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At least 559 records · Page 31

Tracking the topology of neural manifolds across populations

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and in vivo experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds.

97 MATHEMATICS AND COMPUTING↗

Conformational free energy landscape of a glutamate transporter and microscopic details of its transport mechanism

Removing glutamate from the synaptic cleft is vital for proper function of the brain. Excitatory amino acid transporters mediate this process by uptaking the neurotransmitter from the synaptic cleft back to the cell after its release. The archaeal homolog, Glt Ph , an aspartate transporter fromPyrococcus horikoshii, presents the best structurally characterized model for this family of transporters. In order to transport, Glt Ph undergoes elevator-like conformational changes between inward-facing (IF) and outward-facing (OF) states. Here, we characterize, at an atomic level, the OF⇌IF transition of Glt Ph in differentapo/bound states using a combination of ensemble-based enhanced sampling techniques, employing more than two thousand of coupled simulation replicas of membrane-embedded Glt Ph . The resulting free-energy profiles portray the transition ofapo/bound states as a complex four-stage process, while sodium binding alone locks the structure in one of its states. Along the transition, the transport domain (TD) disengages from the scaffold domain (SD), allowing it to move as a piston sliding vertically with respect to the membrane during the elevator-like motion of TD. Lipid interactions with residues comprising the SD–TD interface directly influence the large-scale conformational changes and, consequently, the energetics of transport. Structural intermediates formed during the transition leak water molecules and may correlate to the uncoupled Cl − ion conductance observed experimentally in both prokaryotic and mammalian glutamate transporters. Mechanistic insights obtained from our study provide a structural framework for better development of therapeutic for neurological disorders.

Science & Technology - Other Topics↗

Myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia are indistinguishable by their cerebrospinal fluid proteomes

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia have overlapping neurologic symptoms particularly disabling fatigue. This has given rise to the question whether they are distinct central nervous system (CNS) entities or is one an extension of the other. To investigate this, we used unbiased quantitative mass spectrometry-based proteomics to examine the most proximal fluid to the brain, cerebrospinal fluid (CSF). This was to ascertain if the proteome profile of one was the same or different from the other. We examined two separate groups of ME/CFS, one with (n = 15) and one without (n = 15) fibromyalgia. We quantified a total of 2083 proteins using immunoaffinity depletion, tandem mass tag isobaric labelling and offline two-dimensional liquid chromatography coupled to tandem mass spectrometry, including 1789 that were quantified in all the CSF samples. ANOVA analysis did not yield any proteins with an adjusted p value <.05. This supports the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities.

60 APPLIED LIFE SCIENCES↗

EVAP: A two-photon imaging tool to study conformational changes in endogenous Kv2 channels in live tissues

A primary goal of molecular physiology is to understand how conformational changes of proteins affect the function of cells, tissues, and organisms. Here, we describe an imaging method for measuring the conformational changes of the voltage sensors of endogenous ion channel proteins within live tissue, without genetic modification. We synthesized GxTX-594, a variant of the peptidyl tarantula toxin guangxitoxin-1E, conjugated to a fluorophore optimal for two-photon excitation imaging through light-scattering tissue. We term this tool EVAP (Endogenous Voltage-sensor Activity Probe). GxTX-594 targets the voltage sensors of Kv2 proteins, which form potassium channels and plasma membrane–endoplasmic reticulum junctions. GxTX-594 dynamically labels Kv2 proteins on cell surfaces in response to voltage stimulation. To interpret dynamic changes in fluorescence intensity, we developed a statistical thermodynamic model that relates the conformational changes of Kv2 voltage sensors to degree of labeling. We used two-photon excitation imaging of rat brain slices to image Kv2 proteins in neurons. We found puncta of GxTX-594 on hippocampal CA1 neurons that responded to voltage stimulation and retain a voltage response roughly similar to heterologously expressed Kv2.1 protein. Our findings show that EVAP imaging methods enable the identification of conformational changes of endogenous Kv2 voltage sensors in tissue.

