lower right) Cell increases firing during cue and does not change during reward. Other combinations of increase and/or decrease and/or stay the same were seen, but not pictured. See also Figure S6B. (C) Cells that change firing during the conditioned approach task cannot be categorized as cells correlated to speed or acceleration (n = 223). Cells were considered to have changed firing at cue or reward if firing rate changed more than 20%. The populations of cue- and reward-modulated cells are not significantly different from one another in their makeup of speed- and acceleration-modulated cells (one-way ANOVA; f(5,18) = 0.17; p > 0.05). (legend continued on next page)

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VTA [29], medial septum [64], and as previously observed in the lateral septum [9, 18, 56, 65, 66]. We therefore sought to determine whether a population of LS cells was theta modulated or showed phase locking to hippocampal theta and whether these LS cells were primarily cells that showed other markers of hippocampal association. To determine this, we filtered CA1 LFPs in the theta band (4–12 Hz) and computed the circular concentration kappa for all LS cells (Figure 6B). We found that about 30% of cells (44/147) were strongly phase locked to hippocampal theta (kappa > 0.3), and about 41% of cells (60/147) showed weak phase locking (0.1 > kappa % 0.3; Figure 6C). Mean peak phase preference for strongly modulated cells was +22 degrees from the phase of maximum hippocampal principle cell firing (Figure S7). Strong theta modulation tended to co-vary with cue, reward, and ripple modulation: a cell was 3.03 more likely to be cue modulated (p < 0.05), 2.83 more likely to be reward modulated (p < 0.05), and 3.73 more likely to be SWR modulated (p < 0.005) if it was modulated by theta than if it was not (all Fisher’s exact test; n = 147). We sought, finally, to determine whether any LS cells had place fields and, if so, whether these cells also tended to be ripple and cue and/or reward modulated. Numerous previous

Figure 5. The Hippocampus Displays Similar Firing Patterns to the LS during Conditioning (A) Examples of PSTHs of four examples of hippocampus cell responses on conditioned approach task. Intertrial periods are marked in blue, the CS period is marked in green, and the reward period is marked in purple. All changes in firing rate during cue and reward are compared to the last 8 s in the intertrial period. (Top left) Cell decreases firing during cue and increases firing during reward. (Top right) Cell decreases firing at cue and slightly decreases firing at reward. (Bottom left) Cell decreases firing at cue and reward. (Bottom right) Cell increases firing at cue and does not change firing at reward. Other combinations of increase and/or decrease and/or stay the same were also seen, but not pictured. See also Figure S6B. (B) Quantification of cue and reward firing changes in the LS versus the hippocampus. LS firing is in blue; hippocampal firing is in purple. x axis shows the percent change in firing rate during cue (left) and reward (right), as compared to the previous 8 s in the intertrial interval. Distribution of firing rates is similar for LS and hippocampus cells during cue (KS test; p > 0.05) and reward (KS test; p > 0.05). Examples of LS and hippocampus cells at the minimum and maximum changes can be seen in Figure S6B.

studies have shown that between 15% and 40% of cells in the lateral septum have spatially selective firing [67–72]. Consistent with this body of work, we found that 56.0% of LS cells (254/ 454) had spatially selective firing, based on a bits-per-spike cutoff of 0.8 [73, 74] (Figures 6D and 6E). The largest number of LS cells with spatially selective firing had place fields in the choice arms, which were rewarded (44.3% of LS cells with bits/spike > 0.8). If place-modulated firing was a sign of hippocampal association, we again would expect LS cells with this firing to be heavily overlapping with ripple-, theta-, and cue- and/or reward-modulated cells. We found that the odds of having a place field in ripple-modulated cells, theta-modulated cells, cue-modulated cells, and reward-modulated cells were 5.73, 2.83, 3.63, and 2.93 higher, respectively, than in non-ripple-, non-cue-, and non-reward-modulated cells (Fisher’s exact test; all p < 0.005; n = 208). Together, these results indicate that the LS contains cells with hippocampally associated spiking activity. This is demonstrated by LS cells that (1) respond similarly to the hippocampus on a conditioned approach task, (2) increase firing during SWRs, (3) are phase locked to hippocampal theta, and (4) have spatially

