Sympathetic Nervous System Reactivity in Women Following Preeclamptic Pregnancies

Women who have had preeclamptic pregnancies are at risk for life-long cardiovascular disease. However, the factors contributing to this risk have yet to be established. Sympathetic nervous system dysregulation has been proposed to contribute to cardiovascular dysfunction during preeclamptic pregnancies. Therefore, we examined muscle sympathetic nerve activity (MSNA) at baseline and during a chemoreflex stimulus in women 6-24 months postpartum following a preeclamptic pregnancy (PE; n=6, age 28±2 y, BMI 27±3 kg/m2, 17±4 months postpartum). We hypothesized that MSNA responses to apnea would be greater in PE relative to control subjects, that is, women 6-24 months following a healthy pregnancy and with no history of disordered pregnancies (HP; n=6, 31±6 y, BMI 29±5 kg/m2, 17±4 months postpartum). Integrated MSNA recordings were obtained at baseline and during a voluntary end-inspiratory apnea. Baseline mean arterial pressure (MAP; 87±10 vs 95±10 mmHg, P=0.2), total peripheral resistance (TPR; 13±3 vs 14±1 mmHg/L/min, P=0.4), and heart rate (HR; 74±5 vs 74±13, P=0.9) were similar in PE vs HP. Baseline MSNA was higher in PE compared to HP (26±9 vs 14±6 bursts/100heartbeats, P\u3c0.01). The voluntary apnea was maintained for a similar duration in PE and HP (44±17 and 45±10 sec, P=0.9), without any difference in mean MAP (93±14 and 99±11, P=0.4), TPR (14±4 and 14±2, P=0.6), or HR (74±8 and 81±22, P=0.5) between groups. To discern between mild and moderate phases of chemoreflex stress, the apnea was divided into initial (i.e. first half) and latter (i.e. second half) phases for subsequent analyses. The initial phase of the apnea elicited a large increase in MSNA in the PE women which exceeded that observed in HP (37±13 vs 19±11, P=0.03, respectively). The peak sympathetic response observed in the latter half of the apnea was similar between PE and HP (56±21 vs 49±13 bursts/100hb, P=0.5). Thus, the sympathetic nervous system response to a mild chemoreflex stimulus is exaggerated in women who have had preeclampsia within the past 6-24 months relative to women without a history of preeclampsia. We have demonstrated that a recent history of preeclampsia is associated with chronic sympathetic activation as well as greater sympathetic reactivity. We propose these changes to the sympathetic nervous system contribute to the life-long risk for cardiovascular disease in formerly preeclamptic women. Funded by the Paul Titus Fellowship, Department of Obstetrics, Gynecology and Reproductive Sciences, Yale School of Medicin

Probabilistic Successor Representations with Kalman Temporal Differences

The effectiveness of Reinforcement Learning (RL) depends on an animal's ability to assign credit for rewards to the appropriate preceding stimuli. One aspect of understanding the neural underpinnings of this process involves understanding what sorts of stimulus representations support generalisation. The Successor Representation (SR), which enforces generalisation over states that predict similar outcomes, has become an increasingly popular model in this space of inquiries. Another dimension of credit assignment involves understanding how animals handle uncertainty about learned associations, using probabilistic methods such as Kalman Temporal Differences (KTD). Combining these approaches, we propose using KTD to estimate a distribution over the SR. KTD-SR captures uncertainty about the estimated SR as well as covariances between different long-term predictions. We show that because of this, KTD-SR exhibits partial transition revaluation as humans do in this experiment without additional replay, unlike the standard TD-SR algorithm. We conclude by discussing future applications of the KTD-SR as a model of the interaction between predictive and probabilistic animal reasoning.Comment: Conference on Cognitive Computational Neuroscienc

Rapid learning of predictive maps with STDP and theta phase precession

The predictive map hypothesis is a promising candidate principle for hippocampal function. A favoured formalisation of this hypothesis, called the successor representation, proposes that each place cell encodes the expected state occupancy of its target location in the near future. This predictive framework is supported by behavioural as well as electrophysiological evidence and has desirable consequences for both the generalisability and efficiency of reinforcement learning algorithms. However, it is unclear how the successor representation might be learnt in the brain. Error-driven temporal difference learning, commonly used to learn successor representations in artificial agents, is not known to be implemented in hippocampal networks. Instead, we demonstrate that spike-timing dependent plasticity (STDP), a form of Hebbian learning, acting on temporally compressed trajectories known as 'theta sweeps', is sufficient to rapidly learn a close approximation to the successor representation. The model is biologically plausible - it uses spiking neurons modulated by theta-band oscillations, diffuse and overlapping place cell-like state representations, and experimentally matched parameters. We show how this model maps onto known aspects of hippocampal circuitry and explains substantial variance in the temporal difference successor matrix, consequently giving rise to place cells that demonstrate experimentally observed successor representation-related phenomena including backwards expansion on a 1D track and elongation near walls in 2D. Finally, our model provides insight into the observed topographical ordering of place field sizes along the dorsal-ventral axis by showing this is necessary to prevent the detrimental mixing of larger place fields, which encode longer timescale successor representations, with more fine-grained predictions of spatial location

