Topic: Pressure

Screen Savers : Protecting adolescent mental health in a digital world

In our rapidly evolving digital world, there is increasing concern about the impact of digital technologies and social media on the mental health of young people. Policymakers and the public are nervous. Psychologists are facing mounting pressures to deliver evidence that can inform policies and practices to safeguard both young people and society at large. However, research progress is slow while technological change is accelerating.My talk will reflect on this, both as a question of psychological science and metascience. Digital companies have designed highly popular environments that differ in important ways from traditional offline spaces. By revisiting the foundations of psychology (e.g. development and cognition) and considering digital changes' impact on theories and findings, we gain deeper insights into questions such as the following. (1) How do digital environments exacerbate developmental vulnerabilities that predispose young people to mental health conditions? (2) How do digital designs interact with cognitive and learning processes, formalised through computational approaches such as reinforcement learning or Bayesian modelling?However, we also need to face deeper questions about what it means to do science about new technologies and the challenge of keeping pace with technological advancements. Therefore, I discuss the concept of ‘fast science’, where, during crises, scientists might lower their standards of evidence to come to conclusions quicker. Might psychologists want to take this approach in the face of technological change and looming concerns? The talk concludes with a discussion of such strategies for 21st-century psychology research in the era of digitalization.

Error Consistency between Humans and Machines as a function of presentation duration

Within the last decade, Deep Artificial Neural Networks (DNNs) have emerged as powerful computer vision systems that match or exceed human performance on many benchmark tasks such as image classification. But whether current DNNs are suitable computational models of the human visual system remains an open question: While DNNs have proven to be capable of predicting neural activations in primate visual cortex, psychophysical experiments have shown behavioral differences between DNNs and human subjects, as quantified by error consistency. Error consistency is typically measured by briefly presenting natural or corrupted images to human subjects and asking them to perform an n-way classification task under time pressure. But for how long should stimuli ideally be presented to guarantee a fair comparison with DNNs? Here we investigate the influence of presentation time on error consistency, to test the hypothesis that higher-level processing drives behavioral differences. We systematically vary presentation times of backward-masked stimuli from 8.3ms to 266ms and measure human performance and reaction times on natural, lowpass-filtered and noisy images. Our experiment constitutes a fine-grained analysis of human image classification under both image corruptions and time pressure, showing that even drastically time-constrained humans who are exposed to the stimuli for only two frames, i.e. 16.6ms, can still solve our 8-way classification task with success rates way above chance. We also find that human-to-human error consistency is already stable at 16.6ms.

Mathematical and computational modelling of ocular hemodynamics: from theory to applications

Changes in ocular hemodynamics may be indicative of pathological conditions in the eye (e.g. glaucoma, age-related macular degeneration), but also elsewhere in the body (e.g. systemic hypertension, diabetes, neurodegenerative disorders). Thanks to its transparent fluids and structures that allow the light to go through, the eye offers a unique window on the circulation from large to small vessels, and from arteries to veins. Deciphering the causes that lead to changes in ocular hemodynamics in a specific individual could help prevent vision loss as well as aid in the diagnosis and management of diseases beyond the eye. In this talk, we will discuss how mathematical and computational modelling can help in this regard. We will focus on two main factors, namely blood pressure (BP), which drives the blood flow through the vessels, and intraocular pressure (IOP), which compresses the vessels and may impede the flow. Mechanism-driven models translates fundamental principles of physics and physiology into computable equations that allow for identification of cause-to-effect relationships among interplaying factors (e.g. BP, IOP, blood flow). While invaluable for causality, mechanism-driven models are often based on simplifying assumptions to make them tractable for analysis and simulation; however, this often brings into question their relevance beyond theoretical explorations. Data-driven models offer a natural remedy to address these short-comings. Data-driven methods may be supervised (based on labelled training data) or unsupervised (clustering and other data analytics) and they include models based on statistics, machine learning, deep learning and neural networks. Data-driven models naturally thrive on large datasets, making them scalable to a plethora of applications. While invaluable for scalability, data-driven models are often perceived as black- boxes, as their outcomes are difficult to explain in terms of fundamental principles of physics and physiology and this limits the delivery of actionable insights. The combination of mechanism-driven and data-driven models allows us to harness the advantages of both, as mechanism-driven models excel at interpretability but suffer from a lack of scalability, while data-driven models are excellent at scale but suffer in terms of generalizability and insights for hypothesis generation. This combined, integrative approach represents the pillar of the interdisciplinary approach to data science that will be discussed in this talk, with application to ocular hemodynamics and specific examples in glaucoma research.