(D) Cells that are correlated to speed or acceleration cannot be categorized as changing firing during cue or reward (n = 223). The populations of speed- and acceleration-modulated cells are not significantly different from one another in their makeup of cue- and reward-modulated cells (one-way ANOVA; f(3,8) = 0.46; p > 0.05). (E) Diagram illustrating the overlap between cells that are correlated with speed or acceleration and cells correlated with cue and/or reward. 4% of cells were not modulated by speed, acceleration, cue, or reward.

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Figure 6. LS Cells Show Hippocampally Associated Spiking Activity (A) PSTH of LS firing rate around ripple start time. Each line is the average firing of a cell around ripple start at 0 ms. Blue lines are cells that increased firing over 20% during a ripple; red lines are cells that did not. Bolded blue and purple lines are the averages for their respective populations. (B) Distribution of circular concentration kappa for all LS cells, measuring degree of theta modulation. A cell was considered strongly modulated if it had a kappa >0.3, marked with a dotted line. (C) Examples of three LS cells: not theta modulated; strongly theta modulated; and slightly theta modulated. (Top row) Spiking phase preference during theta measured from a CA1 tetrode is shown, normalized so theta phase 0 is maximum hippocampal spiking. (Bottom row) Autocorrelogram of the same cells is shown. See also Figure S7. (D) Distribution of bits per spike for LS cells. Cutoff to be considered spatially modulated was 0.8, marked by a dotted line. (E) Two examples of ‘‘place fields’’ in LS cells. (Left) An LS cell with place-selective firing at reward site is shown. Bits per spike = 0.8. (Right) LS cell with placeselective firing on center stem and forced choice point is shown. Bits per spike = 1.4.

10 Current Biology 29, 1–16, October 7, 2019

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modulated firing. These associations strongly co-vary and tend to occur together and are highly concentrated in cells that respond to cue and reward during the conditioning task (Figure 7A). If the speed and acceleration modulation were derived from hippocampal input, we would expect speed and acceleration cells to be overwhelmingly represented within groups of cells with these hippocampus-associated spiking patterns. However, these hippocampus-associated cells were no more likely to contain movement correlated activity than other LS cells (Fisher’s exact test; all p > 0.05; Figure 7A). This suggests that speed and acceleration spiking is not hippocampally derived. We hypothesize that the speed and acceleration firing modulation may arise from structures outside the limbic system, such as from one of the numerous brainstem inputs to the LS (Figure 7B) [10]. Coordination between the Hippocampus and the LS Previous work found kappa in the prefrontal cortex (PFC) to be higher on the middle stem of the maze when approaching the choice point and reward than when approaching the forced arms [31]. Strongly theta-modulated LS cells (kappa > 0.3) also showed a significantly higher kappa during approach to the choice point (paired one-sided t test(20) = 2.14; p < 0.05).

Figure 7. Speed- and Acceleration-Modulated Cells, as a Population, Have Unique Properties and Anatomical Connections Compared to Cue- and Reward-Modulated Cells (A) Chart showing the cross tabulations of the likelihood of co-occurring modulations. Number is odds of finding the two modulations together (each ratio was calculated independently) as compared to the odds if the modulations were independent. Odds ratio and p values were calculated using Fisher’s exact test. p < 0.05 marked with one star; p < 0.005 marked with two stars. Hippocampally correlated cells can be identified by the presence of ripple-modulated firing, theta-modulated firing, place-modulated firing, and response to cue and reward in the conditioned approach task. All three of these identifiers are co-occurring and occur separately from speed and acceleration modulation, which are highly covariant. (B) We hypothesize that the movement information is coming from one of the many brainstem connections to the LS. The brainstem may send movement information from the septum, which is also receiving spatial and working memory information from the hippocampus. These convergences of these two inputs allow the LS to transmit spatial and contextual information, in concert with locomotor information, to downstream areas, such as the VTA, where value weighting occurs.

