Self-location and navigation are crucial to daily life, and their disruption is particularly disabling. The hippocampus is central to this process as well as episodic memory. Hippocampal place cells are spatially selective, increasing their activity in specific locations. When external landmarks are scarce, place cells use self-motion (idiothetic) information to track location based on distance traveled (distance coding). Distance coding may be particularly relevant to episodic memory. However, the determinants of distance coding are poorly understood. Here, we used virtual reality, electrophysiological recordings in mice, and local cue manipulations to isolate and characterize hippocampal distance coding. In cue-poor conditions, a global distance coding scheme dominated hippocampal activity with high distance indices in all place cells, including both superficial and deep CA1 pyramidal cells. The mapping of distance onto a low-dimensional manifold and rigid distance relationships between place fields are compatible with attractor dynamics similar to those observed for grid cells. Inactivation of the medial septum (MS), which disrupts grid cells, significantly reduced both distance coding and rigid distance dynamics, suggesting an alteration (but not complete abolition) of the underlying attractor. By contrast, place cell coding was also influenced by local visual cues in cue-rich environments, notably deep CA1 pyramidal cells, and this persisted under MS inactivation. We propose that grid cells and associated rigid attractor dynamics predominantly contribute to hippocampal distance coding.

Animals navigate using cognitive maps of their environment, integrating external landmarks and distances between them. Hippocampal place cells are the neuronal substrate of these cognitive maps. However, while hippocampal allocentric position coding in reference to external landmarks is well characterized, the determinants of idiothetic hippocampal distance coding remain poorly understood. Using virtual reality, electrophysiological recordings in mice, and local cue manipulations we could dissociate distance from position coding. In the cue-poor condition, we found pervasive distance coding with high distance indices in all bidirectional place cells including both superficial and deep CA1 pyramidal cells. In this condition, the mapping of distance onto a low-dimensional manifold and rigid distance relationships between place fields suggested strong attractor dynamics similar to those observed for grid cells. Inactivation of the medial septum (MS), which disrupts grid cells, significantly reduced both distance coding and rigid distance dynamics, suggesting an alteration (but not complete abolition) of the underlying attractor. In contrast, allocentric position coding could be observed in cue-rich environments, predominantly engaged deep CA1 pyramidal cells, and persisted during MS inactivation. These results are consistent with a selective contribution of grid cells and associated rigid attractor dynamics to hippocampal idiothetic distance coding but not allocentric position coding. ### Competing Interest Statement The authors have declared no competing interest.

While rats pause to eat or rest during navigation tasks, neuronal sequences in the brain are replaying routes around moving obstacles, allowing the animals to reach their goals even in changing environments.

Article Figures and data Abstract Editor's evaluation eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic calcium spikes, or plateau potentials, has been reported to underlie place field formation in rodent hippocampal CA1 neurons. Here, we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience. Editor's evaluation This manuscript uses a combination of high-quality in vivo electrophysiology and modelling to demonstrate that Behavioural Time Scale Plasticity (BTSP) is bidirectional, and the amplitude and direction of this plasticity are dictated by the current weight of the inputs and not by the correlated activity of pairs of neurons. These findings challenge our current views on synaptic plasticity, which are primarily based on Hebb's concept. In addition, the network model used in this study demonstrates that this type of plasticity can rapidly reshape population activity to respond to environmental clues. This study will be of interest to the broad neuroscience audience and foster new ideas on biological and artificial learning. https://doi.org/10.7554/eLife.73046.sa0 Decision letter Reviews on Sciety eLife's review process eLife digest A new housing development in a familiar neighborhood, a wrong turn that ends up lengthening a Sunday stroll: our internal representation of the world requires constant updating, and we need to be able to associate events separated by long intervals of time to finetune future outcome. This often requires neural connections to be altered. A brain region known as the hippocampus is involved in building and maintaining a map of our environment. However, signals from other brain areas can activate silent neurons in the hippocampus when the body is in a specific location by triggering cellular events called dendritic calcium spikes. Milstein et al. explored whether dendritic calcium spikes in the hippocampus could also help the brain to update its map of the world by enabling neurons to stop being active at one location and to start responding at a new position. Experiments in mice showed that calcium spikes could change which features of the environment individual neurons respond to by strengthening or weaking connections between specific cells. Crucially, this mechanism allowed neurons to associate event sequences that unfold over a longer timescale that was more relevant to the ones encountered in day-to-day life. A computational model was then put together, and it demonstrated that dendritic calcium spikes in the hippocampus could enable the brain to make better spatial decisions in future. Indeed, these spikes are driven by inputs from brain regions involved in complex cognitive processes, potentially enabling the delayed outcomes of navigational choices to guide changes in the activity and wiring of neurons. Overall, the work by Milstein et al. advances the understanding of learning and memory in the brain and may inform the design of better systems for artificial learning. Introduction Activity-dependent changes in synaptic strength can flexibly alter the selectivity of neuronal firing, providing a cellular substrate for learning and memory. In the hippocampus, synaptic plasticity plays an important role in various forms of spatial and episodic learning and memory (Nakazawa et al., 2004). The spatial firing rates of hippocampal place cells have been shown to be modified by experience and by changes in environmental context or the locations of salient features (O'Keefe and Conway, 1978; Mehta et al., 1997; Lever et al., 2002; Dupret et al., 2010; Zaremba et al., 2017; Turi et al., 2019; Ziv et al., 2013; Muller and Kubie, 1987; Bostock et al., 1991; Fyhn et al., 2007; Leutgeb et al., 2005). These modifications can occur rapidly, even within a single trial (Hill, 1978; Mehta, 2015; Monaco et al., 2014; Bittner et al., 2015; Bittner et al., 2017; Diamantaki et al., 2018; Jezek et al., 2011; Geiller et al., 2017; Bourboulou et al., 2019; Zhao et al., 2020). Here, we investigate the synaptic plasticity mechanisms underlying such rapid changes in the spatial selectivity of hippocampal place cells. Various forms of Hebbian synaptic plasticity have been considered for decades to be the main, or even only, synaptic plasticity mechanisms present within most brain regions of a number of species (Magee and Grienberger, 2020). The core feature of such plasticity mechanisms is that they are autonomously driven by repeated synchronous activity between synaptically connected neurons, which results in either increases or decreases in synaptic strength depending on the exact temporal coincidence (Gerstner et al., 2018; Keck et al., 2017; Shouval et al., 2010; Song et al., 2000). This includes the so-called 'three-factor' plasticity rules that, in addition to pre- and postsynaptic activity, depend on a third factor that extends the time course over which plasticity can function (Magee and Grienberger, 2020; Gerstner et al., 2018; He et al., 2015; Yagishita et al., 2014). To implement these three-factor plasticity rules, it has been proposed that correlated pre- and postsynaptic activity drives the formation of a synaptic flag or eligibility trace (ET) that is then converted into changes in synaptic weights by the delayed third factor, usually a neuromodulatory signal (Gerstner et al., 2018; Sajikumar and Frey, 2004; Frey and Morris, 1997). Recently, we reported a potent, rapid form of synaptic plasticity in hippocampal CA1 pyramidal neurons that enables a de novo place field to be generated in a single trial following a dendritic calcium spike (also called a plateau potential) (Bittner et al., 2015; Bittner et al., 2017; Diamantaki et al., 2018). This form of synaptic plasticity, termed behavioral timescale synaptic plasticity (BTSP), rapidly modifies synaptic inputs active within a seconds-long time