A Model of Late Long-Term Potentiation Simulates Aspects of Memory Maintenance

Late long-term potentiation (L-LTP) appears essential for the formation of long-term memory, with memories at least partly encoded by patterns of strengthened synapses. How memories are preserved for months or years, despite molecular turnover, is not well understood. Ongoing recurrent neuronal activity, during memory recall or during sleep, has been hypothesized to preferentially potentiate strong synapses, preserving memories. This hypothesis has not been evaluated in the context of a mathematical model representing biochemical pathways important for L-LTP. I incorporated ongoing activity into two such models: a reduced model that represents some of the essential biochemical processes, and a more detailed published model. The reduced model represents synaptic tagging and gene induction intuitively, and the detailed model adds activation of essential kinases by Ca. Ongoing activity was modeled as continual brief elevations of [Ca]. In each model, two stable states of synaptic weight resulted. Positive feedback between synaptic weight and the amplitude of ongoing Ca transients underlies this bistability. A tetanic or theta-burst stimulus switches a model synapse from a low weight to a high weight stabilized by ongoing activity. Bistability was robust to parameter variations. Simulations illustrated that prolonged decreased activity reset synapses to low weights, suggesting a plausible forgetting mechanism. However, episodic activity with shorter inactive intervals maintained strong synapses. Both models support experimental predictions. Tests of these predictions are expected to further understanding of how neuronal activity is coupled to maintenance of synaptic strength.Comment: Accepted to PLoS One. 8 figures at en

A Phenome-Wide Association Study (PheWAS) of Late Onset Alzheimer Disease Genetic Risk in Children of European Ancestry at Middle Childhood: Results from the ABCD Study®

Genetic risk for Late Onset Alzheimer Disease (AD) has been associated with lower cognition and smaller hippocampal volume in healthy young adults. However, whether these and other associations are present during childhood remains unclear. Using data from 5,556 genomically-confirmed European ancestry youth who completed the baseline session of the ongoing the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®), our phenome-wide association study estimating associations between four indices of genetic risk for late-onset AD (i.e., AD polygenic risk scores (PRS), APOE rs429358 genotype, AD PRS with the APOE region removed (ADPRS-APOE), and an interaction between ADPRS-APOE and APOE genotype) and 1,687 psychosocial, behavioral, and neural phenotypes revealed no significant associations after correction for multiple testing (all ps \u3e 0.0002; all pfdr \u3e 0.07). These data suggest that AD genetic risk may not phenotypically manifest during middle-childhood or that effects are smaller than this sample is powered to detect

A Spiking Neural Model of Episodic Memory Encoding and Replay in Hippocampus

As we experience life, we are constantly creating new memories, and the hippocampus plays an important role in the formation and recall of these episodic memories. This thesis begins by describing the neural mechanisms that make the hippocampus ideally suited for memory formation, consolidation and recall. We then describe a biologically plausible spiking-neuron model of the hippocampus' role in episodic memory. The model includes a mechanism for generating temporal indexing vectors, for associating these indices with experience vectors to form episodes, and for replaying the original experience vectors in sequence when prompted. The model also associates these episodes with context vectors using synaptic plasticity, such that it is able to retrieve an episodic memory associated with a given context and replay it, even after long periods of time. We demonstrate the model's ability to experience sequences of sensory information in the form of semantic pointer vectors and replay the same sequences later, comparing the results to experimental data. In particular, the model runs a T-maze experiment in which a simulated rat is forced to choose between left or right at a decision point, during which the neural ring patterns of the model's place cells closely match those found in real rats performing the same task. We demonstrate that the model is robust to both spatial and non-spatial data, since the vector representation of the input data remains the same in either case. To our knowledge, this is the rst spiking neural hippocampal model that can encode and recall sequences of both spatial and non-spatial data, while exhibiting temporal and spatial selectivity at a neural level

Neural patterns of hippocampus and amygdala supporting memory over long timespans

Episodic memory is an imperfect record of events arranged in time and space. When dealing with the storage of memories, the brain is faced with a predicament: it must retain an acceptably faithful facsimile of transpired events while simultaneously permitting inevitable modifications to accommodate learning new information. In this thesis, I first review contemporary theories of how memories can be stored in a neural substrate within the hippocampus, particularly in regards to how they can be arranged in time. Next, using in vivo calcium imaging, I detail how hippocampal “time cell” sequences could support encoding of behavioral events along multiple temporal dimensions. In this study, I trained mice to run in place on a treadmill, thereby measuring single-cell activity in CA1 as a function of time. Neurons in CA1 formed sequences, each cell firing one after another as if forming a scaffold upon which memories can be laid. These sequences were relatively well-preserved over a period of four days, satisfying the first requirement that information must be stored for a memory to persist. Additionally, these sequences also changed over time, which may be revealing a mechanism for how memories can change over time to assimilate new information. In the next experiment, I describe a collaborative project where we used immunohistochemistry, optogenetics, and calcium imaging to investigate the long-term dynamics of a fear memory. After mice initially associated a context with an aversive stimulus, they were placed in the same context over two days where they gradually relearned that the context was harmless. This produced molecular and neurophysiological signatures consistent with memory modification. However, after re-triggering fear, mice reverted to fearful expression with commensurate neural correlates. Using optogenetics, these behaviors could also be reliably suppressed. Finally, I conclude by synthesizing these findings with hippocampal literature on sequence formation and consolidation by proposing a holistic view of how these features can support episodic memory

