The human brain relies on the complex interactions of distinct brain regions to support cognitive processes. The interplay between the hippocampus and neocortical regions plays a key role in the formation, storage, and retrieval of long-term episodic memories. Oscillatory activities during sleep are a fundamental mechanism that binds distributed neuronal networks into functionally coherent ensembles. However, the large-scale hippocampal-neocortical oscillatory mechanisms that support flexible modulation of long-term memory remain poorly understood. Furthermore, alterations to physiologic spatiotemporal patterns that are essential for intact memory function can result in pathophysiology in brain disorders such as focal epilepsy. Investigating how epileptic network activity disrupts connectivity in distributed networks and the organization of oscillatory activity are additional crucial areas that require further research. Our experiments on rodents and human patients with epilepsy have provided valuable insights into these mechanisms. In rodents, we used high-density microelectrode arrays in tandem with hippocampal probes to analyze intracranial electroencephalography (iEEG) from multiple cortical regions and the hippocampus. We identified key hippocampal-cortical oscillatory biomarkers that were differentially coordinated based on the age, strength, and type of memory. We also analyzed iEEG from patients with focal epilepsy and demonstrated how individualized pattern of pathologic-physiologic coupling can disrupt large-scale memory circuits. Our findings may offer new opportunities for therapies aimed at addressing distributed network dysfunction in various neuropsychiatric disorders.

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Food-caching birds like black-capped chickadees offer unique advantages for studying neural processes underlying episodic memory. Chickadees exhibit prodigious memories—they can cache thousands of food items throughout their environment and use memory to navigate back to these hidden food stores. Additionally, their hippocampal circuit is simplified relative to that of mammals, containing far fewer inputs and outputs. However, little work had been done to understand the neural processes underlying these animal’s memory abilities. This thesis details several projects that aimed to better establish food-caching birds as an animal model of memory for systems neuroscience. In Chapter 2, we described the creation of behavioral tasks to utilize the chickadees’ natural memory behavior. Here, we monitored chickadees’ behavior while they cached food into a grid of sites covered by rubber flaps. We then applied probabilistic modeling to examine how different strategies guided birds’ choices during caching and retrieval. Chickadees used memories of the contents of individual cache sites in a context-dependent manner, avoiding sites that contained food during caching and returning to those same sites during retrieval. These results demonstrate memory flexibility in an animal in a tractable spatial paradigm. In Chapter 3, we asked whether the bird brain had a region that was similar to the entorhinal cortex. We found that the dorsal lateral hippocampal formation (DL/CDL), one of the main inputs to the chickadee hippocampus, sharded marked anatomical and physiological similarities to the mammalian entorhinal cortex. We first used retrograde and anterograde tracing to examine the connectivity between DL/CDL and the hippocampus, as well as DL and the rest of the pallium. We found that the topographic patterns of DL/CDL input were similar to those of the mammalian entorhinal cortex. We next examined the physiology of DL, using 1-photon calcium imaging to monitor neural activity while birds performed a random foraging task. Like the entorhinal cortex, DL contained multi-field ‘grid-like’ spatial neurons, as well as border cells, head direction cells and speed-tuned cells. Collectively, these results establish DL/CDL as an entorhinal cortex analog. In Chapter 4, we expanded the anatomical analysis to examine all of the inputs to the hippocampal formation. We varied our injections of retrograde tracers along the hippocampal long and transverse axes to examine if, like in mammals, there were topographic input patterns along these major axes. We found many patterns in input that were highly reminiscent of mammalian connectivity: like in rodents, visual pallial input preferentially innervated the septal portion of the hippocampus, while amygdala input preferentially targeted the temporal portion. These results further solidify the homology between the mammalian and avian hippocampal formations. Collectively, through these sets of experiments, we have laid the groundwork for studying the black-capped chickadee in a modern neuroscience context.