We then divided the LS cells into movement-associated (correlated with velocity and/or acceleration) and hippocampusassociated (cue and/or reward, ripple, theta, and place modulated). (Cells could be in both groups, one group, or neither group.) We determined kappa on the center stem for each group, both in both forced and choice directions. Hippocampally associated LS cells had an increased kappa when the rat was on the center stem in the choice direction as compared to the forced direction (paired one-sided t test(101) = 2.29; p < 0.05). Movement-correlated cells did not show increased kappa when the rat was on the center stem (paired one-sided t test(99) = 1.45; p > 0.05). Although differences in baseline theta modulation cannot be ruled out as being responsible for this difference, there was no significant difference between mean kappas of cue and/or reward cells and hippocampus-associated cells in the choice direction (unpaired t test(232) =
0.34; p > 0.05) and forced direction (unpaired t test(236) = 0.14; p > 0.05). PFC neurons have a higher average kappa on the center stem, compared to the forced and choice arms, during all trials [28]. Using a paired t test, LS cells also have a significantly higher average kappa on the center stem during all trials (paired one-tailed t test(19) =
2.50; p = 0.01), as well as a significant difference between the average kappa on the center stem on correct and incorrect trials (correct trials mean = 0.58 ± 0.07; incorrect trials mean = 0.41 ± 0.06; unpaired one tailed t test(43) = 1.74; p < 0.05). We were not able to determine whether there was a difference in trial-by-trial kappa for Current Biology 29, 1–16, October 7, 2019 11

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movement-correlated cells versus hippocampally associated cells as the movement-correlated units and hippocampus-associated units that fired enough to get a sufficient number of kappa measurements were highly overlapping. Unlike the VTA [29], there did not appear to be a difference in spiking rate during reward approach for correct and incorrect trials (paired t test(62) = 0.14; p > 0.05). DISCUSSION Although the LS is a primary output of the hippocampus [10] and has been implicated in a variety of motivation-related behaviors [1–9], little is known about how LS cells respond during goaldirected behavioral tasks. In this study, we identified a population of cells in the lateral septum with firing correlated to the animal’s running speed and/or acceleration during navigation (Figures 1, 2, and 3). We additionally described a partially overlapping population of cells that are modulated by cue and reward during an approach-conditioning task (Figure 4). The firing of these cells appears to reflect input from the hippocampus, as evidenced by increased firing during ripples, theta modulation, and spatial tuning (Figures 5 and 6). These results suggest a function for the septum wherein the convergence of movement-related signaling with hippocampal input allows the LS to contribute to path integration as well as to evaluate task-relevant changes in context arising from the animal’s movement. Anatomical and Functional Considerations of LS Movement-Related Firing In the current study, cells with speed and acceleration correlates were recorded from the same electrode sites as were cue and reward cells, suggesting that these populations are somewhat intermingled in the far dorsal portion of the lateral septum from which we recorded. As septal inputs from the hippocampal formation, diencephalon, and brainstem are all topographically organized, it is possible that different populations of cells may be found in other septal subregions. It is likely that we were sampling from sites that received inputs from both the CA1 region of the dorsal hippocampus and from the brainstem (Figure 7B) [10, 75]. One might expect that recording sites in more ventral regions of the LS, which receive greater input from the hypothalamus, and more ventral regions of the hippocampus (and little to no input from the VTA) [10, 11] would show less cue- and reward-related firing, fewer place fields, and increased engagement during hypothalamic-associated tasks, such as feeding. Indeed, recordings from more ventral LS neurons have described lack of place fields [56], and an LS-to-hypothalamus circuit has been described that appears to be involved in the regulation of feeding [1, 2]. A previous study by Tingley and Buzsa´ki failed to find speedrelated cells in the LS, although they did demonstrate that LS cells carry spatial information in the phase of their spiking [56]. Interestingly, this study also did not find any LS cells with place fields, although such cells were observed in the current experiments and in numerous previous studies [67–72]. These disparities may reflect differences in recording sites as we, in contrast to Tingley and Buzsa´ki, sampled solely from the most dorsal region of the LS. We determined that speed and acceleration signaling in the LS is present during both exploration and passive movement 12 Current Biology 29, 1–16, October 7, 2019

