Rapid and continuous modulation of hippocampal network state during exploration of new places

Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations

Foraging theory has been a remarkably successful approach to understanding the behavior of animals in many contexts. In patch-based foraging contexts, the marginal value theorem (MVT) shows that the optimal strategy is to leave a patch when the marginal rate of return declines to the average for the environment. However, the MVT is only valid in deterministic environments whose statistics are known to the forager; naturalistic environments seldom meet these strict requirements. As a result, the strategies used by foragers in naturalistic environments must be empirically investigated. We developed a novel behavioral task and a corresponding computational framework for studying patch-leaving decisions in head-fixed and freely moving mice. We varied between-patch travel time, as well as within-patch reward depletion rate, both deterministically and stochastically. We found that mice adopt patch residence times in a manner consistent with the MVT and not explainable by simple ethologically motivated heuristic strategies. Critically, behavior was best accounted for by a modified form of the MVT wherein environment representations were updated based on local variations in reward timing, captured by a Bayesian estimator and dynamic prior. Thus, we show that mice can strategically attend to, learn from, and exploit task structure on multiple timescales simultaneously, thereby efficiently foraging in volatile environments. The results provide a foundation for applying the systems neuroscience toolkit in freely moving and head-fixed mice to understand the neural basis of foraging under uncertainty.

Event Integration and Temporal Differentiation: How Hierarchical Knowledge Emerges in Hippocampal Subfields through Learning

Everyday life is composed of events organized by changes in contexts, with each event containing an unfolding sequence of occurrences. A major challenge facing our memory systems is how to integrate sequential occurrences within events while also maintaining their details and avoiding over-integration across different contexts. We asked if and how distinct hippocampal subfields come to hierarchically and, in parallel, represent both event context and subevent occurrences with learning. Female and male human participants viewed sequential events defined as sequences of objects superimposed on shared color frames while undergoing high-resolution fMRI. Importantly, these events were repeated to induce learning. Event segmentation, as indexed by increased reaction times at event boundaries, was observed in all repetitions. Temporal memory decisions were quicker for items from the same event compared to across different events, indicating that events shaped memory. With learning, hippocampal CA3 multivoxel activation patterns clustered to reflect the event context, with more clustering correlated with behavioral facilitation during event transitions. In contrast, in the dentate gyrus (DG), temporally proximal items that belonged to the same event became associated with more differentiated neural patterns. A computational model explained these results by dynamic inhibition in the DG. Additional similarity measures support the notion that CA3 clustered representations reflect shared voxel populations, while DG's distinct item representations reflect different voxel populations. These findings suggest an interplay between temporal differentiation in the DG and attractor dynamics in CA3. They advance our understanding of how knowledge is structured through integration and separation across time and context.

Prefrontal neuronal ensembles link prior knowledge with novel actions during flexible action selection

We make decisions based on currently perceivable information or an internal model of the environment. The medial prefrontal cortex (mPFC) and its interaction with the hippocampus have been implicated in the latter, model-based decision-making; however, the underlying computational properties remain incompletely understood. We have examined mPFC spiking and hippocampal oscillatory activity while rats flexibly select new actions using a known associative structure of environmental cues and outcomes. During action selection, the mPFC reinstates representations of the associative structure. These awake reactivation events are accompanied by synchronous firings among neurons coding the associative structure and those coding actions. Moreover, their functional coupling is strengthened upon the reactivation events leading to adaptive actions. In contrast, only cue-coding neurons improve functional coupling during hippocampal sharp wave ripples. Thus, the lack of direct experience disconnects the mPFC from the hippocampus to independently form self-organized neuronal ensemble dynamics linking prior knowledge with novel actions.

Resolving Non-identifiability Mitigates Bias in Models of Neural Tuning and Functional Coupling

In the brain, all neurons are driven by the activity of other neurons, some of which maybe simultaneously recorded, but most are not. As such, models of neuronal activity need to account for simultaneously recorded neurons and the influences of unmeasured neurons. This can be done through inclusion of model terms for observed external variables (e.g., tuning to stimuli) as well as terms for latent sources of variability. Determining the influence of groups of neurons on each other relative to other influences is important to understand brain functioning. The parameters of statistical models fit to data are commonly used to gain insight into the relative importance of those influences. Scientific interpretation of models hinge upon unbiased parameter estimates. However, evaluation of biased inference is rarely performed and sources of bias are poorly understood. Through extensive numerical study and analytic calculation, we show that common inference procedures and models are typically biased. We demonstrate that accurate parameter selection before estimation resolves model non-identifiability and mitigates bias. In diverse neurophysiology data sets, we found that contributions of coupling to other neurons are often overestimated while tuning to exogenous variables are underestimated in common methods. We explain heterogeneity in observed biases across data sets in terms of data statistics. Finally, counter to common intuition, we found that model non-identifiability contributes to bias, not variance, making it a particularly insidious form of statistical error. Together, our results identify the causes of statistical biases in common models of neural data, provide inference procedures to mitigate that bias, and reveal and explain the impact of those biases in diverse neural data sets.

Over and above frequency: Gamma oscillations as units of neural circuit operations

Gamma oscillations (∼30-150 Hz) are widespread correlates of neural circuit functions. These network activity patterns have been described across multiple animal species, brain structures, and behaviors, and are usually identified based on their spectral peak frequency. Yet, despite intensive investigation, whether gamma oscillations implement causal mechanisms of specific brain functions or represent a general dynamic mode of neural circuit operation remains unclear. In this perspective, we review recent advances in the study of gamma oscillations toward a deeper understanding of their cellular mechanisms, neural pathways, and functional roles. We discuss that a given gamma rhythm does not per se implement any specific cognitive function but rather constitutes an activity motif reporting the cellular substrates, communication channels, and computational operations underlying information processing in its generating brain circuit. Accordingly, we propose shifting the attention from a frequency-based to a circuit-level definition of gamma oscillations.

Hippocampal oscillatory dynamics in freely behaving rats during exploration of social and non-social stimuli

Hippocampal CA2 supports social memory and encodes information about social experiences. Our previous study showed that CA2 place cells responded specifically to social stimuli (Nat Commun, (Alexander et al. 2016)). In addition, a prior study showed that activation of CA2 induces slow gamma rhythms (~ 25-55 Hz) in the hippocampus (Elife, (Alexander 2018)). Together, these results raise the question of whether slow gamma rhythms coordinate CA2 activity during social information processing. We hypothesized that slow gamma would be associated with transmission of social memories from CA2 to CA1, perhaps to integrate information across regions or promote social memory retrieval. We recorded local field potentials from hippocampal subfields CA1, CA2, and CA3 of 4 rats performing a social exploration task. We analyzed the activity of theta, slow gamma, and fast gamma rhythms, as well as sharp wave-ripples (SWRs), within each subfield. We assessed interactions between subfields during social exploration sessions and during presumed social memory retrieval in post-social exploration sessions. We found that CA2 slow gamma rhythms increased during social interactions but not during non-social exploration. CA2-CA1 theta-show gamma coupling was enhanced during social exploration. Furthermore, CA1 slow gamma rhythms and SWRs were associated with presumed social memory retrieval. In conclusion, these results suggest that CA2-CA1 interactions via slow gamma rhythms occur during social memory encoding, and CA1 slow gamma is associated with retrieval of social experience.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09829-8.