Biological brains are composed of neurons, interconnected by synapses to create large complex networks. Learning and memory occur, in large part, due to synaptic plasticity -- modifications in the efficacy of information transmission through these synaptic connections. Artificial neural networks model these with neural "units" which communicate through synaptic weights. Models of learning and memory propose synaptic plasticity rules that describe and predict the weight modifications. An equally important but under-evaluated question is the selection of \textit{which} synapses should be updated in response to a memory event. In this work, we attempt to separate the questions of synaptic plasticity from that of memory addressing. Chapter 1 provides an overview of the problem of memory addressing and a summary of the solutions that have been considered in computational neuroscience and artificial intelligence, as well as those that may exist in biology. Chapter 2 presents in detail a solution to memory addressing and synaptic plasticity in the context of familiarity detection, suggesting strong feedforward weights and anti-Hebbian plasticity as the respective mechanisms. Chapter 3 proposes a model of recall, with storage performed by addressing through local third factors and neo-Hebbian plasticity, and retrieval by content-based addressing. In Chapter 4, we consider the problem of concurrent memory consolidation and memorization. Both storage and retrieval are performed by content-based addressing, but the plasticity rule itself is implemented by gradient descent, modulated according to whether an item should be stored in a distributed manner or memorized verbatim. However, the classical method for computing gradients in recurrent neural networks, backpropagation through time, is generally considered unbiological. In Chapter 5 we suggest a more realistic implementation through an approximation of recurrent backpropagation. Taken together, these results propose a number of potential mechanisms for memory storage and retrieval, each of which separates the mechanism of synaptic updating -- plasticity -- from that of synapse selection -- addressing. Explicit studies of memory addressing may find applications not only in artificial intelligence but also in biology. In artificial networks, for example, selectively updating memories in large language models can help improve user privacy and security. In biological ones, understanding memory addressing can help with health outcomes and treating memory-based illnesses such as Alzheimers or PTSD.

Memory-related disorders, such as Alzheimer’s disease and dementia, are devastating and often irreparable given our limited knowledge of how to effectively treat them. Animal studies have made significant advances in identifying neural correlates of memory, but in order to develop better interventions for memory loss, we need a deeper understanding of the neural basis of memory in the human brain. The main focus of my research is examining large-scale electrophysiological correlates of memory and learning in humans. In my studies, I recorded local field potential (LFP) data directly from the brains of neurosurgical patients performing memory tasks. First, in Chapter 2, I investigated the prevalence of sharp-wave ripples—synchronous high-frequency bursts of LFP activity—in the human hippocampus and cortex. I found that spectral characteristics of detected ripples closely matched those of other previously described high-frequency patterns in the human brain, thus raising important considerations for the detection and definition of ripple-like activity in humans. For my second study, in Chapter 3, I examined the impact of scopolamine, a cholinergic blocker, in the human hippocampal area during episodic memory. I found that the memory impairment caused by scopolamine was coupled to disruptions of both the amplitude and phase alignment of theta oscillations (2-10 Hz) during encoding. These findings suggest that cholinergic circuits support memory by coordinating the temporal dynamics of theta oscillations. Finally, in Chapter 4, I explored how brain oscillations in the medial temporal lobe (MTL) support learning. I found that subjects’ accuracy in a spatial memory task improved significantly within and across sessions, and that these short- and long-term learning effects were predicted by greater theta synchrony. My research translates important memory- and learning-related signals from animal studies, and extends those findings by revealing spectral patterns that are specifically relevant to humans. Together, my studies point to a key electrophysiological phenomenon underlying memory and learning in humans: the synchrony of neuronal activity in the brain. In particular, my results suggest that the temporal coordination of neuronal activity offered by brain oscillations, especially those in the theta frequency band, is vital for successful memory and learning. These findings expand our mechanistic understanding of the neurophysiology of human memory and learning, and suggest that improving the temporal coordination of neuronal activity in the MTL may provide a novel route to treating memory- and learning-related disorders.

The entorhinal cortex (EC) is central to the brain’s navigation system. Its subregions are conventionally thought to compute dichotomous representations for spatial processing: medial entorhinal cortex (MEC) provides a global spatial map, while lateral entorhinal cortex (LEC) encodes specific sensory details of experience. While local recordings of EC circuits have amassed a vast catalogue of specialized cell types that could support navigational computations in the brain, we have little direct evidence for how these signals are actually transmitted outside of the EC to its primary downstream reader, the hippocampus, which itself is critical for the formation of spatial and episodic memories. Here we exploit in vivo sub-cellular imaging to directly record from EC axon terminals as they locally innervate hippocampal area CA1, while mice performed navigation and spatial learning tasks in virtual reality. We find both distinct and overlapping representations of task, location, and context in both MEC and LEC axons. While MEC transmitted a highly location- and context-specific code, LEC inputs were strongly biased by ongoing navigational goals and reward. Surprisingly, the position of the animal could be accurately decoded from either entorhinal subregion. Our results challenge prevailing dogma on the routing of spatial and non-spatial information from the cortex to the hippocampus, indicating that cortical interactions upstream of the hippocampus are critical for combining these processing streams to support navigation and memory.

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