General Psychology (Fall 2018)

This open textbook represents the version used in several Fall 2018 General Psychology courses at Valparaiso University.https://scholar.valpo.edu/psych_oer/1002/thumbnail.jp

General Psychology (Fall 2018)

This open textbook represents the version used in several Fall 2018 General Psychology courses at Valparaiso University.https://scholar.valpo.edu/psych_oer/1002/thumbnail.jp

Automated Deep Neural Network Approach for Detection of Epileptic Seizures

In this thesis, I focus on exploiting electroencephalography (EEG) signals for early seizure diagnosis in patients. This process is based on a powerful deep learning algorithm for times series data called Long Short-Term Memory (LSTM) network. Since manual and visual inspection (detection) of epileptic seizure through the electroencephalography (EEG) signal by expert neurologists is time-consuming, work-intensive and error-prone and it might take a couple hours for experts to analyze a single patient record and to do recognition when immediate action is needed to be taken. This thesis proposes a reliable automatic seizure/non-seizure classification method that could facilitate the identification process of characteristic epileptic patterns, such as pre-ictal spikes, seizures and determination of seizure frequency, seizure type, etc. In order to recognize epileptic seizure accurately, the proposed model exploits the temporal dependencies in the EEG data. Experiments on clinical data present that this method achieves a high seizure prediction accuracy and maintains reliable performance. This thesis also finds the most efficient lengths of EEG recording for highest accuracies of different classification in the automated seizure detection realm. It could help non-experts to predict the seizure more comprehensively and bring awareness to patients and caregivers of upcoming seizures, enhancing the daily lives of patients against unpredictable occurrence of seizures.Master of Science in Applied Computer Scienc

Information Processing in the Orbitofrontal Cortex and the Ventral Striatum in Rats Performing an Economic Decision-Making Task

University of Minnesota Ph.D. dissertation. August 2015. Major: Neuroscience. Advisor: David Redish. 1 computer file (PDF); vi, 144 pages.The orbitofrontal cortex (OFC) and ventral striatum (vStr) are key brain structures that represent information about value during decision-making tasks. Despite their very different anatomical properties, numerous studies have found similar patterns of value-related signaling in these structures. In particular, both structures are intimately involved in delay-discounting tasks, which involve a tradeoff between reward magnitude and delay to reward. However, the overlapping activity profiles of these brain regions makes it difficult to tease apart their specific contributions to delay-discounting behavior, and to economic decision-making more generally. In order to better understand the contributions of these two regions to value-based choice, we made simultaneous recordings in the OFC and vStr in rats performing a spatial variant of a traditional delay-discounting task. This allowed us to compare OFC and vStr activity directly in the same subjects while they engaged in a prototypical economic decision-making task, and additionally it allowed us to leverage the tools of spatial decoding analysis to measure non-local reward signaling. Chapter 1 provides an introduction to current theories of OFC and vStr function within the decision-making literature, in particular contrasting the concepts of neuroeconomics with the multiple decision-making systems framework. Chapter 2 describes the methods used in this thesis, including the design of the spatial delay-discounting task and the analysis of the neural data. Chapter 3 presents the results of single-unit and Bayesian decoding analyses from this dataset. We found that activity in the OFC and vStr was quite similar at the single-unit level, and inconsistent with the neuroeconomic account of value signaling in a common currency. Instead, when we looked specifically at moments of deliberative decision-making (as emphasized by the multiple systems account), we found important differences between the OFC and vStr. Both the OFC and the vStr showed covert reward signaling during deliberative, vicarious trial-and-error (VTE) behaviors. But vStr signals emerged earlier, before the moment of choice, while covert reward coding in the OFC appeared after the rats had committed to their decision. These analyses were extended to the level of local field potentials (LFPs), recorded from the same dataset. Local field potentials are a useful tool for studying local processing and interactions between brain regions. Chapter 4 describes the LFP results. Important among these was the finding that the vStr led the OFC at the LFP level (again showing temporal precedence), and furthermore, that the vStr was a stronger driver of OFC activity than vice versa, particularly during VTE. The implications of these results, along with those from the single-unit and Bayesian decoding analyses, are discussed in Chapter 5. Emphasis is placed on our emerging understanding of the role of the vStr in flexible behavior, and how the OFC and the vStr might cooperate to influence value-based choice