(Figure 3), suggesting that this signal is not only generated during goal-directed navigation. Instead, it may be contributing to the formation of a cognitive map via latent learning [76] and path integration, which may be dependent on spatial processing in the entorhinal cortex [77–79]. Speed cells have also been found in the entorhinal cortex (EC), which, like those studied here, have a linear spiking correlation with running speed (Figure 2). These cells also resemble those we studied, in that their spiking rate correlates best with future speed (Figures 3G and S5A) [80, 81]. The LS sends projections to multiple regions that project directly to the EC [10, 82], so LS speed and acceleration spiking may therefore contribute to grid cell activity during path integration. Movement-correlated signaling has also been reported in the brainstem, which shows similarity to the speed- and acceleration-correlated spiking we have identified in the LS. Recent work has shown spiking and LFP correlations with speed and onset of movement in the mesencephalic locomotor region [83–85], an area connected to the lateral septum most directly through the lateral preoptic area, hypothalamus, and raphe, as well less directly via numerous other areas [10, 75, 83]. The number of acceleration-correlated cells was greatly reduced during passive, as compared to active, movement (Figures 3A and 3F). This suggests that the acceleration signal was dependent on active movement generation rather than being a passive response to vestibular or visual input. It is possible that acceleration-correlated firing may be driven by proprioceptive or reafferent motor input. Indeed, brainstem regions that may contribute to LS movement signaling, such as the mesencephalic locomotor region (Figure 7B), respond to self-generated movements even in the absence of visual or vestibular input [86]. Acceleration-modulated LS spiking as a reflection of motor movement is consistent with our finding that this spiking correlated best with past acceleration (Figures 3H and S5B). This is in contrast to speed-modulated spiking, which slightly precedes movement (Figures 3G and S5A), and therefore may be using visual or vestibular input to predict future movement and is not reliant on self-generation of locomotion. In the hippocampus, theta frequency and power are correlated with running speed [55, 87–89]. Given that a substantial number of cells in the LS show hippocampally associated activity, we considered the possibility that LS speed and acceleration spiking might be hippocampally derived. Although some LS cells show theta-modulated spiking (Figures 6B and 6C), this modulation, like other signatures of hippocampal involvement, is seen primarily in cue- and reward-modulated cells (Figure 7A). The majority of speed- and acceleration-modulated cells are not strongly theta modulated, suggesting that the speed- and acceleration-associated firing of LS cells is not directly associated with hippocampal theta. Although previous work has shown that a hippocampus-LS-hypothalamus pathway regulates running speed via theta rhythm [9], these findings are not inconsistent with the idea that LS speed firing may be derived from outside the hippocampus and then used within the hippocampal circuit to modulate running speed and hippocampal speedrelated spiking. In particular, if the contested LS to MS projection exists, the LS may be sending speed information to the medial septum, which then uses this information to modulate hippocampal speed-related firing and theta rhythm [90].

