Spatial working memory, the caching of behaviourally relevant spatial cues on a timescale of seconds, is a fundamental constituent of cognition. Although the prefrontal cortex and hippocampus are known to contribute jointly to successful spatial working memory, the anatomical pathway and temporal window for the interaction of these structures critical to spatial working memory has not yet been established. Here we find that direct hippocampal–prefrontal afferents are critical for encoding, but not for maintenance or retrieval, of spatial cues in mice. These cues are represented by the activity of individual prefrontal units in a manner that is dependent on hippocampal input only during the cue-encoding phase of a spatial working memory task. Successful encoding of these cues appears to be mediated by gamma-frequency synchrony between the two structures. These findings indicate a critical role for the direct hippocampal–prefrontal afferent pathway in the continuous updating of task-related spatial information during spatial working memory.
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The authors would like to thank M. Topiwala for technical assistance, M. Kheirbek for advice and assistance with the design of fibre optics, and M. Shapiro for advice with regards to designing the four-arm T-maze. This work was supported by grants from the National Institutes of Health (MH096274 and MH081968), the Hope for Depression Research Foundation, the International Mental Health Research Organization, the Gatsby Charitable Foundation and the Swartz Foundation.
The authors declare no competing financial interests.
Extended data figures and tables
a, Multiple individual units clustered from stereotrode recordings in the mPFC in the absence and presence of illumination. b, Mean waveforms of extracellular potentials from example units in a.
a, A raster plot of spikes fired by an example single unit across trials, sorted by sample goal, temporally aligned to arrival at sample goal. b, Traces of firing rates averaged across trials by sample goal location, for the unit from a. This unit shows location selectivity, firing preferentially in the back left goal. Traces are mean ± s.e.m. c, Spatial map of firing rates for the same unit for the full recording session. Goal-selective units tended to fire more at the preferred goal than at the other goals, and more at all goals than in the rest of the environment. d, Percentage of units that were goal-selective as a function of time from sample goal, according to two-way repeated measures ANOVAs performed on binned spike rates. Units were identified as having selectivity for left/right (blue), back/front (red), and/or combined spatial dimensions (green). Dashed line represents chance (P = 0.05). Inset, percentage of units having each type and/or combination of selectivity at time zero (arrival at sample goal). Percentages are out of 792 recorded units.
Extended Data Figure 3 mPFC units represent choice goal location, not sample goal location, during choice runs.
a, Model accuracy at the time bin corresponding with arrival at the sample goal port during the four-goal task was highest for spike histograms with time bins of 500 ms and 1,000 ms. Five-hundred-millisecond time bins were used for spike analyses. b, Decoding sample goal location during subsequent choice run during the four-goal task. Using the linear decoder, previously visited location was not detectable above chance accuracy. Ten- and twenty-second delay trials were combined. c, Decoding choice goal during choice run, correct versus incorrect trials during the four-goal task. Location decoded for this analysis was chosen goal (that is, the mouse’s current location) rather than correct goal. Model accuracy reached 0.93 upon arrival at the goal on correct trials. On incorrect trials, model accuracy exceeded chance during goal approach but dropped to chance levels upon reaching the goal. Ten- and twenty-second delay trials were combined. d, Decoding choice accuracy (correct versus incorrect) during choice trials. Model accuracy peaked at 0.99 at 1.9 s after arrival at the goal. b–d, Histograms were aligned to departure from start box. Ten- and twenty-second delay trials were combined. Data show mean ± 95% confidence intervals for b and d; s.e.m. for c and e.
a, Waveform features used to separate putative cell types. Spike duration was defined as the peak-to-trough time, while afterhyperpolarization (AHP) energy was taken as the area over the curve after the second zero-crossing. Spike duration yielded the clearest separation. b, Putative fast-spiking (FS) and non-FS cells, sorted by spike width, showed no effect of terminal illumination on spike rate (Arch– non-FS: sign rank z = −1.7, P = 0.095; Arch– FS: z = −1.6, P = 0.11; Arch+ non-FS: z = −2.7, P = 0.79; Arch+ FS: z = −0.49, P = 0.62).
a, Low-weighted units, as identified using the classifier, show no difference in firing between the goal with the highest weight relative to the other goals. In the sample goal these units fire at rates not different than their session mean rates. Traces indicate mean ± s.e.m. of normalized firing rate (bin FR − session FR). b, Terminal inhibition eliminates firing rate differences in preferred (Pref.) versus non-preferred (Other) goal during encoding across all units. On sample runs with no light, units from both Arch– (bottom left) and Arch+ animals (top left) had elevated firing rates in preferred goal relative to non-preferred goal (red asterisks mark time points with Bonferroni-corrected significance). In Sample Light runs, units from Arch– animals maintain elevated firing in the preferred goal (bottom right), while units from Arch+ animals show no significant firing rate difference (top right; N = 358 Arch– units, 325 Arch+ units, sign rank P < 0.0005).
a, vHPC units phase-lock maximally to the vHPC gamma rhythm at a lag of zero (P value from Rayleigh’s test < 0.05, dashed line indicates chance rate). b, Normalized PPC values, sorted by lag of maximal phase-locking, for significantly phase-locked vHPC units. Units with Bonferroni-corrected significance within the −40 to 40 ms lag window (Rayleigh test, P < 0.0029) were included. c, Mean normalized PPC value for the population shown in b. Shading is s.e.m. d, Histogram of units with maximum PPC value at each lag. Units maximally phase-locked at a lag of zero, with no net difference from zero across the population. e, vHPC units share a common preferred gamma phase. Pooled spikes from significantly phase-locked vHPC units were modulated by vHPC gamma phase at zero-lag (N = 26,303 spikes, Rayleigh’s z = 17.6, P = 2.2 × 10−8, PPC value = 0.002), with peak spiking in the descending phase of the gamma cycle. (Note that spikes and LFPs were both recorded from stereotrodes in the stratum pyramidale and that this gamma phase would probably differ from that recorded in SLM, as in Fig. 5).
a, Example vHPC LFP (blue, right) and spectrogram (left) demonstrating robust theta (grey, 4–12 Hz) and gamma (red, 30–70 Hz) components during all runs towards goals. b, Pseudocolour plot of relative strength of mPFC unit phase-locking to vHPC theta at lags from −200 ms to 200 ms, for units with Bonferroni-corrected significance in at least one lag. Warmer colours indicate stronger phase-locking. c, Distribution of lags at peak phase-locking strength for significantly phase-locked mPFC units. Distribution centred at 0 (N = 189 units, z = 2.05, P = 0.98). d, Mean ± s.e.m. PPC value of mPFC units and vHPC theta, as a function of lag. e–g, Phase-locking of mPFC units to dHPC theta as a function of lag, as in b–d. Distribution of lags at peak phase-locking is significantly shifted towards a dHPC lead (N = 160 units, sign rank z = −4.4, P = 6 × 10−6). h, No difference in strength of phase-locking of mPFC units to vHPC (left) and dHPC (right) theta in light on versus light off trials. Mean and s.e.m. shown for each (N = 140 units, sign rank z = −1.3, P = 0.2; z = −1.4, P = 0.12). i–k, Phase-locking of vHPC units to mPFC theta as a function of lag, as in b–d. Distribution of lags at peak phase-locking is significantly shifted towards an mPFC lead (N = 51 units, z = −5.03, P = 2.4 × 10−7).
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Spellman, T., Rigotti, M., Ahmari, S. et al. Hippocampal–prefrontal input supports spatial encoding in working memory. Nature 522, 309–314 (2015). https://doi.org/10.1038/nature14445
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