window around the plateau potential. This relatively long time course suggests that BTSP may be similar to the above-mentioned three-factor forms of plasticity, with synaptic activity generating local signals marking synapses as eligible for plasticity (ETs), and plateau potentials acting as the delayed factor that converts synaptic ETs into changes in synaptic strength. However, BTSP was shown to strengthen many synaptic inputs whose activation did not coincide with any postsynaptic spiking or even subthreshold depolarization detected at the soma (Bittner et al., 2017), suggesting that changes in synaptic weight might be independent of correlated pre- and postsynaptic activity, and that BTSP may be fundamentally different than all variants of Hebbian synaptic plasticity (Gerstner et al., 2018; Keck et al., 2017; Shouval et al., 2010; Mehta, 2004; Golding et al., 2002). Such a non-standard plasticity rule could enable learning to be guided by delayed behavioral outcomes, rather than by short timescale associations of pre- and postsynaptic activity. In this study, we tested the effect of dendritic plateau potentials on the spatial selectivity of CA1 neurons that already express pre-existing place fields, and therefore exhibit substantial postsynaptic depolarization and spiking prior to plasticity induction. We found that dendritic plateau potentials rapidly translocate the place field position of hippocampal place cells, both by strengthening inputs active near the plateau position and weakening inputs active within the original place field. In order to determine if the increased postsynaptic activity in place cells is causally related to the synaptic depression observed within the initial place field, we performed a series of voltage perturbation experiments, which indicated that the direction of plasticity induced by plateau potentials is independent of postsynaptic depolarization and spiking. Next, we inferred from the data a computational model of the synaptic learning rule underlying this bidirectional form of plasticity, which suggested that it is instead the current weight of each synaptic input that controls the direction of plasticity such that weak inputs potentiate and strong inputs depress. Finally, we implemented this weight-dependent learning rule in a network model to explore the capabilities of bidirectional BTSP to adapt network-level population representations to changes in the environment. Results Plateau potentials translocate existing place fields We first examined how plasticity induced by dendritic plateau potentials changes the intracellular membrane potential (Vm) dynamics in neurons already exhibiting location-specific firing (i.e. place cells). Intracellular voltage recordings from CA1 pyramidal neurons were established in head-fixed mice trained to run for a water reward on a circular treadmill decorated with visual and tactile cues to distinguish spatial positions (~185 cm in length). Brief step currents (700 pA, 300 ms) were injected through the intracellular electrode for a small number (Nakazawa et al., 2004; O'Keefe and Conway, 1978; Mehta et al., 1997; Lever et al., 2002; Dupret et al., 2010; Zaremba et al., 2017; Turi et al., 2019; Ziv et al., 2013) of consecutive laps to evoke plateau potentials at a second location that was between 0 and 150 cm from the initial place field (labeled 'Induction 2' in Figure 1A and B; n = 26 plasticity inductions in 24 neurons). In 8/24 neurons a 'natural' pre-existing place field was expressed from the start of recording, while in 16/24 the initial place field was first experimentally induced by the same procedure (labeled 'Induction 1' in Figure 1A and B). In 2/24 neurons the induction procedure was repeated a third time with plateaus evoked at a different location, resulting in a total of 26 plasticity inductions in cells with pre-existing place fields (see Figure 1—figure supplement 1E and Materials and methods). Figure 1 with 2 supplements see all Download asset Open asset Dendritic plateau potentials translocate hippocampal place fields. (A) Spatial firing of a CA1 pyramidal cell recorded intracellularly from a mouse running laps on a circular treadmill. Dendritic plateau potentials evoked by intracellular current injection first induce a place field at ~120 cm (Induction 1), then induce a second place field at ~10 cm and suppress the first place field (Induction 2). (B) Intracellular Vm traces from individual laps in (A). (C) Spatially binned Vm ramp depolarizations averaged across 10 laps before (gray) and after (black, blue, green, red) the second induction (100 spatial bins). Dashed lines and red triangles mark the average locations of evoked plateaus, black open triangles mark the location of the initial Vm ramp peak, and blue triangles indicate the position of the peak of the new place field. (D) Data from all cells were sorted by position of initial place field. Black line indicates location of initial peak, blue triangles indicate the position of the peak of the new place field, and red triangles the position of the plateau. Neurons in (C) are indicated by like colored arrows. (E) The distance between the new place field and the initial place field vs. the distance between the plateau and the initial place field (p = 0.000015; two-tailed null hypothesis test; explained variance [R2] computed by Pearson's correlation). Red line is unity. In most cases the evoked dendritic plateaus shifted the location of the neuron's pre-existing place field toward the position of the second induction site (Figure 1A and B). Place field firing is known to be driven by a slow, ramping depolarization of Vm from sub- to supra-threshold levels (Bittner et al., 2015; Harvey et al., 2009). Isolation of these low-pass filtered Vm ramps (Figure 1—figure supplement 2A-H; Materials and methods) revealed that plateau potentials likewise shifted the neuron's Vm ramp toward the position of the plateau, such that the new Vm ramp peaked near the plateau position in most neurons (average distance = 19.5 ± 4.7 cm; n = 26; Figure 1C–E and Figure 1—figure supplement 2; example cells shown in Figure 1C are indicated with matching colored arrows in Figure 1D). We also observed similar shifts in place field position to be induced by spontaneous, naturally occurring plateau potentials in a separate set of recordings (n = 5; Figure 1—figure supplement 2I-M). Spatial extent of Vm plasticity The spatial profile of plateau-induced Vm changes (ΔVm) (Figure 2A) was obtained by subtracting the average Vm ramp for trials occurring before plateau initiation (Figure 1C; before) from the average Vm ramp for trials occurring after (Figure 1C; after). These data indicate that plateaus induced both positive and negative changes to Vm ramp amplitude (Figure 2A and B). In general, the increases in Vm depolarization peaked near the position of the plateau, while the negative changes peaked near the initial place field (Figure 2A and B, and Figure 2—figure supplement 1A and B). Although these changes varied considerably in magnitude across cells, the peak change in the positive direction was greater than the peak change in the negative direction (mean positive change± SEM vs. mean negative change± SEM: 6.73 ± 0.73 mV vs. 3.89 ± 0.32 mV, n = 26 inductions; p = 0.0001, paired two-way Student's t-test; Figure 2—figure supplement 1A). Aligning each ΔVm trace to the position of the plateau (Figure 2A and B) demonstrates that the increases in Vm depolarization observed near the plateau position decay with distance, eventually becoming hyperpolarizing decreases in Vm. At even greater distances from a plateau, ΔVm decays back to zero (Figure 2B). To summarize the data presented thus far, dendritic plateau potentials change the location of place field firing by depolarizing Vm around the plateau position and hyperpolarizing Vm at positions within a pre-existing place field. Figure 2 with 1 supplement see all Download asset Open asset Spatial and temporal profiles of plateau-induced change in Vm. (A) Difference between spatially binned Vm ramp depolarizations averaged across laps after the second induction and those averaged across laps before the second induction. Same example traces as shown in Figure 1C. Red triangles and dashed line indicate plateau location. Open triangles are locations of initial Vm ramp peaks. Traces have been smoothed using a five point boxcar average. (B) All change in Vm traces (ΔVm, not smoothed) from individual neurons (gray) and averaged across cells (black). (C) The running profile of the mice during the plateau induction trials plotted as time from plateau initiation vs. spatial location (100 bins). This indicates the temporal distance of the mouse from the plateau position at any given spatial position and is used as a time base in (E) and (F). (D) Spatial Vm ramp half-width, calculated as distance from plateau position to the final decay of ΔVm in a single direction, vs. the average running speed of the mouse during the induction trials calculated from traces shown in (C). Individual symbols for examples shown in (A) are correspondingly colored. Gray line is linear fit (p = 1.8e-06, two-tailed null hypothesis test; explained variance (R2) computed by Pearson's correlation). (E) Change in Vm traces (ΔVm) using the time base shown in (C). Traces have been smoothed using a five-point boxcar average. (F) All change in Vm traces (ΔVm, not smoothed) from individual neurons (gray) and averaged across cells (red). Black crosses and circles indicate the 10% peak amplitude times used to calculate the asymmetry of positive changes (left/right potentiation ratio). Time dependence of Vm plasticity Previously we showed that location-specific increases in Vm depolarization induced by plateau potentials are the result of synapse-specific increases in the strength of spatially tuned excitatory inputs (Bittner et al., 2017). The above results suggest that, in addition to this synaptic potentiation, BTSP is also capable of inducing synaptic depression to cause location-specific decreases in Vm depolarization. In analyzing the spatial extent of the Vm changes induced by plateaus, we observed a strong linear relationship between the width of the resulting ΔVm and the running speed of the animal during plateau induction laps (Figure 2C and D), which had a slope on the order of seconds. This suggested that the run trajectory of the animal (Figure 2C) affected the spatial extent of the plasticity (Figure 2A and B) by determining which positions were traversed within a fixed seconds-long temporal window for plasticity, as we previously reported (Bittner et al., 2017). Therefore, we next analyzed the temporal relationship between plateau potentials and location-specific potentiation and depression. To do this, we used the running trajectory of the mice during plateau induction trials (Figure 2C) as a time base for ΔVm (Figure 2E; see also Figure 1—figure supplement 1, Figure 1—figure supplement 2A-H and Materials and methods). This analysis showed that the positive and negative changes to Vm induced in place cells occurred over a timescale of multiple seconds (Figure 2F), with the positive changes appearing to be asymmetric with respect to the onset time of the plateaus (ratio of potentiation duration before/after plateau onset: 2.2; black circles and crossmarks in Figure 2F mark the time points when ΔVm crosses zero). This asymmetry was similar to that observed for the positive Vm changes induced by BTSP in silent cells (Bittner et al., 2017). The negative changes (i.e. the hyperpolarizations indicative of synaptic depression) occurred within a time window between ±2 and ±6 s from the plateau in many neurons that expressed pre-existing place fields (Figure 2E and F). Notably, this hyperpolarization was greatly reduced, or even absent, in a set of place cells where the time delay between plateau onset and the initial place field Vm ramp was greater than 4–5 s (red traces in Figures 1C, 2A and E; see also Figure 2—figure supplement 1C-F), further indicating the time delimited aspect of the depression component. These data reinforce the idea that BTSP is a bidirectional form of synaptic plasticity with a seconds-long timescale that enables dendritic plateau potentials to shift the locations of hippocampal place fields by inducing both synaptic potentiation and depression. Plasticity drives Vm towards a target shape with an apparent inverse dependence on initial Vm We next sought to understand why dendritic plateaus induce both Vm depolarization and Vm hyperpolarization in cells expressing pre-existing place fields (Figures 1 and 2), but induce only Vm depolarization in spatially untuned silent cells (Figure 3—figure supplement 1; Bittner et al., 2017). Figure 3A shows that the initial temporal profile of Vm in place cells with pre-existing place fields was highly variable across neurons, as plateaus were experimentally induced at different temporal intervals from the existing place field in different neurons. In contrast, the change in Vm (ΔVm) induced by plateaus showed a more consistent shape in time that appeared to depend on the initial level of Vm depolarization at each time point prior to plasticity (Figure 3B). Large positive changes occurred at time points with relatively hyperpolarized initial Vm, while time points with more depolarized initial Vm were associated with less positive and more negative ΔVm. These changes resulted in final Vm profiles that were highly similar across neurons, regardless of the initial Vm (Figure 3C). These results indicate that BTSP induces variable changes in synaptic strength that reshape the selectivity of neurons toward a common target shape – a place field centered near the location of evoked plateau potentials that decays toward baseline over many seconds in each direction. Figure 3 with 1 supplement see all Download asset Open asset Vm ramp plasticity varies with both time delay from plateau onset and initial Vm depolarization. (A) Temporal profile of initial Vm before plasticity for inductions in neurons with pre-existing place fields (26 inductions from 24 place cells), aligned to the onset time of evoked plateau potentials. (B) Temporal profile of changes in Vm (ΔVm) induced by plasticity in all place cells. Each ΔVm trace is color-coded by initial Vm. See inset color scale in (C). (C) Temporal profile of final Vm after plasticity in all place cells. Each Vm trace is color-coded by initial Vm (color scale inset). (D) The temporal profiles of initial Vm before plasticity are averaged across cells and three conditions are compared: silent cells without pre-existing place fields (black), place cells (dark red), and a subset of data from each place cell at time points when each cell was more depolarized than –56 mV within its place field (light red). Shading indicates SEM across cells. (E) The temporal profiles of changes in Vm (ΔVm) induced by plasticity are averaged across cells and the three conditions from (D) are compared. Shading indicates SEM across cells. (F) The temporal profiles of final Vm after plasticity are averaged across cells and the three conditions from (D) are compared. Shading indicates SEM across cells. (G) Change in Vm ramp (ΔVm) plotted against initial Vm for all inductions in neurons with pre-existing place fields. Black line is linear fit and correlation coefficient shown (p < 0.00001, two-tailed null hypothesis test; explained variance (R2) computed by Pearson's correlation). (H) Final Vm ramp after plasticity plotted against initial Vm before plasticity for all inductions in neurons with pre-existing place fields. Black line is linear fit and correlation coefficient shown (p < 0.014, two-tailed null hypothesis test; explained variance (R2) computed by Pearson's correlation). (I) Heatmap of changes in Vm ramp (ΔVm) as a function of both time and initial Vm (see Materials and methods). Arrows indicate that the variable direction of plasticity serves to drive Vm toward the target equilibrium region (white). (J) Diagram depicts presynaptic spatial firing rates of a population of CA3 inputs to a postsynaptic CA1 neuron (top), the synaptic weights of those inputs before and after plasticity (middle), and the resulting postsynaptic Vm ramp, which reflects a weighted summation of the inputs. Traces are shown before (gray) and after (black) plasticity induction in a silent cell (Induction 1), and after a subsequent induction of plasticity (Induction 2, cyan) that translocates the position of the cell's place field. (K) Table compares predicted outcomes of voltage perturbation experiments (depolarizing a silent cell, or hyperpolarizing a place cell), considering two possible forms of behavioral timescale synaptic plasticity (BTSP) (depends on initial Vm, or depends on initial synaptic weights). In Figure 3D-F, we examined this further by comparing data from initially hyperpolarized silent cells (black; n = 29 inductions, see Figure 3—figure supplement 1 and Materials and methods) to data from place cells (dark red; n = 26 inductions). Place cells were on average more depolarized before plasticity than silent cells (Figure 3D), and more depression occurred in place cells compared to silent cells (Figure 3E). However, each place cell had both spatial positions where it was depolarized within its place field, and positions where it was hyperpolarized out-of-field. To determine if spatial positions that were initially depolarized were associated with larger depression, we grouped Vm ramp data from all place cells, considering only spatial bins where each cell was more depolarized than a threshold of –56 mV (light red traces labeled 'PCs (within-field)' in Figure 3D-F). Indeed, more depression and less potentiation was induced in place cells at those spatial positions that were initially most depolarized (Figure 3E). However, the final Vm ramps after plasticity were less sensitive to the initial state of depolarization across spatial bins of place cells (Figure 3F). This analysis further supported the findings that, while changes in Vm induced by plateaus were highly dependent on initial Vm, these changes drove the resulting final Vm ramp toward a common target shape (Figure 3F). Indeed, when all spatial bins from all place cells were analyzed, ΔVm showed a strong inverse correlation with initial Vm (m = –0.91; Figure 3G). In contrast, final Vm showed a very weak positive correlation