59 BASIC BIOLOGICAL SCIENCES↗

Where did the tumor start? An inverse solver with sparse localization for tumor growth models

In this work, we present a numerical scheme for solving an inverse problem for parameter estimation in tumor growth models for glioblastomas, a form of aggressive primary brain tumor. The growth model is a reaction–diffusion partial differential equation (PDE) for the tumor concentration. We use a PDE-constrained optimization formulation for the inverse problem. The unknown parameters are the reaction coefficient (proliferation), the diffusion coefficient (infiltration), and the initial condition field for the tumor PDE. Segmentation of magnetic resonance imaging (MRI) scans drive the inverse problem where segmented tumor regions serve as partial observations of the tumor concentration. Like most cases in clinical practice, we use data from a single time snapshot. Moreover, the precise time relative to the initiation of the tumor is unknown, which poses an additional difficulty for inversion. We perform a frozen-coefficient spectral analysis and show that the inverse problem is severely ill-posed. We introduce a biophysically motivated regularization on the structure and magnitude of the tumor initial condition. In particular, we assume that the tumor starts at a few locations (enforced with a sparsity constraint on the initial condition of the tumor) and that the initial condition magnitude in the maximum norm is equal to one. We solve the resulting optimization problem using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Our implementation uses PETSc and AccFFT libraries. We conduct numerical experiments on synthetic and clinical images to highlight the improved performance of our solver over a previously existing solver that uses standard two-norm regularization for the calibration parameters. The existing solver is unable to localize the initial condition. Our new solver can localize the initial condition and recover infiltration and proliferation. In clinical datasets (for which the ground truth is unknown), our solver results in qualitatively different solutions compared to the two-norm regularized solver.

97 MATHEMATICS AND COMPUTING↗

A minimum assumption approach to MEG sensor array design

Objective. Our objective is to formulate the problem of the magnetoencephalographic (MEG) sensor array design as a well-posed engineering problem of accurately measuring the neuronal magnetic fields. This is in contrast to the traditional approach that formulates the sensor array design problem in terms of neurobiological interpretability the sensor array measurements. Approach. We use the vector spherical harmonics (VSH) formalism to define a figure-of-merit for an MEG sensor array. We start with an observation that, under certain reasonable assumptions, any array of m perfectly noiseless sensors will attain exactly the same performance, regardless of the sensors' locations and orientations (with the exception of a negligible set of singularly bad sensor configurations). We proceed to the conclusion that under the aforementioned assumptions, the only difference between different array configurations is the effect of (sensor) noise on their performance. We then propose a figure-of-merit that quantifies, with a single number, how much the sensor array in question amplifies the sensor noise. Main results. We derive a formula for intuitively meaningful, yet mathematically rigorous figure-of-merit that summarizes how desirable a particular sensor array design is. We demonstrate that this figure-of-merit is well-behaved enough to be used as a cost function for a general-purpose nonlinear optimization methods such as simulated annealing. We also show that sensor array configurations obtained by such optimizations exhibit properties that are typically expected of 'high-quality' MEG sensor arrays, e.g. high channel information capacity. Significance. Our work paves the way toward designing better MEG sensor arrays by isolating the engineering problem of measuring the neuromagnetic fields out of the bigger problem of studying brain function through neuromagnetic measurements.

60 APPLIED LIFE SCIENCES↗

Roadmap for unconventional computing with nanotechnology

Abstract In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.

Finocchio, Giovanni (ORCID:0000000210433876)↗

Scaling neural simulations in STACS

Abstract As modern neuroscience tools acquire more details about the brain, the need to move towards biological-scale neural simulations continues to grow. However, effective simulations at scale remain a challenge. Beyond just the tooling required to enable parallel execution, there is also the unique structure of the synaptic interconnectivity, which is globally sparse but has relatively high connection density and non-local interactions per neuron. There are also various practicalities to consider in high performance computing applications, such as the need for serializing neural networks to support potentially long-running simulations that require checkpoint-restart. Although acceleration on neuromorphic hardware is also a possibility, development in this space can be difficult as hardware support tends to vary between platforms and software support for larger scale models also tends to be limited. In this paper, we focus our attention on Simulation Tool for Asynchronous Cortical Streams (STACS), a spiking neural network simulator that leverages the Charm++ parallel programming framework, with the goal of supporting biological-scale simulations as well as interoperability between platforms. Central to these goals is the implementation of scalable data structures suitable for efficiently distributing a network across parallel partitions. Here, we discuss a straightforward extension of a parallel data format with a history of use in graph partitioners, which also serves as a portable intermediate representation for different neuromorphic backends. We perform scaling studies on the Summit supercomputer, examining the capabilities of STACS in terms of network build and storage, partitioning, and execution. We highlight how a suitably partitioned, spatially dependent synaptic structure introduces a communication workload well-suited to the multicast communication supported by Charm++. We evaluate the strong and weak scaling behavior for networks on the order of millions of neurons and billions of synapses, and show that STACS achieves competitive levels of parallel efficiency.