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The Role of the LS in Motivated Behavior Like the PFC [31], cue and reward LS neurons show stronger phase locking to hippocampal theta during choice point on trials during which they make the correct, as compared to the incorrect, response. Therefore, it appears that working memory information relevant to choice accuracy is conveyed to the LS, despite that expected reward outcome is not reflected in firing rate changes. The LS may be firing in response to locations that have been associated with reward, apart from whether the location is to be rewarded on a specific trial. Hippocampal input to the LS, carrying theta phase locking and spatial information, may be integrated with movement correlates to modulate downstream reward responses. These results would also be consistent with previously published work showing that reward-related neurons in the VTA show higher phase locking to hippocampal theta than non-reward-related neurons [29]. Given that the LS sends a substantial projection to the VTA, increased phase locking of LS neurons upon reward approach may be driving these VTA reward-related neurons. It has been proposed that phase locking is one mechanism by which theta rhythms coordinate interactions between the hippocampus and extrahippocampal areas [31, 61, 63]. Although the LS is not necessary for the production of theta [18, 91, 92], lesions or inactivation of the LS do disturb theta phase and frequency [65, 91, 93] and alter locomotor activity [9]. Theta rhythm coordination may enable the LS to combine information from the hippocampus and movement-related areas, and this information may then be sent to downstream areas, such as the VTA, where it may influence goal-related firing. This is consistent with previous work showing that VTA cells that are associated with hippocampal SWR events also exhibit theta-modulated firing [29] and that VTA responses to theta-frequency hippocampal stimulation required a functional LS [11]. Dopaminergic VTA neurons are also reactivated during hippocampal SWR-associated replay of goal locations [29]. It has been suggested that hippocampal replay, which occurs during SWRs in both wake and sleep, is used to plan trajectories [74, 94–96]. Our work has demonstrated that LS firing, specifically of cue- and reward-related cells, is increased during SWRs (Figure 7A), at about the latency that would be expected based on a hippocampus to LS to VTA pathway [29]. Therefore, the LS may be passing cue information to the VTA not just during active reward seeking but also during periods of hippocampal replay. Cue, reward, and movement information may therefore be transmitted from the LS to other structures during SWRs that occur during planning and memory consolidation. This information may allow the animal to evaluate and plan behaviors that may require movement or initiation of action. Conclusions We have demonstrated that the LS contains cells that carry information about changes in a number of behaviorally relevant variables, including acceleration, speed, cue, reward, and location, and LS reactivation during SWRs may further implicate the LS in trajectory planning. These findings suggest a role for the LS in representing conjunctions of these factors that could be relevant to the guidance and evaluation of motivated behavior.

STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: d d d d

d

d

KEY RESOURCES TABLE LEAD CONTACT AND MATERIALS AVAILABILITY EXPERIMENTAL MODEL AND SUBJECT DETAILS METHOD DETAILS B Tetrode Implementation and Electrophysiology B Behavioral Training QUANTIFICATION AND STATISTICAL ANALYSIS B Speed and Acceleration Analysis B Decoding Acceleration and Speed B Conditioned Approach Task Analysis B Sharp Wave Ripple, Theta Rhythm, and Place Cell Analysis DATA AND CODE AVAILABILITY

SUPPLEMENTAL INFORMATION Supplemental Information can be found online at https://doi.org/10.1016/j. cub.2019.07.089. ACKNOWLEDGMENTS H.S.W. was supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program. We thank Israel Donato Ridgley and Molly Quan for support with analysis and experiments and David Wirtshafter for discussion and edits. We also thank all members of the Wilson lab, especially Hector Penagos, for discussion and comments. AUTHOR CONTRIBUTIONS Investigation, H.S.W.; Formal Analysis, H.S.W.; Writing – Original Draft, H.S.W.; Writing – Review & Editing, H.S.W. and M.A.W.; Supervision, M.A.W. DECLARATION OF INTERESTS The authors declare no competing interests. Received: May 16, 2019 Revised: June 3, 2019 Accepted: July 31, 2019 Published: September 19, 2019 REFERENCES 1. Sweeney, P., and Yang, Y. (2015). An excitatory ventral hippocampus to lateral septum circuit that suppresses feeding. Nat. Commun. 6, 10188. 2. Sweeney, P., and Yang, Y. (2016). An inhibitory septum to lateral hypothalamus circuit that suppresses feeding. J. Neurosci. 36, 11185–11195. 3. Harasta, A.E., Power, J.M., von Jonquieres, G., Karl, T., Drucker, D.J., Housley, G.D., Schneider, M., and Klugmann, M. (2015). Septal glucagon-like peptide 1 receptor expression determines suppression of cocaine-induced behavior. Neuropsychopharmacology 40, 1969–1978. 4. Gray, J.A., and McNaughton, N. (2003). The Neuropsychology of Anxiety: An Enquiry into the Functions of the Septo-hippocampal System, Second Edition (Oxford University Press), p. 1. 5. Anthony, T.E., Dee, N., Bernard, A., Lerchner, W., Heintz, N., and Anderson, D.J. (2014). Control of stress-induced persistent anxiety by an extra-amygdala septohypothalamic circuit. Cell 156, 522–536.

