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A longstanding goal in neuroscience is to provide a biological understanding of episodic memory, our conscious recollection of prior experience. While the hippocampus is thought to be a critical locus for episodic learning in the mammalian brain, the nature of its involvement is unsettled. This thesis details several investigations that attempt to probe the neural mechanisms that support the encoding and organization of new experiences into memory. Throughout the included works, we utilize in vivo two-photon fluorescence microscopy and calcium imaging to study the functional dynamics of hippocampal networks in mice during memory-guided behavior. To begin, Chapter 2 examines how neural coding in hippocampal area CA1 is modified during trace fear conditioning, a common model of episodic learning in rodents that requires linking events separated in time. We longitudinally tracked network activity throughout the entire learning process, analyzing how changes in hippocampal representations paralleled behavioral expression of conditioned fear. Our data indicated that, contrary to contemporary theories, the hippocampus does not generate sequences of persistent activity to learn the temporal association. Instead, neural firing rates were reorganized by learning to encode the relevant stimuli in a temporally variable manner, which could reflect a fundamentally different mode of information transmission and learning across longer time intervals. The remaining chapters concern place cells---neurons identified in the hippocampus that are active only in specific locations of an animals' environment. In Chapter 3, we use mouse virtual reality to explore how the hippocampus constructs representations of novel environments. Through multiple lines of analysis, we identify signatures of place cells that acquire spatial tuning through a particularly rapid form of synaptic plasticity. These motifs were enriched specifically during novel exploration, suggesting that the hippocampus can dynamical tune its learning rate to rapidly encode memories of new experiences. Finally, Chapter 4 expands a model of hippocampal computation that explains the emergence of place cells through a more general mechanism of efficient memory coding. In theory and experiment, we identified properties of place cells that systematically varied with the compressibility of sensory information in the environment. Our preliminary data suggests that hippocampal coding adapts to the statistics of experience to compress correlated patterns, a computation generically useful for memory and which naturally extends to representation of variables beyond physical space.

The hippocampus has long been known to play a role in learning and memory as well as spatial navigation. However, studies over the past several decades have shown that the hippocampus is not just a cognitive structure, but is also involved in emotional behaviors, particularly through its ventral pole. Recent experiments, from our lab and others, have revealed that ventral CA1 neural activity is strongly modulated by anxiogenic environments. Furthermore, optogenetic manipulation of ventral CA1 cell bodies and projections modifies anxiety-like behavior in the open field and elevated plus maze. However, it is still unknown if ventral CA1 represents anxiogenic stimuli through a single cell or a population code. Additionally, whether ventral CA1 encodes the moment-to-moment behavioral state changes caused by anxiogenic stimuli is unresolved. I investigate these questions using in-vivo freely moving calcium imaging in combination with neural population decoding analysis and unsupervised behavioral segmentation. My results show that ventral CA1 encodes anxiogenic stimuli through a high dimensional, distributed population code that allows for the separation of aversive stimuli with different sensory properties. I also demonstrate that ventral CA1 represents behavioral states through a low dimensional, distributed population code that generalizes across distinct contexts. Thus, ventral CA1 possesses multiple population codes that represent different kinds of information.

Merging the senses is key to perception, yet how we achieve this remains unclear. New research finds multimodality even in primary sensory areas, but its role is not understood. We address this question by using in vivo 2-photon calcium imaging in awake mice to test several hypotheses about the possible functions primary somatosensory cortex (S1) may subserve in integrating auditory and tactile sensory input. We first test whether S1 encodes pure auditory stimulus identity by training a linear classifier to decode different sounds from S1 activity. We find that decoder accuracy is slightly-but-significantly above chance, suggesting that S1 weakly encodes sounds. We then ask whether S1 encodes specific audio-tactile feature conjunctions by testing decoder performance for distinct combinations of simultaneously-presented auditory and tactile stimuli. We find that accuracy was within chance levels, indicating that sound-evoked suppression of whisker responses is auditory-stimulus non-specific. Subsequently, we test whether passive experience is sufficient to induce either a) Hebbian-like reactivation of tactile stimulus representations by correlated auditory stimuli or b) enhanced mixed selectivity. We find that passive experience results in neither effect. We also find S1’s auditory and audio-tactile encoding properties to be stable in the face of reinforcement conditioning. As part of a separate project, we also present results that reinforcement conditioning enhances encoding of time and temporal surprise in primary somatosensory cortex.

Memories are thought to be stored in neuronal ensembles throughout the brain, or engrams. These ensembles are defined as the neuronal populations active during learning, that undergo lasting cellular changes due to the learning, and are necessary for memory retrieval. Genetic strategies that utilize immediate early genes (IEGs), which are expressed upon cellular stimulation, have been developed to identify engrams. These tools allow for the labeling of the cells active during memory encoding, which can then be compared with those active during retrieval, advancing our knowledge of how single memories are stored in the brain. Despite these advances, little is known about how multiple memories are encoded and stored in the brain. This limitation is due to the current methods, which restrict our ability to visualize multiple ensembles in the brain. Here, I developed a multiple labeling system, based on the IEG Arc, that allows us to investigate how single and multiple memories are stored in the brain. In Chapter 2, we first show the validity of using an existing Arc-based labeling system to investigate how fear memory ensembles are modulated by propranolol, a β-adrenergic receptor antagonist. We found that propranolol modulates fear retrieval and decreases the reactivation of fear ensembles in the dorsal dentate gyrus (DG). In Chapter 3, I show the development of a novel, multiple Arc (mArc) labeling system that allows for the tagging of multiple Arc ensembles in the brain. We validated this system by investigating how context, time, and valence influence ensemble reactivation in the DG. We show that similar contextual experiences and experiences occurring close in time are stored in overlapping ensembles. The mArc system provides a powerful approach for investigating how multiple memories are organized in the brain and will be useful for multiple areas of investigation.

While the stress response represents an example of allostasis that enables the organism to cope with environmental and psychosocial challenges, its chronic activation imposes an allostatic load that contributes to the cumulative wear and tear of the system and induces negative mental and physical health outcomes. Nonetheless, the underlying basis of the stress-disease connection is still poorly understood and represents a gap in the knowledge that requires further research. We investigated the effects of chronic glucocorticoid stimulation in three independent human primary fibroblast lines, as an in vitro model of chronic psychosocial stress. By deploying a longitudinal, high-frequency, repeated-measures strategy across their entire lifespan, we were able to determine that chronically stressed cells present a significant increase in their total energy expenditure and that this stress-induced hypermetabolism is linked to an acceleration of their biological aging. Expanding from our results and placing emphasis on the energetic costs associated with the activation of the stress response, we proposed the “Energetic Model of Allostatic Load”. This model proposes that chronic stress causes a redirection of the energetic resources towards allostatic responses and away from growth, maintenance, and repair processes, which in turn leads to the accumulation of damage that will further contribute to the development of disease and increased risk of mortality. Finally, we highlighted new avenues to quantify allostatic load and its link to health via the integration of systemic and cellular energy expenditure measurements together with classic biomarkers, that could contribute to further advances in the stress field.