59 BASIC BIOLOGICAL SCIENCES↗

Deep learning approaches for neural decoding across architectures and recording modalities

Decoding behavior, perception or cognitive state directly from neural signals is critical for brain–computer interface research and an important tool for systems neuroscience. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks ranging from speech recognition to image segmentation. The success of deep networks in other domains has led to a new wave of applications in neuroscience. In this article, we review deep learning approaches to neural decoding. Here, we describe the architectures used for extracting useful features from neural recording modalities ranging from spikes to functional magnetic resonance imaging. Furthermore, we explore how deep learning has been leveraged to predict common outputs including movement, speech and vision, with a focus on how pretrained deep networks can be incorporated as priors for complex decoding targets like acoustic speech or images. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.

59 BASIC BIOLOGICAL SCIENCES↗

Current and future directions in network biology

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

59 BASIC BIOLOGICAL SCIENCES↗

Diabetes and hypertension are related to amyloid-beta burden in the population-based Rotterdam Study

Abstract Higher vascular disease burden increases the likelihood of developing dementia, including Alzheimer’s disease. Better understanding the association between vascular risk factors and Alzheimer’s disease pathology at the predementia stage is critical for developing effective strategies to delay cognitive decline. In this work, we estimated the impact of six vascular risk factors on the presence and severity of in vivo measured brain amyloid-beta (Aβ) plaques in participants from the population-based Rotterdam Study. Vascular risk factors (hypertension, hypercholesterolaemia, diabetes, obesity, physical inactivity and smoking) were assessed 13 (2004–2008) and 7 years (2009–2014) prior to 18F-florbetaben PET (2018–2021) in 635 dementia-free participants. Vascular risk factors were associated with binary amyloid PET status or continuous PET readouts (standard uptake value ratios, SUVrs) using logistic and linear regression models, respectively, adjusted for age, sex, education, APOE4 risk allele count and time between vascular risk and PET assessment. Participants’ mean age at time of amyloid PET was 69 years (range: 60–90), 325 (51.2%) were women and 190 (29.9%) carried at least one APOE4 risk allele. The adjusted prevalence estimates of an amyloid-positive PET status markedly increased with age [12.8% (95% CI 11.6; 14) in 60–69 years versus 35% (36; 40.8) in 80–89 years age groups] and APOE4 allele count [9.7% (8.8; 10.6) in non-carriers versus 38.4% (36; 40.8) to 60.4% (54; 66.8) in carriers of one or two risk allele(s)]. Diabetes 7 years prior to PET assessment was associated with a higher risk of a positive amyloid status [odds ratio (95% CI) = 3.68 (1.76; 7.61), P < 0.001] and higher standard uptake value ratios, indicating more severe Aβ pathology [standardized beta = 0.40 (0.17; 0.64), P = 0.001]. Hypertension was associated with higher SUVr values in APOE4 carriers (mean SUVr difference of 0.09), but not in non-carriers (mean SUVr difference 0.02; P = 0.005). In contrast, hypercholesterolaemia was related to lower SUVr values in APOE4 carriers (mean SUVr difference −0.06), but not in non-carriers (mean SUVr difference 0.02). Obesity, physical inactivity and smoking were not related to amyloid PET measures. The current findings suggest a contribution of diabetes, hypertension and hypercholesterolaemia to the pathophysiology of Alzheimer’s disease in a general population of older non-demented adults. As these conditions respond well to lifestyle modification and drug treatment, further research should focus on the preventative effect of early risk management on the development of Alzheimer’s disease neuropathology.

van Arendonk, Joyce (ORCID:0000000172941633)↗

Striatal dopamine synthesis and cognitive flexibility differ between hormonal contraceptive users and nonusers

In rodents and nonhuman primates, sex hormones are powerful modulators of dopamine (DA) neurotransmission. Yet less is known about hormonal regulation of the DA system in the human brain. Using positron emission tomography (PET), we address this gap by comparing hormonal contraceptive users and nonusers across multiple aspects of DA function: DA synthesis capacity via the PET radioligand 6-[ 18 F]fluoro-m-tyrosine ([ 18 F]FMT), baseline D2/3 receptor binding potential using [ 11 C]raclopride, and DA release using methylphenidate-paired [ 11 C]raclopride. Participants consisted of 36 healthy women (n = 15 hormonal contraceptive users; n = 21 naturally cycling/non users of hormonal contraception), and men (n = 20) as a comparison group. A behavioral index of cognitive flexibility was assessed prior to PET imaging. Hormonal contraceptive users exhibited greater DA synthesis capacity than NC participants, particularly in dorsal caudate, and greater cognitive flexibility. Furthermore, across individuals, the magnitude of striatal DA synthesis capacity was associated with cognitive flexibility. No group differences were observed in D2/3 receptor binding or DA release. Analyses by sex alone may obscure underlying differences in DA synthesis tied to women’s hormone status. Hormonal contraception (in the form of pill, shot, implant, ring, or intrauterine device) is used by ~400 million women worldwide, yet few studies have examined whether chronic hormonal manipulations impact basic properties of the DA system. Findings from this study begin to address this critical gap in women’s health.