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STAR+METHODS KEY RESOURCES TABLE

REAGENT or RESOURCE

SOURCE

IDENTIFIER

Charles River

N/A

[97]

https://github.com/wilsonlab/arte-backend

XClust

N/A

https://github.com/wilsonlab/mwsoft64

OAT

[98]

https://github.com/jonnew/Oat

MATLAB circular statistics toolbox

[99]

https://github.com/circstat/circstat-matlab

This paper

https://github.com/hsw28/data_analysis

Experimental Models: Organisms/Strains Rat Software and Algorithms ARTE

Other Other analysis code

LEAD CONTACT AND MATERIALS AVAILABILITY Questions or requests for information should be directed to and will be fulfilled by the Lead Contact, Hannah Wirtshafter (hsw@mit. edu). This study did not generate new unique reagents. EXPERIMENTAL MODEL AND SUBJECT DETAILS All procedures were performed within MIT Committee on Animal Care and NIH guidelines. Seven male Long-Evans rats (275 g to 325 g) sourced from Charles River were implanted with tetrode arrays and run on a navigation and/or conditioning task (see Table S1). Animals were individually housed in an animal facility with a 12h light dark cycle. METHOD DETAILS Tetrode Implementation and Electrophysiology Seven male Long-Evans rats (275 g to 325 g) were implanted under isoflourine anesthesia (induction 4%, maintenance 1%–2%) with 2 arrays, each containing 16 independently moveable tetrodes (see [31]). One array was stereotaxically directed toward the dorsal hippocampus CA1 (coordinates Bregma
3.7, midline
3.2), while the other was directed toward caudo-dorsal lateral septum (coordinates Bregma +.05, midline
0.5). Animals were grounded with a screw posterior to Lambda. Tetrodes were individually lowered to goal location over a period of days. A CA1 reference was placed in the corpus collosum white matter tract above CA1. A lateral septum reference was placed in white matter above the LS, or in the quiet lateral septum. Electrical signals were passed through two 16 channel headstage preamplifiers to a patchbox used to select a reference channel. Signal was then fed to 16 Neurolynx amplifiers. Extracellular action potentials were acquired at 31kHz, 0.3-6kHz filtering. LFPs were simultaneously recorded at a sampling rate of 2kHz and filtered at 1Hz to 475Hz. Data were collected using lab software ARTE [97]. Animal position and direction during RUN was collected at 30Hz via overhead cameras. Position was collected and extracted using OAT [98]. Cells were isolated using a custom software package (Xclust) using spike amplitude on each of the four channels. Septal and hippocampal cells with unstable waveforms or large amounts of drift were excluded in the analysis. After completion of the study, animals were lesioned with 15 mA of current for 10 s. Animals were perfused at least one week post lesioning, and tetrode location was verified with histology. Behavioral Training Implanted animals were trained for 2-4 weeks to run a spatial choice task [29, 31] on an end-to-end T maze (Figure S1). Prior to the start of training, the animals were allowed to explore the unrewarded maze for two days, followed by two days of exploration with reward at the reward site in order for the animals to learn the reward associated locations. During training, animals were food deprived to 85% body weight. The maze task consisted of two phases: in the forced choice phase, animals were randomly forced to either side of the T. (Animals were not directed to one side more than 3 consecutive times.) In the free choice phase, animals had to choose, at the opposite end of the maze, the same side that they were forced to. If they made the correct choice, animals were rewarded with 0.2mL of 20% sucrose 10% chocolate milk powder dispensed remotely from a syringe pump. Tail tip in an arm was used as the criteria for arm entrance. Animals were trained to a criterion of 75%. There was wide variability among animals for length of time it took to learn the task, as well as their ability to maintain performance at criterion. Animals were run for 30 minutes a day. For passive Current Biology 29, 1–16.e1–e3, October 7, 2019 e1