A probabilistic successor representation for context-dependent learning

Two of the main impediments to learning complex tasks are that relationships between different stimuli, including rewards, can be uncertain and context-dependent. Reinforcement learning (RL) provides a framework for learning, by predicting total future reward directly (model-free RL), or via predictions of future states (model-based RL). Within this framework, "successor representation" (SR) predicts total future occupancy of all states. A recent theoretical proposal suggests that the hippocampus encodes the SR in order to facilitate prediction of future reward. However, this proposal does not take into account how learning should adapt under uncertainty and switches of context. Here, we introduce a theory of learning SRs using prediction errors which includes optimally balancing uncertainty in new observations versus existing knowledge. We then generalize that approach to a multicontext setting, allowing the model to learn and maintain multiple task-specific SRs and infer which one to use at any moment based on the accuracy of its predictions. Thus, the context used for predictions can be determined by both the contents of the states themselves and the distribution of transitions between them. This probabilistic SR model captures animal behavior in tasks which require contextual memory and generalization, and unifies previous SR theory with hippocampal-dependent contextual decision-making. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Diffusion Generative Inverse Design

Inverse design refers to the problem of optimizing the input of an objective function in order to enact a target outcome. For many real-world engineering problems, the objective function takes the form of a simulator that predicts how the system state will evolve over time, and the design challenge is to optimize the initial conditions that lead to a target outcome. Recent developments in learned simulation have shown that graph neural networks (GNNs) can be used for accurate, efficient, differentiable estimation of simulator dynamics, and support high-quality design optimization with gradient- or sampling-based optimization procedures. However, optimizing designs from scratch requires many expensive model queries, and these procedures exhibit basic failures on either non-convex or high-dimensional problems.In this work, we show how denoising diffusion models (DDMs) can be used to solve inverse design problems efficiently and propose a particle sampling algorithm for further improving their efficiency. We perform experiments on a number of fluid dynamics design challenges, and find that our approach substantially reduces the number of calls to the simulator compared to standard techniques.Comment: ICML workshop on Structured Probabilistic Inference & Generative Modelin

Effectiveness of short term heat acclimation on intermittent sprint performance with moderately trained females controlling for menstrual cycle phase

YesIntroduction: Investigate the effectiveness of short-term heat acclimation (STHA), over 5-days (permissive dehydration), on an intermittent sprint exercise protocol (HST) with females. Controlling for menstrual cycle phase. Materials and Methods: Ten, moderately trained, females (Mean [SD]; age 22.6 [2.7] y; stature 165.3 [6.2] cm; body mass 61.5 [8.7] kg; VO˙ 2 peak 43.9 [8.6] mL·kg−1 ·min−1 ) participated. The HST (31.0◦C; 50%RH) was 9 × 5 min (45-min) of intermittent exercise, based on exercise intensities of female soccer players, using a motorized treadmill and Wattbike. Participants completed HST1 vs. HST2 as a control (C) trial. Followed by 90 min, STHA (no fluid intake), for five consecutive days in 39.5◦C; 60%RH, using controlled-hyperthermia (∼rectal temperature [Tre] 38.5◦C). The HST3 occurred within 1 week after STHA. The HST2 vs HST3 trials were in the luteal phase, using self-reported menstrual questionnaire and plasma 17β-estradiol. Results: Pre (HST2) vs post (HST3) STHA there was a reduction at 45-min in Tre by 0.20◦C (95%CI −0.30 to −0.10◦C; d = 0.77); Tsk (−0.50; −0.90 to −0.10◦C; d = 0.80); and Tb (−0.25; −0.35 to −0.15◦C; d = 0.92). Cardiac frequency reduced at 45-min (−8; −16 to −1 b·min−1 ; d = 1.11) and %PV increased (7.0; −0.4 to 14.5%: d = 1.27). Mean power output increased across all nine maximal sprints by 56W (−26 to 139W; d = 0.69; n = 9). There was limited difference (P > 0.05) for these measures in HST1 vs HST2 C trial. Discussion: Short-term heat acclimation (5-days) using controlled-hyperthermia, leads to physiological adaptation during intermittent exercise in the heat, in moderately trained females when controlling for menstrual cycle phase

Emergence of Sensory Representations Using Prediction in Partially Observable Environments

n order to explore and act autonomously in an environment,an agent can learn from the sensorimotor information that is capturedwhile acting. By extracting the regularities in this sensorimotor stream,it can build a model of the world, which in turn can be used as a basis foraction and exploration. It requires the acquisition of compact representa-tions from possibly high dimensional raw observations. In this paper, wepropose a model which integrates sensorimotor information over time,and project it in a sensory representation. It is trained by preformingsensorimotor prediction. We emphasize on a simple example the role ofmotor and memory for learning sensory representations