with initial Vm (m = 0.04; Figure 3H), which reflects that some spatial bins show no change in Vm during plasticity, either because they were traversed outside the temporal window for plasticity or because the Vm at those positions had already reached a final Vm target value. That BTSP induces variable changes in Vm that reshape the Vm ramp toward a particular target shape is further evident from a heatmap depicting the relationships of ΔVm to both initial Vm ramp depolarization and time from plateau onset (Figure 3I, positive ΔVm in red, and negative ΔVm in blue; see Materials and methods). The white regions of this plot trace out a temporal profile of Vm that corresponds to the final target place field shapes shown in Figure 3C and F. All initial deviations from this equilibrium Vm profile resulted in either positive or negative changes to approach this target place field shape (see dashed arrows). It should also be noted that the depression of Vm in place cells appeared to be weaker than the potentiation, leaving some residual depolarization at positions distant from the peak (Figure 3F). The functional significance of this is unclear, but may suggest that BTSP induces synaptic depression at a slower rate than potentiation (Cone and Shouval, 2021). To summarize, BTSP induces precise changes in synaptic strength that modify pre-existing place fields with any initial shape such that they approach a target shape that peaks near the location where dendritic plateaus were evoked. Dependence on initial Vm vs. initial synaptic weights Altogether these data revealed that, in general, the magnitude and direction of ΔVm depended on the time from the plateau potential, and correlated inversely with the initial Vm ramp amplitude prior to plasticity induction. Does this anti-correlation reflect a causal relationship between postsynaptic depolarization and changes in synaptic weight induced by BTSP? This possibility would require that small depolarizations induce synaptic potentiation and large depolarizations induce synaptic depression, which is actually opposite to what has been observed in CA1 pyramidal cells with a variety of other plasticity protocols (Shouval et al., 2010; Yang et al., 1999; Graupner and Brunel, 2012; Clopath et al., 2010; Clopath and Gerstner, 2010; Jedlicka et al., 2015). Furthermore, the increased Vm depolarization within a cell's place field also reflects the activation of strongly weighted synaptic inputs, which have been potentiated by prior plasticity (Bittner et al., 2015; Bittner et al., 2017; Figure 3J). Thus, a causal dependency on either Vm or synaptic weight could explain the data so far. To discriminate between these two possibilities, we next devised a set of voltage perturbation experiments. We reasoned that, if increased depolarization and spiking within a cell's place field causes synaptic depression, then artificially increasing Vm and inducing spiking in otherwise silent cells would cause plateau potentials to induce negative ΔVm. Likewise, artificially decreasing Vm and preventing spiking in place cells would prevent plateau potentials from inducing negative ΔVm (Figure 3K). On the contrary, if the direction of plasticity depended instead on the initial strengths of synapses prior to plasticity, these voltage manipulations would have no effect on the balance between positive and negative ΔVm (Figure 3K). It is important to note that these somatic voltage manipulations are not expected to strictly control or even completely overwhelm Vm at the synaptic sites relevant to plasticity induction (Magee and Johnston, 1997; Koester and Sakmann, 1998; Froemke et al., 2005) due to attenuation of current and voltage along the dendritic cable (Magee, 1998; Golding et al., 2005), and compartmentalization of synaptic voltage in dendritic spines (Harnett et al., 2012). However, by either increasing or decreasing the generation of somatic action potentials, this manipulation will unequivocally alter the number of action potentials that back-propagate into dendrites, which will in turn influence the activation of voltage-gated channels in dendrites and spines (e.g. Na+ channels, Ca2+ channels, and NMDA-Rs) (Magee and Johnston, 1997; Takahashi and Magee, 2009). Expected changes to the mean Vm in active dendritic spines were supported by simulations of a biophysically a

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract The hippocampal dentate gyrus is an important relay conveying sensory information from the entorhinal cortex to the hippocampus proper. During exploration, the dentate gyrus has been proposed to act as a pattern separator. However, the dentate gyrus also shows structured activity during immobility and sleep. The properties of these activity patterns at cellular resolution, and their role in hippocampal-dependent memory processes have remained unclear. Using dual-color in vivo two-photon Ca2+ imaging, we show that in immobile mice dentate granule cells generate sparse, synchronized activity patterns associated with entorhinal cortex activation. These population events are structured and modified by changes in the environment; and they incorporate place- and speed cells. Importantly, they are more similar than expected by chance to population patterns evoked during self-motion. Using optogenetic inhibition, we show that granule cell activity is not only required during exploration, but also during immobility in order to form dentate gyrus-dependent spatial memories. Introduction The dentate gyrus receives polymodal sensory information from the entorhinal cortex, and relays it into the hippocampal network. The most prevalent view of the dentate gyrus input-output transformation in this circuit is that it acts as a pattern separator. This capability requires the animal to generate dissimilar neuronal representations from overlapping input states that represent similar but not identical environments (Cayco-Gajic and Silver, 2019). Such an operation, termed pattern separation, has been ascribed to the hippocampal dentate gyrus in species ranging from rodents to humans (Berron et al., 2016; Leutgeb et al., 2007; Sakon and Suzuki, 2019). In the dentate gyrus, polysensory inputs are mapped onto a large number of granule cells which exhibit extremely sparse firing patterns, resulting in a high probability of non-overlapping output patterns (Danielson et al., 2016; GoodSmith et al., 2017; Hainmueller and Bartos, 2018; Pilz et al., 2016; Senzai and Buzsáki, 2017; van Dijk and Fenton, 2018). This concept has been influential in understanding dentate gyrus function when processing multimodal, current, or 'online' sensory information during mobility and exploration. However, the dentate gyrus is far from silent during immobility. It displays prominent electrographic activity patterns such as dentate spikes and sharp waves, which occur primarily during immobility or sleep (Bragin et al., 1995; Meier et al., 2020; Penttonen et al., 1997). In downstream hippocampal regions such as CA1, neuronal activity during immobility incorporates the replay of behaviorally relevant sequences during sharp wave ripples, a process important in memory consolidation (Davidson et al., 2009; Diba and Buzsáki, 2007; Dupret et al., 2010; Foster and Wilson, 2006; Girardeau et al., 2009; Malvache et al., 2016; Skaggs and McNaughton, 1996; Wilson and McNaughton, 1994). In the dentate gyrus, little detail is known about how granule cells are active during immobility at the population level, and it is unknown whether activity during immobility reiterates behaviorally relevant information. Moreover, the role of dentate gyrus activity during immobility in memory formation is unclear. Here, we have used dual-color two-photon in-vivo Ca2+ imaging to show that in immobile mice, the dentate gyrus exhibits frequent, sparse, and synchronous population events that at the population level are similar to activity patterns during locomotion. Moreover, we have tested the idea that dentate gyrus activity during immobility is relevant for dentate-gyrus dependent spatial memory. Results Sparse, structured dentate network events in immobile animals We imaged the activity of large populations of hippocampal dentate granule cells (GCs) using a Thy1-GCaMP6s mouse line (GP4.12Dkim/J, Dana et al., 2014). In addition, we monitored the bulk activity of the major input system into the dentate gyrus, the medial perforant path (MPP). To this end, we expressed the red-shifted Ca2+ indicator jRGECO1a (Dana et al., 2016) in the medial entorhinal cortex using viral gene transfer (see Materials and methods section, Figure 1A, Figure 1—figure supplement 1A). To allow efficient excitation of both genetically encoded Ca2+ indicators, we established excitation with two pulsed laser sources at 940 and 1070 nm (see Figure 1—figure supplement 1B–F). The mice were placed under a two-photon microscope and ran on different variants of a linear track, equipped with different types of cues (see Figure 1—figure supplement 1G–J, Figure 1—video 1). Figure 1 with 5 supplements see all Download asset Open asset Synchronous dentate granule cell activations, 'network events', occur preferentially during immobility. (A) Expression