59 BASIC BIOLOGICAL SCIENCES↗

Identification of conserved skeletal enhancers associated with craniosynostosis risk genes

Abstract Craniosynostosis, defined by premature fusion of one or multiple cranial sutures, is a common congenital defect affecting more than 1/2000 infants and results in restricted brain expansion. Single gene mutations account for 15%–20% of cases, largely as part of a syndrome, but the majority are nonsyndromic with complex underlying genetics. We hypothesized that the two noncoding genomic regions identified by a GWAS for craniosynostosis contain distal regulatory elements for the risk genes BMPER and BMP2. To identify such regulatory elements, we surveyed conserved noncoding sequences from both risk loci for enhancer activity in transgenic Danio rerio. We identified enhancers from both regions that direct expression to skeletal tissues, consistent with the endogenous expression of bmper and bmp2. For each locus, we also found a skeletal enhancer that also contains a sequence variant associated with craniosynostosis risk. We examined the activity of each enhancer during craniofacial development and found that the BMPER-associated enhancer is active in the restricted region of cartilage closely associated with frontal bone initiation. The same enhancer is active in mouse skeletal tissues, demonstrating evolutionarily conserved activity. Using enhanced yeast one-hybrid assays, we identified transcription factors that bind each enhancer and observed differential binding between alleles, implicating multiple signaling pathways. Our findings help unveil the genetic mechanism of the two craniosynostosis risk loci. More broadly, our combined in vivo approach is applicable to many complex genetic diseases to build a link between association studies and specific genetic mechanisms.

He(何璇), Xuan Anita (ORCID:0000000313221972)↗

Electrochemical Amyloid β Immunosensor Based on Ti 3 C 2 T x MXene Nanosheets

Alzheimer’s disease (AD) is the most common neurodegenerative disease and a major health-care problem. More than 55 million people in the world, including 6.7 million Americans, are living with AD. As AD is characterized by production and deposition of β-amyloid peptide (Aβ) in the brain, Aβ is considered as a potential biomarker for diagnosing and monitoring the progression of AD. Nanomaterials-based electrochemical immunosensor is one of the most promising methods for early diagnosis and therapeutic of AD, due to its highly selective, sensitive, and label-free advantages. The graphene-like two-dimensional (2D) MXene nanosheets have shown large surface area, good conductivity, biocompatibility, high ion transport, and low diffusion barrier, attracting attention in recent years to use it as the nanomaterial substrate for biosensor fabrication. In this paper, we presented a Ti 3 C 2 T x MXene nanosheets-based electrochemical immunosensor to detect trace Aβ for early diagnosis of AD.

59 BASIC BIOLOGICAL SCIENCES↗

HIV influences microtubule associated protein-2: potential marker of HIV-associated neurocognitive disorders

Postmortem brains of patients diagnosed with HIV-1-associated neurocognitive disorders (HAND) exhibit loss of dendrites. However, the mechanisms by which synapses are damaged are not fully understood. Dendrite length and remodeling occurs via microtubules, the dynamics of which are regulated by microtubule-binding proteins, including microtubule-associated protein 2 (MAP2). The HIV protein gp120 is neurotoxic and interferes with neuronal microtubules. We measured MAP2 concentrations in human cerebrospinal fluid (CSF) and MAP2 immunoreactivity in rat cortical neurons exposed to HIV and gp120. First, we examined whether HIV affects MAP2 levels by analyzing the CSF of 27 persons living with HIV (PLH) whose neurocognitive performance had been characterized. We then used rat cortical neurons to study the mechanisms of HIV-mediated dendritic loss. PLH who had HAND had greater MAP2 concentrations within the CSF than cognitive normal PLH. In cortical neurons, the deleterious effect of HIV on MAP2-positive dendrites occurred through a gp120-mediated mechanism. The neurotoxic effect of HIV was blocked by a CCR5 antagonist and prevented by Helix-A, a peptide that displaces gp120 from binding to microtubules, conjugated to a nanolipoprotein particle delivery platform. Our findings support that HIV at least partially effects its neurotoxicity via neuronal cytoskeleton modifications and provide evidence of a new therapeutic compound that could be used to prevent the HIV-associated neuropathology.

60 APPLIED LIFE SCIENCES↗

Reinforcement expectation in the honeybee ( Apis mellifera ): Can downshifts in reinforcement show conditioned inhibition?

