Introduction-Despite known health risks, nicotine use remains high, especially in populations diagnosed with mental illnesses, including anxiety disorders and Post-Traumatic Stress Disorder (PTSD). Smoking in these populations may relate to the effects of nicotine on emotional memories. The current study examined the effects of nicotine administration on the extinction of conditioned fear memories.
The hippocampus is critical to the temporal organization of our experiences. Although this fundamental capacity is conserved across modalities and species, its underlying neuronal mechanisms remain unclear. Here we recorded hippocampal activity as rats remembered an extended sequence of nonspatial events unfolding over several seconds, as in daily life episodes in humans. We then developed statistical machine learning methods to analyze the ensemble activity and discovered forms of sequential organization and coding important for order memory judgments. Specifically, we found that hippocampal ensembles provide significant temporal coding throughout nonspatial event sequences, differentiate distinct types of task-critical information sequentially within events, and exhibit theta-associated reactivation of the sequential relationships among events. We also demonstrate that nonspatial event representations are sequentially organized within individual theta cycles and precess across successive cycles. These findings suggest a fundamental function of the hippocampal network is to encode, preserve, and predict the sequential order of experiences.
The hippocampus plays a critical role in the memory for sequences of events, a defining feature of episodic memory. To shed light on the fundamental mechanisms supporting this capacity, we recently recorded neural activity in CA1 as rats performed a nonspatial odor sequence memory task. Our main finding was that, while the animals' location and behavior remained constant, a proportion of CA1 neurons fired differentially to odors depending on whether they were presented in or out of sequence (sequence cells). Here, we further examined if such sequence coding varied along the distal-to-proximal axis of the dorsal CA1 region (distal: toward subiculum; proximal: toward CA3). Differences in information processing along this axis have been suggested by recent anatomical and electrophysiological evidence that odor information may be more strongly represented in the distal segment, whereas spatial information may be more strongly represented in the proximal segment. Recorded neurons were grouped into four arbitrary sections of dorsal CA1, ranging from distal to proximal. We found that, although sequence cell coding was observed across the distal-to-proximal extent of CA1 from which we recorded, it was significantly higher in intermediate CA1, a region with more balanced anatomical input from lateral and medial entorhinal regions. More specifically, in that particular segment of CA1, we observed a significant increase in the magnitude of sequence coding of all cells, as well as in the sequential information content of sequence cells. Importantly, a different pattern was observed when examining the distribution of spatial coding from the same electrodes. Consistent with previous reports, our results suggest that spatial information was more strongly represented in the proximal section of CA1 (higher proportion of cells with place fields). These findings indicate that nonspatial sequence memory coding is not uniformly distributed along the transverse axis of CA1, and that this distribution does not simply follow the expected gradient based on the stimulus modality or the degree of spatial selectivity. Instead, the observed distribution suggests this form of sequence coding may be associated with convergent input from lateral and medial entorhinal regions, which is present throughout the proximodistal axis but higher in intermediate CA1.
Primary sensory cortical fields develop highly specific associative representational plasticity, notably enlarged area of representation of reinforced signal stimuli within their topographic maps. However, overtraining subjects after they have solved an instrumental task can reduce or eliminate the expansion while the successful behavior remains. As the development of this plasticity depends on the learning strategy used to solve a task, we asked whether the loss of expansion is due to the strategy used during overtraining. Adult male rats were trained in a three-tone auditory discrimination task to bar-press to the CS+ for water reward and refrain from doing so during the CS− tones and silent intertrial intervals; errors were punished by a flashing light and time-out penalty. Groups acquired this task to a criterion within seven training sessions by relying on a strategy that was “bar-press from tone-onset-to-error signal” (“TOTE”). Three groups then received different levels of overtraining: Group ST, none; Group RT, one week; Group OT, three weeks. Post-training mapping of their primary auditory fields (A1) showed that Groups ST and RT had developed significantly expanded representational areas, specifically restricted to the frequency band of the CS+ tone. In contrast, the A1 of Group OT was no different from naïve controls. Analysis of learning strategy revealed this group had shifted strategy to a refinement of TOTE in which they self-terminated bar-presses before making an error (“iTOTE”). Across all animals, the greater the use of iTOTE, the smaller was the representation of the CS+ in A1. Thus, the loss of cortical expansion is attributable to a shift or refinement in strategy. This reversal of expansion was considered in light of a novel theoretical framework (CONCERTO) highlighting four basic principles of brain function that resolve anomalous findings and explaining why even a minor change in strategy would involve concomitant shifts of involved brain sites, including reversal of cortical expansion.
Modeling correlation (and covariance) matrices can be challenging due to the positivedefiniteness constraint and potential high-dimensionality. Our approach is to decompose the covariance matrix into the correlation and variance matrices and propose a novel Bayesian framework based on modeling the correlations as products of unit vectors. By specifying a wide range of distributions on a sphere (e.g. the squared-Dirichlet distribution), the proposed approach induces flexible prior distributions for covariance matrices (that go beyond the commonly used inverse-Wishart prior). For modeling real-life spatio-temporal processes with complex dependence structures, we extend our method to dynamic cases and introduce unit-vector Gaussian process priors in order to capture the evolution of correlation among components of a multivariate time series. To handle the intractability of the resulting posterior, we introduce the adaptive ∆-Spherical Hamiltonian Monte Carlo. We demonstrate the validity and flexibility of our proposed framework in a simulation study of periodic processes and an analysis of rat's local field potential activity in a complex sequence memory task.
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