Biological oscillations can be controlled by a small population of rhythmic pacemaker cells, or in the brain, they also can emerge from complex cellular and circuit-level interactions. Whether and how these mechanisms are combined to give rise to oscillatory patterns that govern cognitive function are not well understood. For example, the activity of hippocampal networks is temporally coordinated by a 7- to 9-Hz local field potential (LFP) theta rhythm, yet many individual cells decouple from the LFP frequency to oscillate at frequencies ∼1 Hz higher. To better understand the network interactions that produce these complex oscillatory patterns, we asked whether the relative frequency difference between LFP and individual cells is retained when the LFP frequency is perturbed experimentally. We found that rhythmic optogenetic stimulation of medial septal GABAergic neurons controlled the hippocampal LFP frequency outside of the endogenous theta range, even during behavioral states when endogenous mechanisms would otherwise have generated 7- to 9-Hz theta oscillations. While the LFP frequency matched the optogenetically induced stimulation frequency, the oscillation frequency of individual hippocampal cells remained broadly distributed, and in a subset of cells including interneurons, it was accelerated beyond the new base LFP frequency. The inputs from septal GABAergic neurons to the hippocampus, therefore, do not appear to directly control the cellular oscillation frequency but rather engage cellular and circuit mechanisms that accelerate the rhythmicity of individual cells. Thus, theta oscillations are an example of cortical oscillations that combine inputs from a subcortical pacemaker with local computations to generate complex oscillatory patterns that support cognitive functions.

The neural circuits underlying memory change over prolonged periods after learning, in a process known as systems consolidation. Postlearning spontaneous reactivation of memory-related neural ensembles is thought to mediate this process, although a causal link has not been established. Here we test this hypothesis in mice by using optogenetics to selectively reactivate neural ensembles representing a contextual fear memory (sometimes referred to as engram neurons). High-frequency stimulation of these ensembles in the retrosplenial cortex 1 day after learning produced a recent memory with features normally observed in consolidated remote memories, including higher engagement of neocortical areas during retrieval, contextual generalization, and decreased hippocampal dependence. Moreover, this effect was only present if memory ensembles were reactivated during sleep or light anesthesia. These results provide direct support for postlearning memory ensemble reactivation as a mechanism of systems consolidation, and show that this process can be accelerated by ensemble reactivation in an unconscious state. engram | memory consolidation | retrosplenial cortex | fear conditioning | replay T he ability to encode and retrieve episodic memories requires coordinated activity in diverse brain areas, including the thalamus, neocortex, and areas of the medial-temporal lobe such as the hippocampus (HPC) (1-3). At the time of learning, synaptic plasticity is thought to occur in a subset of neurons that are activated during the experience and become part of the neural ensemble representing the specific memory, sometimes referred to as the memory engram (4). These changes occur rapidly with memory encoding, and are essential for the initial formation and maintenance of memory (5, 6). As time passes, memory ensembles throughout the brain are further stabilized and modified through a process known as systems memory consolidation, which is thought to be necessary for the maintenance, integration, and correct categorization of new information (7,8). This process is usually slow (months to years in humans and weeks to months in rodents) and changes the relative contribution of different brain areas for memory retrieval. Studies from both humans and rodents show that the hippocampus is preferentially engaged during learning and recent memory retrieval, whereas neocortical areas are more active when a remote memory is retrieved (9-11). In addition, some neocortical areas involved in remote memory are not necessary for recent memory retrieval (9, 12), whereas the hippocampus is generally dispensable for remote memory retrieval (13-16), although some recent studies have challenged this idea (12,17). Interestingly, these broad changes at the neural circuit level are often accompanied by changes in the quality of memory. For example, humans tend to lose details of episodic memories as time passes (18), and rodents are unable to discriminate between two different contexts in a remote retrieval trial in the context fear conditioning (CFC) paradi...