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movement, animals were either unconstrained and placed on top of a moving cart which was rolled linearly, or they were rotated, unrestrained, atop a lazy Susan. A subset of animals was trained on a conditioned approach task (Figure S6A) in a Med Associates conditioning chamber fitted with a retractable spout containing a reward of 20% sucrose 10% chocolate milk powder (chamber dimensions: 29.53cm L, 23.5cm W, 27.21cm H). Animals were first familiarized with the chamber and allowed to explore and freely access the spout. Subsequently, animals were placed in the chamber and the spout was randomly inserted and retracted to habituate the animals to the spout’s movement. The task was as follows: an intertrial interval of a random duration (minimum 30 s, maximum 90 s) was followed by an 8 s period of sound and light cues. While the cues were on, the reward spout was inserted into the cage for 8 s. The cues were then turned off and the reward spout was retracted. Animals were run for 75 trials. Animals that were run on the conditioned approach task during the same recording period as the maze task were run at least 45min post completion of the maze task. For the interval between tasks, animals were placed in a ‘‘sleep box.’’ Recording was continuous throughout this period to allow tracking of the same cells throughout the maze task and conditioned approach task. When not being run, animals were housed in individual cages with enrichment and a 12hr light- 12hr dark light cycle. QUANTIFICATION AND STATISTICAL ANALYSIS Means are expressed as mean ± the standard error of the mean. Speed and Acceleration Analysis Position was sampled by overhead cameras at about 30Hz. Due to occlusion, sampling rates of position were more often at about 15Hz. Absolute value of velocity (speed) was determined by taking the hypotenuse of the coordinates of the point one before and one after the time point of interest. Speed was then smoothed using a Gaussian kernel of 6 samples and velocity was converted from pixels/s to cm/s. Acceleration (acc.) was found similarly by taking the hypotenuse of the surrounding speeds. Correlations with speed were found by first finding the animals’ occupancy per speed, dividing the speed into 2cm/s bins. Then, for each cell of interest, spike count as a function of speed was found. Spike count per speed was then divided by speed occupancy to result in firing rate as a function of speed. Speeds with less than 5% of total occupancy were excluded from analysis. Correlations with acc. were found similarly, but the absolute value was not taken so that correlations with negative and positive acc. were calculated separately, and acc. was divided into 5cm/s2 bins. All correlations were assessed using a linear regression to find an r2 value and the F-test to determine a p value of the linear fit. Decoding Acceleration and Speed Speed and acc. were decoded using a Bayesian decoding technique analogous to decoding of position using place cells [41]. x = velocities fi (x) = average firing rate per second for cell N at velocity X n = spikes per cell, ni = number of spikes in time window N = total number of cells t = time window of decoding C (t, n) = normalization factor for probabilities to equal 1 P (x j n) = odds of being at velocity x giving spiking n ! ! N N Y X n PðxjnÞ = Cðt; nÞ fi ðxÞ i exp
t fi ðxÞ i=1