Theta-gamma phase amplitude coupling in a hippocampal CA1 microcircuit

Phase amplitude coupling (PAC) between slow and fast oscillations is found throughout the brain and plays important functional roles. Its neural origin remains unclear. Experimental findings are often puzzling and sometimes contradictory. Most computational models rely on pairs of pacemaker neurons or neural populations tuned at different frequencies to produce PAC. Here, using a data-driven model of a hippocampal microcircuit, we demonstrate that PAC can naturally emerge from a single feedback mechanism involving an inhibitory and excitatory neuron population, which interplay to generate theta frequency periodic bursts of higher frequency gamma. The model suggests the conditions under which a CA1 microcircuit can operate to elicit theta-gamma PAC, and highlights the modulatory role of OLM and PVBC cells, recurrent connectivity, and short term synaptic plasticity. Surprisingly, the results suggest the experimentally testable prediction that the generation of the slow population oscillation requires the fast one and cannot occur without it.

Nonlinear Theta-Gamma Coupling between the Anterior Thalamus and Hippocampus Increases as a Function of Running Speed

The hippocampal theta rhythm strongly correlates to awake behavior leading to theories that it represents a cognitive state of the brain. As theta has been observed in other regions of the Papez circuit, it has been theorized that activity propagates in a reentrant manner. These observations complement the energy cascade hypothesis in which large-amplitude, slow-frequency oscillations reflect activity propagating across a large population of neurons. Higher frequency oscillations, such as gamma, are related to the speed with which inhibitory and excitatory neurons interact and distribute activity on the local level. The energy cascade hypothesis suggests that the larger anatomic loops, maintaining theta, drive the smaller loops. As hippocampal theta increases in power with running speed, so does the power and frequency of the gamma rhythm. If theta is propagated through the circuit, it stands to reason that the local field potential (LFP) recorded in other regions would be coupled to the hippocampal theta, with the coupling increasing with running speed. We explored this hypothesis using open-source simultaneous recorded data from the CA1 region of the hippocampus and the anterior dorsal and anterior ventral thalamus. Cross-regional theta coupling increased with running speed. Although the power of the gamma rhythm was lower in the anterior thalamus, there was an increase in the coupling of hippocampal theta to anterior thalamic gamma. Broadly, the data support models of how activity moves across the nervous system, suggesting that the brain uses large-scale volleys of activity to support higher cognitive processes.

Organization of hippocampal CA3 into correlated cell assemblies supports a stable spatial code

Hippocampal subfield CA3 is thought to stably store memories in assemblies of recurrently connected cells functioning as a collective. However, the collective hippocampal coding properties that are unique to CA3 and how such properties facilitate the stability or precision of the neural code remain unclear. Here, we performed large-scale Ca2+ imaging in hippocampal CA1 and CA3 of freely behaving mice that repeatedly explored the same, initially novel environments over weeks. CA3 place cells have more precise and more stable tuning and show a higher statistical dependence with their peers compared with CA1 place cells, uncovering a cell assembly organization in CA3. Surprisingly, although tuning precision and long-term stability are correlated, cells with stronger peer dependence exhibit higher stability but not higher precision. Overall, our results expose the three-way relationship between tuning precision, long-term stability, and peer dependence, suggesting that a cell assembly organization underlies long-term storage of information in the hippocampus.

Theta dominates cross-frequency coupling in hippocampal-medial entorhinal circuit during awake-behavior in rats

Hippocampal theta and gamma rhythms are hypothesized to play a role in the physiology of higher cognition. Prior research has reported that an offset in theta cycles between the entorhinal cortex, CA3, and CA1 regions promotes independence of population activity across the hippocampus. In line with this idea, it has recently been observed that CA1 pyramidal cells can establish and maintain coordinated place cell activity intrinsically, with minimal reliance on afferent input. Counter to these observations is the contemporary hypothesis that CA1 neuron activity is driven by a gamma oscillation arising from the medial entorhinal cortex (MEC) that relays information by providing precisely timed synchrony between MEC and CA1. Reinvestigating this in rats during appetitive track running, we found that theta is the dominant frequency of cross-frequency coupling between the MEC and hippocampus, with hippocampal gamma largely independent of entorhinal gamma.

•

Theta, theta harmonic, and gamma power increase with running speed in the HPC and MEC

•

Intra-regionally, theta-theta harmonic and theta-gamma coupling increases with speed

•

Cross-regionally, theta is the dominant frequency of coupling between HPC and MEC

•

Marginal gamma coupling can be explained by local gamma modulated by coherent theta

A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations

Decades of rodent research have established the role of hippocampal sharp wave ripples (SPW-Rs) in consolidating and guiding experience. More recently, intracranial recordings in humans have suggested their role in episodic and semantic memory. Yet, common standards for recording, detection, and reporting do not exist. Here, we outline the methodological challenges involved in detecting ripple events and offer practical recommendations to improve separation from other high-frequency oscillations. We argue that shared experimental, detection, and reporting standards will provide a solid foundation for future translational discovery.

Running speed and REM sleep control two distinct modes of rapid interhemispheric communication

Rhythmic gamma-band communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory. Here, using multisite recordings in both rats and mice, we show that even faster ~140 Hz rhythms are robustly anti-phase across cortical hemispheres, visually resembling splines, the interlocking teeth on mechanical gears. Splines are strongest in superficial granular retrosplenial cortex, a region important for spatial navigation and memory. Spline-frequency interhemispheric communication becomes more coherent and more precisely anti-phase at faster running speeds. Anti-phase splines also demarcate high-activity frames during REM sleep. While splines and associated neuronal spiking are anti-phase across retrosplenial hemispheres during navigation and REM sleep, gamma-rhythmic interhemispheric communication is precisely in-phase. Gamma and splines occur at distinct points of a theta cycle and thus highlight the ability of interhemispheric cortical communication to rapidly switch between in-phase (gamma) and anti-phase (spline) modes within individual theta cycles during both navigation and REM sleep.

Gamma-rhythmic communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory. Here, Ghosh et al. identify even faster ~140 Hz rhythms, named splines, that reflect anti-phase neuronal synchrony across hemispheres. The balance of anti-phase spline and in-phase gamma communication is dynamically controlled by behavior and sleep.

Long-term transverse imaging of the hippocampus with glass microperiscopes

The hippocampus consists of a stereotyped neuronal circuit repeated along the septal-temporal axis. This transverse circuit contains distinct subfields with stereotyped connectivity that support crucial cognitive processes, including episodic and spatial memory. However, comprehensive measurements across the transverse hippocampal circuit in vivo are intractable with existing techniques. Here, we developed an approach for two-photon imaging of the transverse hippocampal plane in awake mice via implanted glass microperiscopes, allowing optical access to the major hippocampal subfields and to the dendritic arbor of pyramidal neurons. Using this approach, we tracked dendritic morphological dynamics on CA1 apical dendrites and characterized spine turnover. We then used calcium imaging to quantify the prevalence of place and speed cells across subfields. Finally, we measured the anatomical distribution of spatial information, finding a non-uniform distribution of spatial selectivity along the DG-to-CA1 axis. This approach extends the existing toolbox for structural and functional measurements of hippocampal circuitry.