of GCaMP6s in granule cells (Thy1-GCaMP mouse line, GP4.12Dkim/J). jRGECO was expressed in medial entorhinal cortex neurons using rAAV mediated gene transfer, and is visible in the middle molecular layer (MML) corresponding to the medial perforant path (MPP). Upper panel: Post hoc analysis in 70 µm fixed slice. Nuclei stained with DAPI (blue). Lower panel: Imaging plane for simultaneous recording of MPP bulk and individual GC activity. Scale bar 100 µm. (B) Data of representative recording session. Bulk fluorescence signal of MPP fibers (red), extracted fluorescence signals from a subset of individual GCs (black), mouse position on linear track (blue) and diameter of mouse pupil (black). (C) Participation of granule cells in synchronous network events. Representative field of view with highlighted simultaneously active GCs. Cells active during an individual network event are depicted in the same color. A subset of neurons is active in multiple network events, recognizable as white color. (D) Raster plot of network events. Dashed lines mark network events, corresponding to simultaneous activity of > four cells. Participating cells are highlighted according to color scheme from panel C. Running speed is depicted (blue) to distinguish running and resting periods. (E) Fluorescence transients of participating granule cells from NEs in panel D. Color scheme corresponds to panel C and D. Each column shows all respective transients of the respective synchronous ensemble. Shown is a time window of ±1 s around each NE. Vertical scale bars correspond to 100% ΔF/F. (F) Mean number of network events per twenty-minute recording session during running and resting. Network events occurred mainly during immobility (repeated measures ANOVA, F(1,8)=71.80, p=2×10−7, n = 9 animals, three sessions). Gray bars depict shuffled data for each condition (n = 9 animals). (G) Average number of identified network events plotted against different thresholds for the size of network events in terms of numbers of synchronously active cells (green line, shaded area depicts SEM, n = 3 mice, three sessions). Shuffled data null-distribution is created by randomly shuffling event times for every individual cell (gray line, shaded area depicts SEM). (H) Frequencies of network events calculated from equal time intervals for locomotion and immobility (Repeated measures ANOVA, F(1,8) = 117.28, p=2×10−6). (I) Cumulative probability distribution of network event occurrence during the entire twenty-minute session for individual sessions (gray lines) and the pooled sessions (Green line, n = 9 mice, three sessions). (J) Pooled cumulative probability distribution of network event occurrence during resting periods. All resting periods of one session that were longer than 5 s were normalized to their length (n = 9 mice, three sessions). As previously described, the firing of GCs was generally sparse (Danielson et al., 2016; Hainmueller and Bartos, 2018; Neunuebel and Knierim, 2012; Pilz et al., 2016), both when animals were immobile and when they were running on a textured belt without additional cues (mean event frequency 1.38 ± 0.19 events/min and 0.97 ± 0.2 events/min, respectively, n = 1415 granule cells in nine mice, Figure 1B, Figure 1—figure supplement 2C–E). Despite the sparse activity of granule cells, we observed synchronized activity patterns (Figure 1—video 2). To rigorously define such events, we used an algorithm that detects synchronized network events within a 200 ms time window, corresponding to 1 ± 1 frame at our sampling rate (see Materials and methods). Such synchronous network events could readily be observed in the dentate gyrus in all mice (Figure 1C,D, network events depicted in different colors, see examples for ΔF/F traces of participating cells in Figure 1E). Network events were sparse, incorporating on average only 5.7 ± 0.09% of the total active GC population. Shuffling analysis (see Materials and methods) confirmed that network events do not arise by chance (Figure 1F, gray bars correspond to shuffled data, n = 9 mice, three sessions/mouse). This was robust over three different types of shuffling analysis (Figure 1G, Figure 1—figure supplement 3 and Materials and methods for the description of the shuffling methods). Notably, network events occurred mainly during immobility periods and were much less prevalent during running (Figure 1D, quantification in Figure 1G). Accordingly, network event frequencies were significantly higher during immobility (repeated measures ANOVA, F(1,8)=117, p=2×10−6, n = 9 mice, three sessions, Figure 1H). During immobility periods (defined as running speeds < 4 cm/s), the vast majority of (99.6%) network events occurred when mice were completely immobile. Network events were on average evenly distributed during the 20 min imaging session (Figure 1I), as well as during individual periods of immobility (Figure 1J). Dentate network events are correlated with MPP activation We then analyzed the activity in the MPP input fiber tract expressing jRGECO, and probed the relation of this activity with GC activity patterns. As expected during exploratory states, the bulk MPP activity was increased during locomotion, consistent with increased sensory input (Figure 2A,B, red channel, Figure 2C for average value). During immobility in particular, larger fluctuations of bulk MPP activity were observed. This phenomenon was reflected in a larger variance of the bulk MPP signal during immobility (Figure 2D). Cross-correlation revealed that during immobility, the increases in bulk MPP activity were associated with peaks in average GC activity levels (Figure 2E). Both signals were significantly correlated in most sessions for periods of immobility (8/9 sessions, n = 3 mice, three sessions per mouse, Granger causality test p<0.05). This correlation was clearly visible during network events, because aligning GC activity and MPP activity to the timepoint of network events revealed a strong coactivation of GC and MPP during network events (Figure 2F, n = 3 mice). During running, MPP signals did not correlate with average GC activity, which is not unexpected given the asynchronous activation of GCs during running. 8/9 sessions, Granger causality test p>0.05. Figure 2 Download asset Open asset Granule cell and MPP activity during locomotion and immobility. (A) Mean MPP activity (red) and the sum of all granule cell activities (green) for a representative section of a recording session in an individual mouse. Dashed lines mark transition between resting and running periods (see blue line indicating running speed). (B) Average MPP fluorescence and running speeds, both aligned to running onsets (dashed line). Shaded areas indicate standard error (n = 4 mice, one mouse had only jRGECO expression in MPP, but no granule cell signal, three sessions per mouse). (C) Mean fluorescence averaged during resting (dark red) and running (light red). Asterisk indicates 5% significance threshold (repeated measures ANOVA, F(1,3) = 7,86, p=0.032, n = 4 mice, three sessions). (D) Variance of MMP bulk signal during resting (dark red) and running (light red, n = 4). Asterisk indicates results of repeated measures ANOVA, F(1,3) = 7,07, p=0.037, n = 4 mice, three sessions. (E) Cross-correlation of MPP bulk signal and summed GC signal during resting. Shaded gray area indicates standard error. (F) Average MPP activity (red) and probability of granule cell activity across all mice, three baseline sessions each, both aligned to the time point of network events (n = 3 mice, 2008 network events, shaded red area depicts SEM). (G) Amplitudes of deconvolved events in MPP bulk data during locomotion (light red) und resting (dark red). (H) Frequencies of all deconvolved MPP bulk events during locomotion and immobility (repeated measures ANOVA, F(1,2) = 255, p=3×10−6, n = 3 mice, three sessions). (I) Frequencies of deconvolved events with amplitudes above two standard deviations (repeated measures ANOVA, F(1,2) = 27, p=2×10−3, n = 3 mice, three sessions). (J) Delay of NEs to the closest identified MPP event. To explore in more detail how individual fluctuations in MPP bulk activity are associated with GC activity, we used a deconvolution algorithm to identify synchronous activity of MPP axons visible in the bulk MPP transients (see Materials and methods). We then quantified amplitude and frequency of these transients during locomotion and immobility. First, we found that bulk MPP events detected during immobility are on average larger than those detected during running (Figure 2G, Kruskal-Wallis test, n = 3 mice, three sessions per mouse, 8469 and 19,106 events during running and resting, respectively, p=2×10−44), in agreement with the larger variance of the MPP signal during these periods. When we examined the frequencies of all detected MPP bulk events during locomotion and immobility, we found that there were significantly more events during running (repeated measures ANOVA, F(1,2)=255, p=3×10−6, n = 3 mice, three sessions, Figure 2H). However, large events, defined as bulk MPP events with amplitudes above two standard deviations of the mean, were significantly more frequent during resting states (repeated measures ANOVA, F(1,2)=27, p=2×10−3, n = 3 mice, three sessions, Figure 2I). Again, this is consistent with the larger variance of the MPP signal during immobility, and likely reflects synchronized activation of MPP fibers. In line with the correlation of MPP and GC signals, there was a short temporal delay between individual bulk MPP transients and network events (Figure 2J). Dentate network events are correlated with pupil constriction Pupil diameter is an indicator of neuronal state and arousal (Reimer et al., 2014; Reimer et al., 2016), and can be used to track changes in neuronal states during quiet wakefulness (Reimer et al., 2014). Of note, pupil changes have been shown to closely track the rate of occurrence of hippocampal synchronous activity, namely sharp waves in the hippocampal CA1 region (McGinley et al., 2015). We therefore asked if dentate network events are also associated with specific changes in pupil diameter (Figure 3A for example measurement of pupil diameter over multiple resting and locomotor states). As previously described (Reimer et al., 2014; Reimer et al., 2016), we found pupil constriction during immobility with dilation at locomotion onsets (Figure 3B,C, n = 6 mice, three sessions). Intriguingly, the average pupil diameters during network events were significantly more constricted compared to the average pupil diameters during entire periods of immobility (Figure 3D, repeated measures ANOVA for all three groups F(2,28)=17.17, p=1×10−5, n = 6, data from three sessions each, Bonferroni post-tests: pupil diameters during locomotion vs. immobility p=0.0068, locomotion vs. network events p=0.0016, immobility vs. network events p=0.0017). Figure 3 Download asset Open asset Pupil dynamics during network events. (A) Representative example of pupil size measurements during different locomotor states. Green dots indicate timepoints of network events. (B, C) Average pupil diameters (gray lines) aligned to locomotion onsets (B) or offsets (C, blue lines) reveals pupil dilation at locomotion onsets, and constriction during locomotion offset. Shaded areas indicate standard error. GC activity stays on baseline value during change of behavioral state (green). (D) Average pupil diameters during locomotion, during resting periods, and during network events. Asterisks indicate significant Bonferroni post-test at 5% level. (E) Average rate of pupil diameter change during locomotion, during resting periods, and during network events. Asterisks indicate significant Bonferroni post-test at 5% level. (F) Averaging pupil diameters aligned to NE times (green bars) reveals pupil constriction during NEs. Shaded areas indicate standard error. (n = 6 mice, three sessions). (n = 6 mice, three sessions). When looking at pupillary dynamics by assessing the rate of diameter change, locomotor episodes were on average associated with pupil dilation, while network events were specifically associated with pupil constriction (Figure 3E, repeated measures ANOVA F(2,28)=34.18, p=3×10−8, n = 6 mice, data from three sessions each, Bonferroni post-tests: pupil diameters during locomotion vs. immobility p=0.0016, locomotion vs. network events p=2.53×10−5, immobility vs. network events p=0.00053). The latter finding was clearly illustrated by averaging pupil diameters aligned to NE times (Figure 3F). Together, this suggests that network events are associated with specific pupillary dynamics reflecting substates of arousal and neuronal synchronization during immobility. Network events are more orthogonal than expected by chance, but repetitively recruit GC sub-ensembles We then further characterized the participation of dentate granule cells in network events. We first asked to what extent individual network events recruit orthogonal cell populations. Indeed, while individual GCs can partake in multiple network events (see Figure 1C,D, Figure 1—video 2), we also observed network events that seemed completely distinct to others. To quantify how similar network events are to one another, we computed population vectors for each network event. We then computed the cosine similarity as a measure of similarity between vectors representing individual network events (see Materials and methods). With this measure, network event pairs recruiting the same set of neurons have a cosine similarity of 1, and completely orthogonal patterns exhibit a cosine similarity of 0. This analysis revealed that 38% of network event pairs were completely orthogonal to one another (Figure 4A, the fraction of completely orthogonal patterns corresponds to the bar with a cosine similarity of zero). Because in sparse activity patterns, orthogonality can and will arise by chance, we additionally performed a shuffling analysis to ascertain if sparse activity per se can account for the observed occurrence of orthogonal patterns. We found significantly more orthogonality than expected by chance (38 ± 4% vs. 29 ± 4% in real vs. shuffled data, respectively, see Figure 4A inset, n = 9 mice, three sessions, comparison to shuffled data: Wilcoxon test, p=0.0039). This is consistent with the capability to represent separate sets of information within network events. Figure 4 with 1 supplement see all Download asset Open asset Network events are orthogonal, but repetitively recruit GC sub-ensembles. (A) Similarity between network events. Similarity of population vectors computed for individual network events. Comparisons were carried out between all possible pairwise combinations of vectors and quantified using cosine similarity. Inset: Mean number of orthogonal NEs for baseline sessions (Black bar) compared to shuffled data (gray bar) with SEM. Gray lines depict individual sessions. (B) Graphical representation of the correlation matrix using Pearson's r for all cell combinations, with values for r being color coded. Data from one representative session in an individual mouse. (C) Identification of clusters of correlated cells using agglomerative hierarchical clustering. Clusters were combined using a standardized Euclidean distance metric and a weighted average linkage method, until the mean of the cluster internal r-value reached a significance threshold. The 5% significance threshold was defined by creating a null-distribution of r values from randomized data sets, and is indicated for this particular experiment with a vertical line. Right panel in C depicts the reordered correlation matrix showing clusters of highly correlated cells. Only clusters whose mean intra-correlation exceeded the threshold were included in further analysis (significant clusters indicated with gray frames). (D) Raster plot showing the reactivation of clusters identified in panel C during multiple episodes of running and immobility. Individual dots indicate participation of individual cells. Clusters are color-coded according to the agglomerative tree. Network events are indicated by vertical dashed lines. Running episodes are indicated at the lower border with the running speed (blue). Even though orthogonal network events were observed, we also observed a repeated activation of granule cells in multiple network events (see i.e. Figure 1—video 2). To examine if specific sub-ensembles of granule cells are repeatedly recruited in network events, we performed a pairwise Pearson's correlation of the activity of all cell pairs during all network events of a recording session (correlation coefficients depicted in the correlation matrix in Figure 4B). We then re-arranged the cells by hierarchical clustering. Clusters were combined using a standardized Euclidean distance metric and a weighted average linkage method (Figure 4C, more examples in Figure 4—figure supplement 1A–H). This visualization reveals the existence of subgroups of cells that are strongly correlated within network event-related activity (Figure 4C), as previously demonstrated for activity during immobility in the CA1 region (Malvache et al., 2016). To more rigorously define what we considered a cluster showing correlated activity, we used a comparison to a null distribution generated by shuffling. Such approaches have been shown to outperform other approaches to define how many clusters are present in complex data (Tibshirani et al., 2001). We combined clusters until the mean of the cluster internal r-value reached a significance threshold, which was defined by creating a null-distribution of r-values from shuffled datasets (indicated with a vertical line in Figure 4C). Thus, clusters were defined quantitatively as exhibiting a mean correlation coefficient within the cluster above chance level. Using this definition, the average cluster size was 6.7 ± 0.4 cells per cluster (n = 9 mice, three sessions). The repetitive nature of GC cluster activation during an entire session becomes clearly apparent when viewing cell activity during network events over an entire session, sorted by their participation in clusters (example shown in Figure 4D). Participation of place- and speed-coding granule cells in network events To ask if network events carry specific spatial or locomotion-related information, we identified GCs with position-related or speed-related activity. We first identified the group of GCs that exhibited significant