i=1

Firing rate and speed or acc. were determined as the mean rate over 500ms bins, and this firing rate was used to decode speed or acc. For speed decoding, bins slid 250ms; for acc. decoding bins slid 125ms. For purposes of decoding, speed was binned into bins of 7cm/s up to 95% velocity occupancy (e.g., [0, 7, 14, 21, 28, 35], and acc. was binned into 14 cm/s2 bins also up to 95% acceleration occupancy (e.g., [-49,
35,
21,
7, 7, 21, 35, 49]). Accuracy of speed and acc. decoding was determined in two ways: First, the difference between actual and decoded speed or acc. was determined per time bin and compared to decoded shuffled data. Data were shuffled by randomly shuffling speed bins and re-decoding. Second, the maze was divided into 10cmX10cm squares and the animals’ actual speed or acc. was ranked at each location. The positions were then ranked based on decoded speed or acc., and compared using Spearman’s rho. Latencies between spiking data and speed and acc. were determined by shifting spike trains in 30ms intervals +300ms and
300ms. The mean of the error for shifted data was then plotted with standard error. Conditioned Approach Task Analysis Cue and reward firing rate were determined as the mean during the duration of the cue only period (8 s) and reward period (8 s), respectively (Figure 6A). Intertrial firing rate was calculated as the mean firing rate of on the 8 s prior to cue start. Fisher’s exact test was used to compare the numbers of cells that changed firing to cue and reward against the number of cells correlated to speed and acc. KS test was used to compare distribution of changes in firing rates across LS and hippocampal cells.

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Sharp Wave Ripple, Theta Rhythm, and Place Cell Analysis Sharp wave ripples were identified using a Blackman window to bandpass filter (100-300Hz) a signal from a tetrode in the CA1. The signal was then computed using a Hilbert transform, and a candidate ripple was identified when the LFP went 2.5 standard deviations above the mean. Ripple start time was determined as the time preceding the >2.5 increase returned to within half a standard deviation above the mean, and ripple end time was calculated similarly. Events were discarded if the animal had a velocity over 5cm/s or the event was less than 30ms or greater than 100ms. Ripple peak was identified as the maximum amplitude during a ripple. Cells that increased firing during ripples were determined by comparing mean firing rate 40ms around ripple peak (20ms before and 20ms after) with mean firing rate for 80ms, starting 200ms before ripple peak. Cells with more than a 20% change in firing were determined to be ripple-modulated. Theta modulation was determined by using a Blackman window to bandpass filter (4-12Hz) an LFP acquired from a CA1 tetrode. The signal was then computed using a Hilbert transform, and instantaneous phase was determined for each spike time. Spiking phase was adjusted by comparing LFP phase to hippocampal spiking in each session, with a phase of 0 assigned to maximum preferred hippocampal phase. Circular concentration kappa was computed using a circular statistics toolbox for MATLAB [99]. Cells were considered strongly theta-modulated if they had a kappa greater than 0.3. Kappa in the middle stem was done by trial. For each trial, the time the animal was in the middle stem was determined. Kappa was then calculated per cell per for any cell that spikes 6 or more times in that trial. A bits per spike measurement was used to determine if LS cells had spatial firing fields, and was calculated as follows: i = bin number Pi = occupancy probability for bin i Ri = mean firing rate at bin i R = overall mean firing rate bits per spike =

  X  R i  Ri log2 Pi R R i

Place cells centers were determined for cells with a bits/spike >0.8 by finding spike rate for 3cm bins along the track, smoothing rates with a Gaussian filter, and finding the coordinates of highest spiking. Odds ratios comparing all factors were determined using Fisher’s exact test. All odds ratios in Figure 7B were determined by comparing two variables at a time and then inserting the resulting ratio in the table. DATA AND CODE AVAILABILITY All analysis code is public on https://github.com/hsw28/data_analysis/.

Current Biology 29, 1–16.e1–e3, October 7, 2019 e3