Theta and gamma hippocampal-neocortical oscillations during the episodic-like memory test: Impairment in epileptogenic rats

Cortical oscillations in different frequency bands have been shown to be intimately involved in exploration of environment and cognition. Here, the local field potentials in the hippocampus, the medial prefrontal cortex (mPFC), and the medial entorhinal cortex (mEC) were recorded simultaneously in rats during the execution of the episodic-like memory task. The power of theta (~4-10 Hz), slow gamma (~25-50 Hz), and fast gamma oscillations (~55-100 Hz) was analyzed in all structures examined. Particular attention was paid to the theta coherence between three mentioned structures. The modulation of the power of gamma rhythms by the phase of theta cycle during the execution of the episodic-like memory test by rats was also closely studied. Healthy rats and rats one month after kainate-induced status epilepticus (SE) were examined. Paroxysmal activity in the hippocampus (high amplitude interictal spikes), excessive excitability of animals, and the death of hippocampal and dentate granular cells in rats with kainate-evoked SE were observed, which indicated the development of seizure focus in the hippocampus (epileptogenesis). One month after SE, the rats exhibited a specific impairment of episodic memory for the what-where-when triad: unlike healthy rats, epileptogenic SE animals did not identify the objects during the test. This impairment was associated with the changes in the characteristics of theta and gamma rhythms and specific violation of theta coherence and theta/gamma coupling in these structures in comparison with the healthy animals. We believe that these disturbances in the cortical areas play a role in episodic memory dysfunction in kainate-treated animals. These findings can shed light on the mechanisms of cognitive deficit during epileptogenesis.

A Variable Clock Underlies Internally Generated Hippocampal Sequences

Humans have the ability to store and retrieve memories with various degrees of specificity, and recent advances in reinforcement learning have identified benefits to learning when past experience is represented at different levels of temporal abstraction. How this flexibility might be implemented in the brain remains unclear. We analyzed the temporal organization of male rat hippocampal population spiking to identify potential substrates for temporally flexible representations. We examined activity both during locomotion and during memory-associated population events known as sharp-wave ripples (SWRs). We found that spiking during SWRs is rhythmically organized with higher event-to-event variability than spiking during locomotion-associated population events. Decoding analyses using clusterless methods further indicate that a similar spatial experience can be replayed in multiple SWRs, each time with a different rhythmic structure whose periodicity is sampled from a log-normal distribution. This variability increases with experience despite the decline in SWR rates that occurs as environments become more familiar. We hypothesize that the variability in temporal organization of hippocampal spiking provides a mechanism for storing experiences with various degrees of specificity.SIGNIFICANCE STATEMENT One of the most remarkable properties of memory is its flexibility: the brain can retrieve stored representations at varying levels of detail where, for example, we can begin with a memory of an entire extended event and then zoom in on a particular episode. The neural mechanisms that support this flexibility are not understood. Here we show that hippocampal sharp-wave ripples, which mark the times of memory replay and are important for memory storage, have a highly variable temporal structure that is well suited to support the storage of memories at different levels of detail.

Theta and gamma oscillatory dynamics in mouse models of Alzheimer's disease: A path to prospective therapeutic intervention

Understanding the neural basis of cognitive deficits, a key feature of Alzheimer's disease (AD), is imperative for achieving the therapy of the disease. Rhythmic oscillatory activities in neural systems are a fundamental mechanism for diverse brain functions, including cognition. In several neurological conditions like AD, aberrant neural oscillations have been shown to play a central role. Furthermore, manipulation of brain oscillations in animals has confirmed their impact on cognition and disease. In this article, we review the evidence from mouse models that shows how synchronized oscillatory activity is intricately linked to AD machinery. We primarily focus on recent reports showing abnormal oscillatory activities at theta and gamma frequencies in AD condition and their influence on cellular disturbances and cognitive impairments. A thorough comprehension of the role that neuronal oscillations play in AD pathology should pave the way to therapeutic interventions that can curb the disease.

Dentate gyrus and CA3 GABAergic interneurons bidirectionally modulate signatures of internal and external drive to CA1

Specific classes of GABAergic neurons play specific roles in regulating information processing in the brain. In the hippocampus, two major classes, parvalbumin-expressing (PV⁺) and somatostatin-expressing (SST⁺), differentially regulate endogenous firing patterns and target subcellular compartments of principal cells. How these classes regulate the flow of information throughout the hippocampus is poorly understood. We hypothesize that PV⁺ and SST⁺ interneurons in the dentate gyrus (DG) and CA3 differentially modulate CA3 patterns of output, thereby altering the influence of CA3 on CA1. We find that while suppressing either interneuron class increases DG and CA3 output, the effects on CA1 were very different. Suppressing PV⁺ interneurons increases local field potential signatures of coupling from CA3 to CA1 and decreases signatures of coupling from entorhinal cortex to CA1; suppressing SST⁺ interneurons has the opposite effect. Thus, DG and CA3 PV⁺ and SST⁺ interneurons bidirectionally modulate the flow of information through the hippocampal circuit.

Prediction errors disrupt hippocampal representations and update episodic memories

The brain supports adaptive behavior by generating predictions, learning from errors, and updating memories to incorporate new information. Prediction error, or surprise, triggers learning when reality contradicts expectations. Prior studies have shown that the hippocampus signals prediction errors, but the hypothesized link to memory updating has not been demonstrated. In a human functional MRI study, we elicited mnemonic prediction errors by interrupting familiar narrative videos immediately before the expected endings. We found that prediction errors reversed the relationship between univariate hippocampal activation and memory: greater hippocampal activation predicted memory preservation after expected endings, but memory updating after surprising endings. In contrast to previous studies, we show that univariate activation was insufficient for understanding hippocampal prediction error signals. We explain this surprising finding by tracking both the evolution of hippocampal activation patterns and the connectivity between the hippocampus and neuromodulatory regions. We found that hippocampal activation patterns stabilized as each narrative episode unfolded, suggesting sustained episodic representations. Prediction errors disrupted these sustained representations and the degree of disruption predicted memory updating. The relationship between hippocampal activation and subsequent memory depended on concurrent basal forebrain activation, supporting the idea that cholinergic modulation regulates attention and memory. We conclude that prediction errors create conditions that favor memory updating, prompting the hippocampus to abandon ongoing predictions and make memories malleable.

GhostiPy: An Efficient Signal Processing and Spectral Analysis Toolbox for Large Data

Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large-scale modalities. Here we introduce GhostiPy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. GhostiPy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high-channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis.