place coding (2.83% of n = 1415 active cells imaged in nine mice, Figure 5A for representative polar plots of three GCs). The place fields of place-coding GCs were distributed over the linear track (Figure 5B). If the fraction of place-coding cells was calculated as a fraction of only those GCs active during running, the fraction of significantly place-coding GCs was 6.09%. Secondly, we identified a fraction of GCs (0.85% of GCs, 1.83% of running-active GCs, n = 9 mice) displaying a significant correlation of activity with running speed (Figure 5C–E). This is in contrast to a previous study (Danielson et al., 2016), but consistent with data obtained in freely moving mice (Stefanini et al., 2020). We also examined recordings from sessions using two other linear track environments with sensory cues placed on the textured belt. First, additional sensory cues were placed randomly on the belt (cue-enriched condition). Under these conditions, the fraction of place cells observed within the GC population increased (4.56% of GCs, 10.74% of running-active GCs), as did the proportion of speed cells (1.54% of GCs, 3.64% of running-active GCs, n = 1425 active GCs imaged in nine mice, Figure 5F). Figure 5 with 1 supplement see all Download asset Open asset Characterization of dentate gyrus place and speed neurons. (A B) Place cells in the dentate gyrus. (A) Representative polar plots of two significantly place-coding granule cells (left, middle), and one without significant place preference (right). Place coding is depicted as spiral plot, where each 360° turn of the spiral represents a transition through the 1.5 m linear track without additional cues (baseline condition). Detected events are shown as black dots. The red line represents the place vector. The corresponding heatmap of normalized fluorescence is shown in the inset. In lower panels, the distributions for place vector lengths generated from shuffled data (see Materials and methods) are shown (gray histograms), the place vector for the individual cell is indicated by the red line. (B) Place field heatmaps of cells showing significant place preference. (C) Representative examples of three significantly speed-modulated neurons (black traces, running speed depicted in cyan). (D) Speed-modulated mean fluorescence signal of a representative example cell. Gray area indicates standard error. (E) Mean fluorescence signals of all significantly speed-modulated cells. Normalized fluorescence is color coded and running speed is normalized to every individual mouse maximum running speed. (F) Fractions of place and speed coding cells (cyan and green bar, respectively) normalized to all active cells (left panel) and only running active cells (right panel). Only a very small number of cells carried encoded both features (dark green bar). Second, we tested if there is a further increase in place cells with a commonly used linear track divided into zones, each with very different spatial cues (see Materials and methods, Figure 1—figure supplement 1G–J). This was not the case. In these mice (n = 3), we recorded 690 GCs, of which 2.61% were place cells. As a fraction of those GCs active during running, we found 8.11% place cells. In all conditions, few cells exhibited both speed coding and place coding. We then examined if place or speed cells are incorporated in network events, and if this participation is altered when the environment changes. Specifically, we examined the difference between the baseline linear track without additional cues and the cue-enriched condition. We chose the cue-enriched condition for further experiments and analyses because it provided sufficient spatial cues for a strong spatial representation, without introducing edges between differently cued zones on the linear track. We found that in the cue-enriched condition, dentate gyrus network events were again observed predominantly during immobility (Figure 5—figure supplement 1D, statistics of GC activity in Figure 5—figure supplement 1A–C) and were similarly related to MPP activity (Figure 5—figure supplement 1F–J). Increasing the cue density did not significantly alter the network event frequency (Figure 5—figure supplements 1D, 2.39±0.73 vs 3.63 ± 0.90 events/minute, respectively, n = 9 mice, two-way ANOVA, baseline vs. cue enriched: F(1,30)=0.71, p=0.41, run vs. rest: F(1,3)=59.13, p=0.001). However, the average size of individual network events, measured as the number of participating GCs, was significantly larger in the cue-enriched condition compared to the baseline condition (Figure 6A, Kruskal-Wallis test, n = 9 mice, 1313 and 1493 network events in baseline and cue-enriched condition, respectively, p=4×10−41), with individual GCs contributing more frequently to network events in the cue-rich condition (Figure 6B, Kruskal-Wallis test, p=1×10−40). Fewer orthogonal networks were observed in the cue-rich condition, but this was not significantly different to the baseline condition (not shown, Kruskal-Wallis test, n.s. p=0.49). Figure 6 Download asset Open asset Increasing sensory cues is associated with enlargement of network events and increased incorporation of place cells. (A) Network events comprise more granule cells in cue-enriched environments. Cumulative probability of network event size (number of cells per network event) for baseline and cue enriched condition (dark and light gray lines, respectively). (B) Cumulative probability of participation in multiple network events per cell for baseline and cue enriched condition (dark and light gray lines, respectively). (C) Fraction of place and speed cells that participate in network events (total number of place/speed cells equals 100%). (D) Relation of place and speed cells to correlated cell clusters (c.f. Figure 4). Fraction of the total number of clusters containing place cells (cyan), speed cells (light green), or both (dark green). Gray indicates clusters containing neither place nor speed cells. (E) Mean number of place and speed cells per cluster, in baseline and cue-enriched conditions, n = 9 mice. We then examined if the participation of place and speed cells in network events is altered in the cue-enriched compared to the baseline condition. As stated above, place cells are more com

Intracellular amyloid beta oligomer (iAβo) accumulation and neuronal hyperexcitability are two crucial events at early stages of Alzheimer's disease (AD). However, to date, no mechanism linking iAβo with an increase in neuronal excitability has been reported. Here, the effects of human AD brain-derived (h-iAβo) and synthetic (iAβo) peptides on synaptic currents and action potential firing were investigated in hippocampal neurons. Starting from 500 pM, iAβo rapidly increased the frequency of synaptic currents and higher concentrations potentiated the AMPA receptor-mediated current. Both effects were PKC-dependent. Parallel recordings of synaptic currents and nitric oxide (NO)-associated fluorescence showed that the increased frequency, related to pre-synaptic release, was dependent on a NO-mediated retrograde signaling. Moreover, increased synchronization in NO production was also observed in neurons neighboring those dialyzed with iAβo, indicating that iAβo can increase network excitability at a distance. Current-clamp recordings suggested that iAβo increased neuronal excitability via AMPA-driven synaptic activity without altering membrane intrinsic properties. These results strongly indicate that iAβo causes functional spreading of hyperexcitability through a synaptic-driven mechanism and offers an important neuropathological significance to intracellular species in the initial stages of AD, which include brain hyperexcitability and seizures.

Background: Intracellular amyloid-beta oligomers (iAβo) accumulation and neuronal hyperexcitability are two crucial events at early stages of Alzheimer’s disease (AD). However, to date, no mechanism linking them has been reported. Methods: Here, the effects of human AD brain-derived (h-iAβo) and synthetic (iAβo) peptides on synaptic currents and action potential (AP) firing were investigated in hippocampal neurons in vitro , ex vivo and in vivo. Results: Starting from 500 pM, iAβo rapidly increased the frequency of synaptic currents and higher concentrations potentiated the AMPA receptor-mediated current. Both effects were PKC-dependent. Parallel recordings of synaptic currents and nitric oxide (NO)-related fluorescence changes indicated that the increased frequency, related to pre-synaptic release, was dependent on a NO-mediated retrograde signaling. Moreover, increased synchronization in NO production was also observed in neurons neighboring those dialyzed with iAβo, indicating that iAβo can increase network excitability at a distance. Current-clamp recordings suggested that iAβo increased neuronal excitability via AMPA-driven synaptic activity without altering membrane intrinsic properties. Conclusion: These results strongly indicate that iAβo causes functional spreading of hyperexcitability through a synaptic-driven mechanism and offer an important neuropathological significance to intracellular species in the initial stages of AD, which include brain hyperexcitability and seizures.