Extended Data Fig. 2 Network property statistics of DSA interneurons (A) Statistical contrast matrices (two-sided Tukey's test) for firing rates of pyramidal cells (PYR), interneurons (INT) and DSA interneurons during waking quiescence (QWake), NREM sleep, REM sleep and active behavior (Walk). (B) Same layout and statistical comparisons as in A but for the average unit CCGs as a function of brain state. (C) Pearson correlations (tested using a Student's t distribution) between average CCG responses of NREM sleep, QWake, REM sleep and walking behavior for all groups. Note that spike vs population relationships are preserved across brain states. (D) Top: Average joint Z-score rate density between 10% of the interneurons (100 shufflings), 90% remaining interneurons and pyramidal cells neurons (left) and between 10% of the pyramidal cells (100 shufflings), interneurons and remaining pyramidal neurons. Bottom: Spearman correlation and statistical contrast matrices (two-sided Tukey's test) for interneuron and pyramidal neurons rate and Z-scored population for all shown joint histograms. (E) Average (mean ± IC95) partial correlation values of the Z-scored rate for all groups (blue for ρDSA,INT controlling for PYR; red for ρDSA,PYR controlling for INT; magenta for ρPYR,INT controlling for DSA), after truncating high firing rate units to match median the spikes number of pyramidal cells (average from n = 32 sessions, P < 10 -25 , F(2, 2787) = 57.42, repeated measures ANOVA). (F) Left: distribution of excitatory divergence in all groups. Only putative pyramidal units excited their postsynaptic target cells (incidence probability for all groups in top-inset; P < 10 -110 , χ 2 (2) = 504.24, χ 2 test). Right: distribution of excitatory convergence for all cell groups (P < 10 -67 , χ 2 (2) = 306.15, χ 2 test). DSA neurons have fewer excitatory connections than the interneurons group (P < 10 -4 , χ 2 (1) = 11.36, χ 2 test). ***P<0.001. Extended Data Fig. 3 Mechanisms of DSA neuron firing during DOWN states -model results (A) Spiking neural model containing 100 leaky DSA neurons receiving an asymmetric inhibitory/excitatory drive (top-left scheme). Bottom, UP/DOWN transitions with DOWN-selective firing DSA neurons (gray dots in the rastergram at the top). Top right, log firing rate distributions in the model corresponded to the those of the recorded neurons. (B) Peri-DOWN-state Z scored firing raster plot for all simulated principal cells (PYR, left) and

Specialized cells in the medial entorhinal cortex (mEC), such as speed cells, head direction (HD) cells, and grid cells, are thought to support spatial navigation. To determine whether these computations are dependent on local circuits, we record neuronal activity in mEC layers II and III and optogenetically perturb locally projecting layer II pyramidal cells. We find that sharply tuned HD cells are only weakly responsive while speed, broadly tuned HD cells, and grid cells show pronounced transient excitatory and inhibitory responses. During the brief period of feedback inhibition, there is a reduction in specifically grid accuracy, which is corrected as firing rates return to baseline. These results suggest that sharp HD cells are embedded in a separate mEC sub-network from broad HD cells, speed cells, and grid cells. Furthermore, grid tuning is not only dependent on local processing but also rapidly updated by HD, speed, or other afferent inputs to mEC.

Understanding how excitatory (E) and inhibitory (I) inputs are integrated by neurons requires monitoring their subthreshold behavior. We probed the subthreshold dynamics using optogenetic depolarizing pulses in hippocampal neuronal assemblies in freely moving mice. Excitability decreased during sharp-wave ripples coupled with increased I. In contrast to this “negative gain,” optogenetic probing showed increased within-field excitability in place cells by weakening I and unmasked stable place fields in initially non–place cells. Neuronal assemblies active during sharp-wave ripples in the home cage predicted spatial overlap and sequences of place fields of both place cells and unmasked preexisting place fields of non–place cells during track running. Thus, indirect probing of subthreshold dynamics in neuronal populations permits the disclosing of preexisting assemblies and modes of neuronal operations.