Fear conditioning potentiates the hippocampal CA1 commissural pathway in vivo and increases awake phase sleep

The hippocampus is essential for spatial learning and memory. To assess learning we used contextual fear conditioning (cFC), where animals learn to associate a place with aversive events like foot‐shocks. Candidate memory mechanisms for cFC are long‐term potentiation (LTP) and long‐term depression (LTD), but there is little direct evidence of them operating in the hippocampus in vivo following cFC. Also, little is known about the behavioral state changes induced by cFC. To address these issues, we recorded local field potentials in freely behaving mice by stimulating in the left dorsal CA1 region and recording in the right dorsal CA1 region. Synaptic strength in the commissural pathway was monitored by measuring field excitatory postsynaptic potentials (fEPSPs) before and after cFC. After cFC, the commissural pathway's synaptic strength was potentiated. Although recordings occurred during the wake phase of the light/dark cycle, the mice slept more in the post‐conditioning period than in the pre‐conditioning period. Relative to awake periods, in non‐rapid eye movement (NREM) sleep the fEPSPs were larger in both pre‐ and post‐conditioning periods. We also found a significant negative correlation between the animal's speed and fEPSP size. Therefore, to avoid confounds in the fEFSP potentiation estimates, we controlled for speed‐related and sleep‐related fEPSP changes and still found that cFC induced long‐term potentiation, but no significant long‐term depression. Synaptic strength changes were not found in the control group that simply explored the fear‐conditioning chamber, indicating that exploration of the novel place did not produce the measurable effects caused by cFC. These results show that following cFC, the CA1 commissural pathway is potentiated, likely contributing to the functional integration of the left and right hippocampi in fear memory consolidation. In addition, the cFC paradigm produces significant changes in an animal's behavioral state, which are observable as proximal changes in sleep patterns.

Differential Impact of Acute and Chronic Stress on CA1 Spatial Coding and Gamma Oscillations

Chronic and acute stress differentially affect behavior as well as the structural integrity of the hippocampus, a key brain region involved in cognition and memory. However, it remains unclear if and how the facilitatory effects of acute stress on hippocampal information coding are disrupted as the stress becomes chronic. To examine this, we compared the impact of acute and chronic stress on neural activity in the CA1 subregion of male mice subjected to a chronic immobilization stress (CIS) paradigm. We observed that following first exposure to stress (acute stress), the spatial information encoded in the hippocampus sharpened, and the neurons became increasingly tuned to the underlying theta oscillations in the local field potential (LFP). However, following repeated exposure to the same stress (chronic stress), spatial tuning was poorer and the power of both the slow-gamma (30–50 Hz) and fast-gamma (55–90 Hz) oscillations, which correlate with excitatory inputs into the region, decreased. These results support the idea that acute and chronic stress differentially affect neural computations carried out by hippocampal circuits and suggest that acute stress may improve cognitive processing.

The role of hippocampal mossy cells in novelty detection

At the encounter with a novel environment, contextual memory formation is greatly enhanced, accompanied with increased arousal and active exploration. Although this phenomenon has been widely observed in animal and human daily life, how the novelty in the environment is detected and contributes to contextual memory formation has lately started to be unveiled. The hippocampus has been studied for many decades for its largely known roles in encoding spatial memory, and a growing body of evidence indicates a differential involvement of dorsal and ventral hippocampal divisions in novelty detection. In this brief review article, we discuss the recent findings of the role of mossy cells in the ventral hippocampal moiety in novelty detection and put them in perspective with other novelty-related pathways in the hippocampus. We propose a mechanism for novelty-driven memory acquisition in the dentate gyrus by the direct projection of ventral mossy cells to dorsal dentate granule cells. By this projection, the ventral hippocampus sends novelty signals to the dorsal hippocampus, opening a gate for memory encoding in dentate granule cells based on information coming from the entorhinal cortex. We conclude that, contrary to the presently accepted functional independence, the dorsal and ventral hippocampi cooperate to link the novelty and contextual information, and this dorso-ventral interaction is crucial for the novelty-dependent memory formation.

Temporal Relations between Cortical Network Oscillations and Breathing Frequency during REM Sleep

Nasal breathing generates a rhythmic signal which entrains cortical network oscillations in widespread brain regions on a cycle-to-cycle time scale. It is unknown, however, how respiration and neuronal network activity interact on a larger time scale: are breathing frequency and typical neuronal oscillation patterns correlated? Is there any directionality or temporal relationship? To address these questions, we recorded field potentials from the posterior parietal cortex of mice together with respiration during REM sleep. In this state, the parietal cortex exhibits prominent θ and γ oscillations while behavioral activity is minimal, reducing confounding signals. We found that the instantaneous breathing frequency strongly correlates with the instantaneous frequency and amplitude of both θ and γ oscillations. Cross-correlograms and Granger causality revealed specific directionalities for different rhythms: changes in θ activity precede and Granger-cause changes in breathing frequency, suggesting control by the functional state of the brain. On the other hand, the instantaneous breathing frequency Granger causes changes in γ frequency, suggesting that γ is influenced by a peripheral reafference signal. These findings show that changes in breathing frequency temporally relate to changes in different patterns of rhythmic brain activity. We hypothesize that such temporal relations are mediated by a common central drive likely to be located in the brainstem.

Spectrum Degradation of Hippocampal LFP During Euthanasia

The hippocampal local field potential (LFP) exhibits a strong correlation with behavior. During rest, the theta rhythm is not prominent, but during active behavior, there are strong rhythms in the theta, theta harmonics, and gamma ranges. With increasing running velocity, theta, theta harmonics and gamma increase in power and in cross-frequency coupling, suggesting that neural entrainment is a direct consequence of the total excitatory input. While it is common to study the parametric range between the LFP and its complementing power spectra between deep rest and epochs of high running velocity, it is also possible to explore how the spectra degrades as the energy is completely quenched from the system. Specifically, it is unknown whether the 1/f slope is preserved as synaptic activity becomes diminished, as low frequencies are generated by large pools of neurons while higher frequencies comprise the activity of more local neuronal populations. To test this hypothesis, we examined rat LFPs recorded from the hippocampus and entorhinal cortex during barbiturate overdose euthanasia. Within the hippocampus, the initial stage entailed a quasi-stationary LFP state with a power-law feature in the power spectral density. In the second stage, there was a successive erosion of power from high- to low-frequencies in the second stage that continued until the only dominant remaining power was <20 Hz. This stage was followed by a rapid collapse of power spectrum toward the absolute electrothermal noise background. As the collapse of activity occurred later in hippocampus compared with medial entorhinal cortex, it suggests that the ability of a neural network to maintain the 1/f slope with decreasing energy is a function of general connectivity. Broadly, these data support the energy cascade theory where there is a cascade of energy from large cortical populations into smaller loops, such as those that supports the higher frequency gamma rhythm. As energy is pulled from the system, neural entrainment at gamma frequency (and higher) decline first. The larger loops, comprising a larger population, are fault-tolerant to a point capable of maintaining their activity before a final collapse.

Acute Effects of Two Different Species of Amyloid-β on Oscillatory Activity and Synaptic Plasticity in the Commissural CA3-CA1 Circuit of the Hippocampus

Recent evidence indicates that soluble amyloid-β (Aβ) species induce imbalances in excitatory and inhibitory transmission, resulting in neural network functional impairment and cognitive deficits during early stages of Alzheimer's disease (AD). To evaluate the in vivo effects of two soluble Aβ species (Aβ_25-35 and Aβ_1-40) on commissural CA3-to-CA1 (cCA3-to-CA1) synaptic transmission and plasticity, and CA1 oscillatory activity, we used acute intrahippocampal microinjections in adult anaesthetized male Wistar rats. Soluble Aβ microinjection increased cCA3-to-CA1 synaptic variability without significant changes in synaptic efficiency. High-frequency CA3 stimulation was rendered inefficient by soluble Aβ intrahippocampal injection to induce long-term potentiation and to enhance synaptic variability in CA1, contrasting with what was observed in vehicle-injected subjects. Although soluble Aβ microinjection significantly increased the relative power of γ-band and ripple oscillations and significantly shifted the average vector of θ-to-γ phase-amplitude coupling (PAC) in CA1, it prevented θ-to-γ PAC shift induced by high-frequency CA3 stimulation, opposite to what was observed in vehicle-injected animals. These results provide further evidence that soluble Aβ species induce synaptic dysfunction causing abnormal synaptic variability, impaired long-term plasticity, and deviant oscillatory activity, leading to network activity derailment in the hippocampus.