OBJECTIVE:Glutamate-gated N-methyl-d-aspartate receptors (NMDARs) are instrumental to brain development and functioning. Defects in the GRIN2A gene, encoding the GluN2A subunit of NMDARs, cause slow-wave sleep (SWS)-related disorders of the epilepsy-aphasia spectrum (EAS). The as-yet poorly understood developmental sequence of early EAS-related phenotypes, and the role of GluN2A-containing NMDARs in the development of SWS and associated electroencephalographic (EEG) activity patterns, were investigated in Grin2a knockout (KO) mice. METHODS:Early social communication was investigated by ultrasonic vocalization (USV) recordings; the relationship of electrical activity of the cerebral cortex with SWS was studied using deep local field potential or chronic EEG recordings at various postnatal stages. RESULTS:Grin2a KO pups displayed altered USV and increased occurrence of high-voltage spindles. The pattern of slow-wave activity induced by low-dose isoflurane was altered in Grin2a KO mice in the 3rd postnatal week and at 1 month of age. These alterations included strong suppression of the delta oscillation power and an increase in the occurrence of the spike-wave bursts. The proportion of SWS and the sleep quality were transiently reduced in Grin2a KO mice aged 1 month but recovered by the age of 2 months. Grin2a KO mice also displayed spontaneous spike-wave discharges, which occurred nearly exclusively during SWS, at 1 and 2 months of age. SIGNIFICANCE:The impaired vocal communication, the spike-wave discharges occurring almost exclusively in SWS, and the age-dependent alteration of SWS that were all seen in Grin2a KO mice matched the sleep-related and age-dependent manifestations seen in children with EAS, hence validating the Grin2a KO as a reliable model of EAS disorders. Our data also show that GluN2A-containing NMDARs are involved in slow-wave activity, and that the period of postnatal brain development (postnatal day 30) when several anomalies peaked might be critical for GluN2A-dependent, sleep-related physiological and pathological processes.

In area CA1 of the hippocampus, the selection of place cells to represent a new environment is biased towards neurons with higher excitability. However, different environments are represented by orthogonal cell ensembles, suggesting that regulatory mechanisms exist. Activity-dependent plasticity of intrinsic excitability, as observed in vitro, is an attractive candidate. Here, using whole-cell patch-clamp recordings of CA1 pyramidal neurons in anesthetized rats, we have examined how inducing theta-bursts of action potentials affects their intrinsic excitability over time. We observed a long-lasting, homeostatic depression of intrinsic excitability which commenced within minutes, and, in contrast to in vitro observations, was not mediated by dendritic Ih. Instead, it was attenuated by the Kv1.1 channel blocker dendrotoxin K, suggesting an axonal origin. Analysis of place cells’ out-of-field firing in mice navigating in virtual reality further revealed an experience-dependent reduction consistent with decreased excitability. We propose that this mechanism could reduce memory interference.

The ability to flexibly navigate an environment relies on a hippocampal-dependent cognitive map. External space can be internally mapped at different spatial resolutions. However, whether hippocampal spatial coding resolution can rapidly adapt to local features of an environment remains unclear. To explore this possibility, we recorded the firing of hippocampal neurons in mice navigating virtual reality environments, embedding or not local visual cues (virtual 3D objects) in specific locations. Virtual objects enhanced spatial coding resolution in their vicinity with a higher proportion of place cells, smaller place fields, increased spatial selectivity and stability. This effect was highly dynamic upon objects manipulations. Objects also improved temporal coding resolution through improved theta phase precession and theta timescale spike coordination. We propose that the fast adaptation of hippocampal spatial coding resolution to local features of an environment could be relevant for large-scale navigation.

The search for the neuronal substrate of memory or engram is an important part of current neuroscience research. This article is protected by copyright. All rights reserved

The dentate gyrus, a major entry point to the hippocampus, gates (or filters) incoming information from the cortex. During sleep or anesthesia, the slow-wave oscillation (SWO) orchestrates hippocampus-neocortex communication, which is important for memory formation. The dentate gate is altered in temporal lobe epilepsy (TLE) early during epileptogenesis, which favors the propagation of pathological activities. Yet, whether the gating of physiological SWO by dentate granule cells (DGCs) is altered in TLE has remained unexplored. We combined intracellular recordings of membrane potential (V-m) of DGCs and local field potential recordings of the SWO in parietal cortex in anesthetized rats early during epileptogenesis [post-status epilepticus (SE) rats]. As expected, in control rats, the V-m of DGCs weakly and rarely oscillated in the SWO frequency range. In contrast, in post-SE rats, the V-m of DGCs displayed strong and long-lasting SWO. In these cells, clear UP and DOWN states, in phase with the neocortical SWO, led to a bimodal V-m distribution. In post-SE rats, the firing of DGCs was increased and more temporally modulated by the neocortical SWO. We conclude that UP/DOWN state dynamics dominate the V-m of DGCs and firing early during epileptogenesis. This abnormally strong neocortical influence on the dynamics of DGCs may profoundly modify the hippocampus-neocortex dialogue during sleep and associated cognitive functions.

The dynamics of neuronal networks connected by synaptic dynamics can sustain long periods of depolarization that can last for hundreds of milliseconds such as Up states recorded during sleep or anesthesia. Yet the underlying mechanism driving these periods remain unclear. We show here within a mean-field model that the residence time of the neuronal membrane potential in cortical Up states does not follow a Poissonian law, but presents several peaks. Furthermore, the present modeling approach allows extracting some information about the neuronal network connectivity from the time distribution histogram. Based on a synaptic-depression model, we find that these peaks, that can be observed in histograms of patch-clamp recordings are not artifacts of electrophysiological measurements, but rather are an inherent property of the network dynamics. Analysis of the equations reveals a stable focus located close to the unstable limit cycle, delimiting a region that defines the Up state. The model further shows that the peaks observed in the Up state time distribution are due to winding around the focus before escaping from the basin of attraction. Finally, we use in vivo recordings of intracellular membrane potential and we recover from the peak distribution, some information about the network connectivity. We conclude that it is possible to recover the network connectivity from the distribution of times that the neuronal membrane voltage spends in Up states.

Kainate is a potent neurotoxin known to induce acute seizures. However, whether kainate receptors (KARs) play any role in the pathophysiology of temporal lobe epilepsy (TLE) is not known. In TLE, recurrent mossy fiber (rMF) axons form abnormal excitatory synapses onto other dentate granule cells that operate via KARs. The present study explores the pathophysiological implications of KARs in generating recurrent seizures in chronic epilepsy. In an in vitro model of TLE, seizure-like activity was minimized in mice lacking the GluK2 subunit, which is a main component of aberrant synaptic KARs at rMF synapses. In vivo, the frequency of interictal spikes and ictal discharges was strongly reduced in GluK2(-/-) mice or in the presence of a GluK2/GluK5 receptor antagonist. Our data show that aberrant GluK2-containing KARs play a major role in the chronic seizures that characterize TLE and thus constitute a promising antiepileptic target.

The patch-clamp technique and the whole-cell measurements derived from it have greatly advanced our understanding of the coding properties of individual neurons by allowing for a detailed analysis of their excitatory/inhibitory synaptic inputs, intrinsic electrical properties, and morphology. Because such measurements require a high level of mechanical stability they have for a long time been limited to in vitro and anesthetized preparations. Recently, however, a considerable amount of effort has been devoted to extending these techniques to awake restrained/head-fixed preparations allowing for the study of the input-output functions of neurons during behavior. In this chapter we describe a technique extending patch-clamp recordings to awake animals free to explore their environments.