When animals learn the association of a conditioned stimulus (CS) with an unconditioned stimulus (US), later presentation of the CS invokes a representation of the US. When the expected US fails to occur, theoretical accounts predict that conditioned inhibition can accrue to any other stimuli that are associated with this change in the US. Empirical work with mammals has confirmed the existence of conditioned inhibition. But the way it is manifested, the conditions that produce it, and determining whether it is the opposite of excitatory conditioning are important considerations. Invertebrates can make valuable contributions to this literature because of the well-established conditioning protocols and access to the central nervous system (CNS) for studying neural underpinnings of behavior. Nevertheless, although conditioned inhibition has been reported, it has yet to be thoroughly investigated in invertebrates. Here, we evaluate the role of the US in producing conditioned inhibition by using proboscis extension response conditioning of the honeybee (Apis mellifera). Specifically, using variations of a “feature-negative” experimental design, we use downshifts in US intensity relative to US intensity used during initial excitatory conditioning to show that an odorant in an odor–odor mixture can become a conditioned inhibitor. We argue that some alternative interpretations to conditioned inhibition are unlikely. However, we show variation across individuals in how strongly they show conditioned inhibition, with some individuals possibly revealing a different means of learning about changes in reinforcement. We discuss how the resolution of these differences is needed to fully understand whether and how conditioned inhibition is manifested in the honeybee, and whether it can be extended to investigate how it is encoded in the CNS. It is also important for extension to other insect models. In particular, work like this will be important as more is revealed of the complexity of the insect brain from connectome projects.

60 APPLIED LIFE SCIENCES↗

Probabilistic inference in very large universes

Our current favored cosmological theories allow for the striking and controversial possibility that the observable universe is just a small part of a much larger universe in which parameters that describe the effective, low-energy laws of physics vary from one region to another. The controversy is largely driven by the fact that such a “very large universe” is mostly observationally inaccessible to us, so the issue arises of how we can reasonably assess a theory that describes such a universe. In this paper, we propose a Bayesian method for theory assessment based on theory-generated probability distributions for our observations. We focus on the principles that define this method, leaving aside concerns about how, in practice, one would carry out the required calculations. (One important issue that we set aside is the measure problem.) We argue that cosmological theories can be tested by the standard method of Bayesian updating, but we need to use theoretical predictions for “first-person” probabilities—that is, probabilities that we should use for our observations, taking into account all relevant selection effects. These selection effects can vary from one observer to another and can vary with time, so, in principle, first-person probabilities are defined for each observer instant—an observer at a specific instant of time. Calculations of first-person probabilities should take into account everything that the observer believes about herself and her surroundings, which we refer to as her subjective state. If the universe is very large, a theory might predict that there are many observer instants in the same subjective state; we argue that first-person probabilities should be calculated using a principle of self-locating indifference (PSLI), the assumption that any real observer should make predictions for her future as if she were chosen randomly and uniformly from the theoretically predicted observer instants that share her subjective state. We believe the PSLI is intuitively very reasonable, but we also argue that, if the theory is correct, the use of this principle maximizes the expected fraction of observers who will make correct predictions. A further complication is that cosmological theories are not expected to fully predict the detailed properties of the universe, but rather will predict a set of possible universes, each with a probability. Different possible universes will generically have different numbers of observers. We argue that, in the calculation of first-person probabilities, the probability for each possible universe should be weighted by the number of observer instants in the specified subjective state that it contains. These issues have been controversial in the literature, so we also provide a rebuttal to the claim that principles like the PSLI involve a “selection fallacy”; a rebuttal to what we dub the principle of required certainty; an argument rejecting theories that predict a preponderance of Boltzmann brains; a rebuttal to a parable about humans and Jovians used by Hartle and Srednicki to argue that assumptions of typicality can lead to absurd consequences; and, finally, a discussion about how the use of “old evidence” can be fit into a Bayesian mold.

Azhar, Feraz [University of Notre Dame, IN (United↗

Fluctuations of subsystem entropies at late times

We study the fluctuations of subsystem entropies in closed quantum many-body systems after thermalization. Using a combination of analytics and numerics for both random quantum circuits and Hamiltonian dynamics, we find that the statistics of such entropy fluctuations is drastically different than in the classical setting. For instance, shortly after a system thermalizes, the probability of entropy fluctuations for a subregion is suppressed in the dimension of the Hilbert space of the complementary subregion. This suppression becomes increasingly stringent as a function of time, ultimately depending on the exponential of the Hilbert space dimension, until extremely late times when the amount of suppression saturates. We also use our results to estimate the total number of rare fluctuations at large timescales. We find that the “Boltzmann brain” paradox is largely ameliorated in quantum many-body systems, in contrast with the classical setting.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