Impaired speed encoding and grid cell periodicity in a mouse model of tauopathy

Dementia is associated with severe spatial memory deficits which arise from dysfunction in hippocampal and parahippocampal circuits. For spatially sensitive neurons, such as grid cells, to faithfully represent the environment these circuits require precise encoding of direction and velocity information. Here, we have probed the firing rate coding properties of neurons in medial entorhinal cortex (MEC) in a mouse model of tauopathy. We find that grid cell firing patterns are largely absent in rTg4510 mice, while head-direction tuning remains largely intact. Conversely, neural representation of running speed information was significantly disturbed, with smaller proportions of MEC cells having firing rates correlated with locomotion in rTg4510 mice. Additionally, the power of local field potential oscillations in the theta and gamma frequency bands, which in wild-type mice are tightly linked to running speed, was invariant in rTg4510 mice during locomotion. These deficits in locomotor speed encoding likely severely impact path integration systems in dementia.

Mnemonic prediction errors bias hippocampal states

When our experience violates our predictions, it is adaptive to upregulate encoding of novel information, while down-weighting retrieval of erroneous memory predictions to promote an updated representation of the world. We asked whether mnemonic prediction errors promote hippocampal encoding versus retrieval states, as marked by distinct network connectivity between hippocampal subfields. During fMRI scanning, participants were cued to internally retrieve well-learned complex room-images and were then presented with either an identical or a modified image (0-4 changes). In the left hemisphere, we find that CA1-entorhinal connectivity increases, and CA1-CA3 connectivity decreases, with the number of changes. Further, in the left CA1, the similarity between activity patterns during cued-retrieval of the learned room and during the image is lower when the image includes changes, consistent with a prediction error signal in CA1. Our findings provide a mechanism by which mnemonic prediction errors may drive memory updating—by biasing hippocampal states.

When our expectations are violated, it is adaptive to update our internal models to improve predictions in the future. Here, the authors show that during mnemonic violations, hippocampal networks are biased towards an encoding state and away from a retrieval state to potentially update these predictions.

Coordination of hippocampal theta and gamma oscillations relative to spatial active avoidance reflects cognitive outcome after febrile status epilepticus

Cognitive deficits may arise from a variety of genetic alterations and neurological insults that impair neural coding mechanisms and the routing of neural information underpinning learning and memory. Slow and medium gamma oscillations underpin memory recall and sensorimotor processing and represent dynamic inputs at CA1 synapses. Febrile status epilepticus (FSE) can lead to increased risk for temporal lobe epilepsy and enduring cognitive impairments. In a rodent model, we assessed how FSE alters hippocampal CA1 signals relative to spatial task performance and serve as a readout of synaptic input efficacy. The power of theta (5-12 Hz), slow gamma (30-50 Hz), and medium gamma (70-90 Hz) differentially interact with respect to cognitive demands during active avoidance behavior on a rotating arena. Successful avoidance was characterized by slow gamma that was largest several seconds before or after peak acceleration. Peak acceleration coincides with peak theta oscillations, followed within approximately 1 s by peak medium gamma. FSE animals showing impairment in the task maintained the profiles of theta and medium gamma associated with increased sensorimotor processing following peak acceleration but did not exhibit the same slow gamma profile associated with epochs of memory retrieval. While CA1 synapses from entorhinal cortex were functionally unaffected by FSE, communication via synapses from CA3 may have been impaired, leading to both temporal discoordination and poor memory retrieval. These findings demonstrate theta/gamma profiles can serve as both physiological biomarkers for memory retrieval or encoding deficits and synapse level treatment targets that could attenuate cognitive comorbidities associated with early life seizures. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Specific Increase of Hippocampal Delta Oscillations Across Consecutive Treadmill Runs

Running speed affects theta (6-10 Hz) oscillations, the most prominent rhythm in the rat hippocampus. Many reports have found a strong positive correlation between locomotion speed and the amplitude and frequency of theta oscillations. However, less is known about how other rhythms such as delta (0.5-4 Hz) and gamma (25-100 Hz) are affected, and how consecutive runs impact oscillatory activity in hippocampal networks. Here, we investigated whether the successive execution of short-term runs modulates local field potentials (LFPs) in the rat hippocampus. To do this, we trained Long-Evans rats to perform voluntary 15-s runs at 30 cm/s on a treadmill placed on the central stem of an eight-shape maze, in which they subsequently performed a spatial alternation task. We bilaterally recorded CA1 LFPs while rats executed at least 35 runs on the treadmill-maze apparatus. Within running periods, we observed progressive increases in delta band power along with decreases in the power of the theta and gamma bands across runs. Concurrently, the inter-hemispheric phase coherence in the delta band significantly increased, while in the theta and gamma bands exhibited no changes. Delta power and inter-hemispheric coherence correlated better with the trial number than with the actual running speed. We observed no significant differences in running speed, head direction, nor in spatial occupancy across runs. Our results thus show that consecutive treadmill runs at the same speed positively modulates the power and coherence of delta oscillations in the rat hippocampus.

Three brain states in the hippocampus and cortex

Contemporary brain research seeks to understand how cognition is reducible to neural activity. Crucially, much of this effort is guided by a scientific paradigm that views neural activity as essentially driven by external stimuli. In contrast, recent perspectives argue that this paradigm is by itself inadequate and that understanding patterns of activity intrinsic to the brain is needed to explain cognition. Yet, despite this critique, the stimulus-driven paradigm still dominates-possibly because a convincing alternative has not been clear. Here, we review a series of findings suggesting such an alternative. These findings indicate that neural activity in the hippocampus occurs in one of three brain states that have radically different anatomical, physiological, representational, and behavioral correlates, together implying different functional roles in cognition. This three-state framework also indicates that neural representations in the hippocampus follow a surprising pattern of organization at the timescale of ∼1 s or longer. Lastly, beyond the hippocampus, recent breakthroughs indicate three parallel states in the cortex, suggesting shared principles and brain-wide organization of intrinsic neural activity.

Hippocampal CA2 Organizes CA1 Slow and Fast γ Oscillations during Novel Social and Object Interaction

A key goal in hippocampal research is to understand how neuronal activity is generated and organized across hippocampal subregions to enable memory formation and retrieval. Neuronal activity in CA2 is regulated by spatial and social investigation as well as by novelty (Mankin et al., 2015; Alexander et al., 2016), and CA2 activity controls population oscillatory activity in the slow γ and ripple ranges within hippocampus (Kay et al., 2016; Oliva et al., 2016; Boehringer et al., 2017; Alexander et al., 2018). CA2 neurons are also required for social recognition memory (Stevenson and Caldwell, 2012; Hitti and Siegelbaum, 2014; Smith et al., 2016). Because CA1 exhibits layer-specific organization (Scheffer-Teixeira et al., 2012; Lasztóczi and Klausberger, 2014, 2016) reflective of its inputs (Fernández-Ruiz et al., 2012; Schomburg et al., 2014), and because CA2 activity controls CA1 slow γ (Alexander et al., 2018), we hypothesized that silencing CA2 would affect CA1 slow γ in a layer-specific manner during investigation of a novel social stimulus. While recording from CA1, we leveraged molecular tools to selectively target and inhibit CA2 pyramidal cells using inhibitory DREADDs while subject mice investigated novel animals or objects. We found that CA2 inhibition reduced slow γ power during investigation of a novel animal and fast γ power during both novel object and animal investigation in a manner reflective of the CA2 axonal projection zones within CA1. Our results suggest that CA2 contributes to CA1 slow and fast γ oscillations in a stimulus-specific manner.

The neural circuitry supporting successful spatial navigation despite variable movement speeds

Ants who have successfully navigated the long distance between their foraging spot and their nest dozens of times will drastically overshoot their destination if the size of their legs is doubled by the addition of stilts. This observation reflects a navigational strategy called path integration, a strategy also utilized by mammals. Path integration necessitates that animals keep track of their movement speed and use it to precisely and instantly modify where they think they are and where they want to go. Here we review the neural circuitry that has evolved to integrate speed and space. We start with the rate and temporal codes for speed in the hippocampus and work backwards towards the motor and sensory systems. We highlight the need for experiments designed to differentiate the respective contributions of motor efference copy versus sensory inputs. In particular, we discuss the importance of high-resolution tracking of the latency of speed-encoding as a precise way to disentangle the sensory versus motor computations that enable successful spatial navigation at very different speeds.

Directional coupling of slow and fast hippocampal gamma with neocortical alpha/beta oscillations in human episodic memory

Episodic memories hinge upon our ability to process a wide range of multisensory information and bind this information into a coherent, memorable representation. On a neural level, these 2 processes are thought to be supported by neocortical alpha/beta desynchronization and hippocampal theta/gamma synchronization, respectively. Intuitively, these 2 processes should couple to successfully create and retrieve episodic memories, yet this hypothesis has not been tested empirically. We address this by analyzing human intracranial electroencephalogram data recorded during 2 associative memory tasks. We find that neocortical alpha/beta (8 to 20 Hz) power decreases reliably precede and predict hippocampal "fast" gamma (60 to 80 Hz) power increases during episodic memory formation; during episodic memory retrieval, however, hippocampal "slow" gamma (40 to 50 Hz) power increases reliably precede and predict later neocortical alpha/beta power decreases. We speculate that this coupling reflects the flow of information from the neocortex to the hippocampus during memory formation, and hippocampal pattern completion inducing information reinstatement in the neocortex during memory retrieval.

Hippocampal Theta-Gamma Coupling Reflects State-Dependent Information Processing in Decision Making

During decision making, hippocampal activity encodes information sometimes about present and sometimes about potential future plans. The mechanisms underlying this transition remain unknown. Building on the evidence that gamma oscillations at different frequencies (low gamma [LG], 30–55 Hz; high gamma [HG], 60–90 Hz; and epsilon, 100–140 Hz) reflect inputs from different circuits, we identified how changes in those frequencies reflect different information-processing states. Using a unique noradrenergic manipulation by clonidine, which shifted both neural representations and gamma states, we found that future representations depended on gamma components. These changes were identifiable on each cycle of theta as asymmetries in the theta cycle, which arose from changes within the ratio of LG and HG power and the underlying phases of those gamma rhythms within the theta cycle. These changes in asymmetry of the theta cycle reflected changes in representations of present and future on each theta cycle.

Five Decades of Hippocampal Place Cells and EEG Rhythms in Behaving Rats

Over the last 50 years, much has been learned about the physiology and functions of the hippocampus from studies in freely behaving rats. Two relatively early works in the field provided major insights that remain relevant today. Here, I revisit these studies and discuss how our understanding of the hippocampus has evolved over the last several decades.

Methodological Considerations on the Use of Different Spectral Decomposition Algorithms to Study Hippocampal Rhythms

Local field potential (LFP) oscillations are primarily shaped by the superposition of postsynaptic currents. Hippocampal LFP oscillations in the 25- to 50-Hz range (“slow γ”) are proposed to support memory retrieval independent of other frequencies. However, θ harmonics extend up to 48 Hz, necessitating a study to determine whether these oscillations are fundamentally the same. We compared the spectral analysis methods of wavelet, ensemble empirical-mode decomposition (EEMD), and Fourier transform. EEMD, as previously applied, failed to account for the θ harmonics. Depending on analytical parameters selected, wavelet may convolve over high-order θ harmonics due to the variable time-frequency atoms, creating the appearance of a broad 25- to 50-Hz rhythm. As an illustration of this issue, wavelet and EEMD depicted slow γ in a synthetic dataset that only contained θ and its harmonics. Oscillatory transience cannot explain the difference in approaches as Fourier decomposition identifies ripples triggered to epochs of high-power, 120- to 250-Hz events. When Fourier is applied to high power, 25- to 50-Hz events, only θ harmonics are resolved. This analysis challenges the identification of the slow γ rhythm as a unique fundamental hippocampal oscillation. While there may be instances in which slow γ is present in the rat hippocampus, the analysis presented here shows that unless care is exerted in the application of EEMD and wavelet techniques, the results may be misleading, in this case misrepresenting θ harmonics. Moreover, it is necessary to reconsider the characteristics that define a fundamental hippocampal oscillation as well as theories based on multiple independent γ bands.

Sub-second dynamics of theta-gamma coupling in hippocampal CA1

Oscillatory brain activity reflects different internal brain states including neurons’ excitatory state and synchrony among neurons. However, characterizing these states is complicated by the fact that different oscillations are often coupled, such as gamma oscillations nested in theta in the hippocampus, and changes in coupling are thought to reflect distinct states. Here, we describe a new method to separate single oscillatory cycles into distinct states based on frequency and phase coupling. Using this method, we identified four theta-gamma coupling states in rat hippocampal CA1. These states differed in abundance across behaviors, phase synchrony with other hippocampal subregions, and neural coding properties suggesting that these states are functionally distinct. We captured cycle-to-cycle changes in oscillatory coupling states and found frequent switching between theta-gamma states showing that the hippocampus rapidly shifts between different functional states. This method provides a new approach to investigate oscillatory brain dynamics broadly.

Theta-gamma cascades and running speed

Oscillations in the hippocampal local field potential at theta and gamma frequencies are prominent during awake behavior and have demonstrated several behavioral correlates. Both oscillations have been observed to increase in amplitude and frequency as a function of running speed. Previous investigations, however, have examined the relationship between speed and each of these oscillation bands separately. Based on energy cascade models where "…perturbations of slow frequencies cause a cascade of energy dissipation at all frequency scales" (Buzsaki G. Rhythms of the Brain, 2006), we hypothesized that cross-frequency interactions between theta and gamma should increase as a function of speed. We examined these relationships across multiple layers of the CA1 subregion, which correspond to synaptic zones receiving different afferents. Across layers, we found a reliable correlation between the power of theta and the power of gamma, indicative of an amplitude-amplitude relationship. Moreover, there was an increase in the coherence between the power of gamma and the phase of theta, demonstrating increased phase-amplitude coupling with speed. Finally, at higher velocities, phase entrainment between theta and gamma increases. These results have important implications and provide new insights regarding how theta and gamma are integrated for neuronal circuit dynamics, with coupling strength determined by the excitatory drive within the hippocampus. Specifically, rather than arguing that different frequencies can be attributed to different psychological processes, we contend that cognitive processes occur across multiple frequency bands simultaneously with organization occurring as a function of the amount of energy iteratively propagated through the brain. NEW & NOTEWORTHY Often, the theta and gamma oscillations in the hippocampus have been believed to be a consequence of two marginally overlapping phenomena. This perspective, however, runs counter to an alternative hypothesis in which a slow-frequency, high-amplitude oscillation provides energy that cascades into higher frequency, lower amplitude oscillations. We found that as running speed increases, all measures of cross-frequency theta-gamma coupling intensify, providing evidence in favor of the energy cascade hypothesis.

Wave Turbulence and Energy Cascade in the Hippocampus

Mesoscale cortical activity can be defined as the organization of activity of large neuron populations into collective action, forming time-dependent patterns such as traveling waves. Although collective action may play an important role in the cross-scale integration of brain activity and in the emergence of cognitive behavior, a comprehensive formulation of the laws governing its dynamics is still lacking. Because collective action processes are macroscopic with respect to neuronal activity, these processes cannot be described directly with methods and models developed for the microscale (individual neurons).To identify the characteristic features of mesoscopic dynamics, and to lay the foundations for a theoretical description of mesoscopic activity in the hippocampus, we conduct a comprehensive examination of observational data of hippocampal local field potential (LFP) recordings. We use the strong correlation between rat running-speed and the LFP power to parameterize the energy input into the hippocampus, and show that both the power and non-linearity of collective action (e.g., theta and gamma rhythms) increase with increased speed. Our results show that collective-action dynamics are stochastic (the precise state of a single neuron is irrelevant), weakly non-linear, and weakly dissipative. These are the principles of the theory of weak turbulence. Therefore, we propose weak turbulence a theoretical framework for the description of mesoscopic activity in the hippocampus. The weak turbulence framework provides a complete description of the cross-scale energy exchange (the energy cascade). It uncovers the mechanism governing major features of LFP spectra and bispectra, such as the physical meaning of the exponent α of power-law LFP spectra (e.g., f ^-α, where f is the frequency), the strengthening of theta-gamma coupling with energy input into the hippocampus, as well as specific phase lags associated with their interaction. Remarkably, the weak turbulence framework is consistent with the theory of self organized criticality, which provides a simple explanation for the existence of the power-law background spectrum. Together with self-organized criticality, weak turbulence could provide a unifying approach to modeling the dynamics of mesoscopic activity.

Analysis of an open source, closed-loop, realtime system for hippocampal sharp-wave ripple disruption

OBJECTIVE

The ability to modulate neural activity in a closed-loop fashion enables causal tests of hypotheses which link dynamically-changing neural circuits to specific behavioral functions. One such dynamically-changing neural circuit is the hippocampus, in which momentary sharp-wave ripple (SWR) events-≈  100 ms periods of large 150-250 Hz oscillations-have been linked to specific mnemonic functions via selective closed-loop perturbation. The limited duration of SWR means that the latency in systems used for closed-loop interaction is of significant consequence compared to other longer-lasting circuit states. While closed-loop SWR perturbation is becoming more wide-spread, the performance trade-offs involved in building a SWR disruption system have not been explored, limiting the design and interpretation of paradigms involving ripple disruption.

APPROACH

We developed and evaluated a low-latency closed-loop SWR detection system implemented as a module to an open-source neural data acquisition software suite capable of interfacing with two separate data acquisition hardware platforms. We first use synthetic data to explore the parameter space of our detection algorithm, then proceed to quantify the realtime in vivo performance and limitations of our system.

MAIN RESULTS

We evaluate the realtime system performance of two data acquisition platforms, one using USB and one using ethernet for communication. We report that signal detection latency decomposes into a data acquisition component of 7.5-13.8 ms and 1.35-2.6 ms for USB and ethernet hardware respectively, and an algorithmic component which varies depending on the threshold parameter. Using ethernet acquisition hardware, we report that an algorithmic latency in the range of  ≈20-66 ms can be achieved while maintaining  <10 false detections per minute, and that these values are highly dependent upon algorithmic parameter space trade-offs.

SIGNIFICANCE

By characterizing this system in detail, we establish a framework for analyzing other closed-loop neural interfacing systems. Thus, we anticipate this modular, open-source, realtime system will facilitate a wide range of carefully-designed causal closed-loop experiments.

CA2 neuronal activity controls hippocampal low gamma and ripple oscillations

Hippocampal oscillations arise from coordinated activity among distinct populations of neurons and are associated with cognitive functions. Much progress has been made toward identifying the contribution of specific neuronal populations in hippocampal oscillations, but less is known about the role of hippocampal area CA2, which is thought to support social memory. Furthermore, the little evidence on the role of CA2 in oscillations has yielded conflicting conclusions. Therefore, we sought to identify the contribution of CA2 to oscillations using a controlled experimental system. We used excitatory and inhibitory DREADDs to manipulate CA2 neuronal activity and studied resulting hippocampal-prefrontal cortical network oscillations. We found that modification of CA2 activity bidirectionally regulated hippocampal and prefrontal cortical low-gamma oscillations and inversely modulated hippocampal ripple oscillations in mice. These findings support a role for CA2 in low-gamma generation and ripple modulation within the hippocampus and underscore the importance of CA2 in extrahippocampal oscillations.

Parsing Hippocampal Theta Oscillations by Nested Spectral Components during Spatial Exploration and Memory-Guided Behavior

Theta oscillations reflect rhythmic inputs that continuously converge to the hippocampus during exploratory and memory-guided behavior. The theta-nested operations that organize hippocampal spiking could either occur regularly from one cycle to the next or be tuned on a cycle-by-cycle basis. To resolve this, we identified spectral components nested in individual theta cycles recorded from the mouse CA1 hippocampus. Our single-cycle profiling revealed theta spectral components associated with different firing modulations and distinguishable ensembles of principal cells. Moreover, novel co-firing patterns of principal cells in theta cycles nesting mid-gamma oscillations were the most strongly reactivated in subsequent offline sharp-wave/ripple events. Finally, theta-nested spectral components were differentially altered by behavioral stages of a memory task; the 80-Hz mid-gamma component was strengthened during learning, whereas the 22-Hz beta, 35-Hz slow gamma, and 54-Hz mid-gamma components increased during retrieval. We conclude that cycle-to-cycle variability of theta-nested spectral components allows parsing of theta oscillations into transient operating modes with complementary mnemonic roles.

•

Spectral profiling of single theta waves enables studying inter-cycle variability

•

Theta spectral components feature different spiking patterns and ensembles

•

Co-firing in theta cycles nesting mid-gamma undergo enhanced offline reactivation

•

Theta components relate differently to learning and memory retrieval demands

Intrinsic Mechanisms of Frequency Selectivity in the Proximal Dendrites of CA1 Pyramidal Neurons

Gamma oscillations are thought to play a role in learning and memory. Two distinct bands, slow (25-50 Hz) and fast (65-100 Hz) gamma, have been identified in area CA1 of the rodent hippocampus. Slow gamma is phase locked to activity in area CA3 and presumably driven by the Schaffer collaterals (SCs). We used a combination of computational modeling and in vitro electrophysiology in hippocampal slices of male rats to test whether CA1 neurons responded to SC stimulation selectively at slow gamma frequencies and to identify the mechanisms involved. Both approaches demonstrated that, in response to temporally precise input at SCs, CA1 pyramidal neurons fire preferentially in the slow gamma range regardless of whether the input is at fast or slow gamma frequencies, suggesting frequency selectivity in CA1 output with respect to CA3 input. In addition, phase locking, assessed by the vector strength, was more precise for slow gamma than fast gamma input. This frequency selectivity was greatly attenuated when the slow Ca²⁺-dependent K⁺ (SK) current was removed from the model or blocked in vitro with apamin. Perfusion of slices with BaCl₂ to block A-type K⁺ channels tightened this frequency selectivity. Both the broad-spectrum cholinergic agonist carbachol and the muscarinic agonist oxotremorine-M greatly attenuated the selectivity. The more precise firing at slower frequencies persisted throughout all of the pharmacological manipulations conducted. We propose that these intrinsic mechanisms provide a means by which CA1 phase locks to CA3 at different gamma frequencies preferentially in vivo as physiological conditions change with behavioral demands.SIGNIFICANCE STATEMENT Gamma frequency activity, one of multiple bands of synchronous activity, has been suggested to underlie various aspects of hippocampal function. Multisite recordings within the rat hippocampal formation indicate that different behavioral tasks are associated with synchronized activity between areas CA3 and CA1 at two different gamma bands: slow and fast gamma. In this study, we examine the intrinsic mechanisms that may allow CA1 to selectively "listen" to CA3 at slow compared with fast gamma and suggest mechanisms that gate this selectivity. Identifying the intrinsic mechanisms underlying differential gamma preference may help to explain the distinct types of CA3-CA1 synchronization observed in vivo under different behavioral conditions.

Gamma Oscillations in Rat Hippocampal Subregions Dentate Gyrus, CA3, CA1, and Subiculum Underlie Associative Memory Encoding

Neuronal oscillations in the rat hippocampus relate to both memory and locomotion, raising the question of how these cognitive and behavioral correlates interact to determine the oscillatory network state of this region. Here, rats freely locomoted while performing an object-location task designed to test hippocampus-dependent spatial associative memory. Rhythmic activity in theta, beta, slow gamma, and fast gamma frequency ranges were observed in both action potentials and local field potentials (LFPs) across four main hippocampal subregions. Several patterns of LFP oscillations corresponded to overt behavior (e.g., increased dentate gyrus-CA3 beta coherence during stationary moments and CA1-subiculum theta coherence during locomotion). In comparison, slow gamma (∼40 Hz) oscillations throughout the hippocampus related most specifically to object-location associative memory encoding rather than overt behavior. The results help to untangle how hippocampal oscillations relate to both memory and motion and single out slow gamma oscillations as a distinguishing correlate of spatial associative memory.

Uncovering temporal structure in hippocampal output patterns

Place cell activity of hippocampal pyramidal cells has been described as the cognitive substrate of spatial memory. Replay is observed during hippocampal sharp-wave-ripple-associated population burst events (PBEs) and is critical for consolidation and recall-guided behaviors. PBE activity has historically been analyzed as a phenomenon subordinate to the place code. Here, we use hidden Markov models to study PBEs observed in rats during exploration of both linear mazes and open fields. We demonstrate that estimated models are consistent with a spatial map of the environment, and can even decode animals’ positions during behavior. Moreover, we demonstrate the model can be used to identify hippocampal replay without recourse to the place code, using only PBE model congruence. These results suggest that downstream regions may rely on PBEs to provide a substrate for memory. Additionally, by forming models independent of animal behavior, we lay the groundwork for studies of non-spatial memory.

Nav1.1-Overexpressing Interneuron Transplants Restore Brain Rhythms and Cognition in a Mouse Model of Alzheimer's Disease

Inhibitory interneurons regulate the oscillatory rhythms and network synchrony that are required for cognitive functions and disrupted in Alzheimer's disease (AD). Network dysrhythmias in AD and multiple neuropsychiatric disorders are associated with hypofunction of Nav1.1, a voltage-gated sodium channel subunit predominantly expressed in interneurons. We show that Nav1.1-overexpressing, but not wild-type, interneuron transplants derived from the embryonic medial ganglionic eminence (MGE) enhance behavior-dependent gamma oscillatory activity, reduce network hypersynchrony, and improve cognitive functions in human amyloid precursor protein (hAPP)-transgenic mice, which simulate key aspects of AD. Increased Nav1.1 levels accelerated action potential kinetics of transplanted fast-spiking and non-fast-spiking interneurons. Nav1.1-deficient interneuron transplants were sufficient to cause behavioral abnormalities in wild-type mice. We conclude that the efficacy of interneuron transplantation and the function of transplanted cells in an AD-relevant context depend on their Nav1.1 levels. Disease-specific molecular optimization of cell transplants may be required to ensure therapeutic benefits in different conditions.

Experience-dependent trends in CA1 theta and slow gamma rhythms in freely behaving mice

CA1 place cells become more anticipatory with experience, an effect thought to be caused by NMDA receptor-dependent plasticity in the CA3-CA1 network. Theta (~5-12 Hz), slow gamma (~25-50 Hz), and fast gamma (~50-100 Hz) rhythms are thought to route spatial information in the hippocampal formation and to coordinate place cell ensembles. Yet, it is unknown whether these rhythms exhibit experience-dependent changes concurrent with those observed in place cells. Slow gamma rhythms are thought to indicate inputs from CA3 to CA1, and such inputs are thought to be strengthened with experience. Thus, we hypothesized that slow gamma rhythms would become more evident with experience. We tested this hypothesis using mice freely traversing a familiar circular track for three 10-min sessions per day. We found that slow gamma amplitude was reduced in the early minutes of the first session of each day, even though both theta and fast gamma amplitudes were elevated during this same period. However, in the first minutes of the second and third sessions of each day, all three rhythms were elevated. Interestingly, theta was elevated to a greater degree in the first minutes of the first session than in the first minutes of later sessions. Additionally, all three rhythms were strongly influenced by running speed in dynamic ways, with the influence of running speed on theta and slow gamma changing over time within and across sessions. These results raise the possibility that experience-dependent changes in hippocampal rhythms relate to changes in place cell activity that emerge with experience. NEW & NOTEWORTHY We show that CA1 theta, slow gamma, and fast gamma rhythms exhibit characteristic changes over time within sessions in familiar environments. These effects in familiar environments evolve across repeated sessions.