Social and non-social learning: Common, or specialised, mechanisms? (BACN Early Career Prize Lecture 2022)

The last decade has seen a burgeoning interest in studying the neural and computational mechanisms that underpin social learning (learning from others). Many findings support the view that learning from other people is underpinned by the same, ‘domain-general’, mechanisms underpinning learning from non-social stimuli. Despite this, the idea that humans possess social-specific learning mechanisms - adaptive specializations moulded by natural selection to cope with the pressures of group living - persists. In this talk I explore the persistence of this idea. First, I present dissociations between social and non-social learning - patterns of data which are difficult to explain under the domain-general thesis and which therefore support the idea that we have evolved special mechanisms for social learning. Subsequently, I argue that most studies that have dissociated social and non-social learning have employed paradigms in which social information comprises a secondary, additional, source of information that can be used to supplement learning from non-social stimuli. Thus, in most extant paradigms, social and non-social learning differ both in terms of social nature (social or non-social) and status (primary or secondary). I conclude that status is an important driver of apparent differences between social and non-social learning. When we account for differences in status, we see that social and non-social learning share common (dopamine-mediated) mechanisms.

Sleep deprivation and the human brain: from brain physiology to cognition”

Sleep strongly affects synaptic strength, making it critical for cognition, especially learning and memory formation. Whether and how sleep deprivation modulates human brain physiology and cognition is poorly understood. Here we examined how overnight sleep deprivation vs overnight sufficient sleep affects (a) cortical excitability, measured by transcranial magnetic stimulation, (b) inducibility of long-term potentiation (LTP)- and long-term depression (LTD)-like plasticity via transcranial direct current stimulation (tDCS), and (c) learning, memory, and attention. We found that sleep deprivation increases cortical excitability due to enhanced glutamate-related cortical facilitation and decreases and/or reverses GABAergic cortical inhibition. Furthermore, tDCS-induced LTP-like plasticity (anodal) abolishes while the inhibitory LTD-like plasticity (cathodal) converts to excitatory LTP-like plasticity under sleep deprivation. This is associated with increased EEG theta oscillations due to sleep pressure. Motor learning, behavioral counterparts of plasticity, and working memory and attention, which rely on cortical excitability, are also impaired during sleep deprivation. Our study indicates that upscaled brain excitability and altered plasticity, due to sleep deprivation, are associated with impaired cognitive performance. Besides showing how brain physiology and cognition undergo changes (from neurophysiology to higher-order cognition) under sleep pressure, the findings have implications for variability and optimal application of noninvasive brain stimulation.

From spikes to factors: understanding large-scale neural computations

It is widely accepted that human cognition is the product of spiking neurons. Yet even for basic cognitive functions, such as the ability to make decisions or prepare and execute a voluntary movement, the gap between spikes and computation is vast. Only for very simple circuits and reflexes can one explain computations neuron-by-neuron and spike-by-spike. This approach becomes infeasible when neurons are numerous the flow of information is recurrent. To understand computation, one thus requires appropriate abstractions. An increasingly common abstraction is the neural ‘factor’. Factors are central to many explanations in systems neuroscience. Factors provide a framework for describing computational mechanism, and offer a bridge between data and concrete models. Yet there remains some discomfort with this abstraction, and with any attempt to provide mechanistic explanations above that of spikes, neurons, cell-types, and other comfortingly concrete entities. I will explain why, for many networks of spiking neurons, factors are not only a well-defined abstraction, but are critical to understanding computation mechanistically. Indeed, factors are as real as other abstractions we now accept: pressure, temperature, conductance, and even the action potential itself. I use recent empirical results to illustrate how factor-based hypotheses have become essential to the forming and testing of scientific hypotheses. I will also show how embracing factor-level descriptions affords remarkable power when decoding neural activity for neural engineering purposes.

Theories of consciousness: beyond the first/higher-order distinction

Theories of consciousness are commonly grouped into "first-order" and "higher-order" families. As conventional wisdom has it, many more animals are likely to be conscious if a first-order theory is correct. But two recent developments have put pressure on the first/higher-order distinction. One is the argument (from Shea and Frith) that an effective global workspace mechanism must involve a form of metacognition. The second is Lau's "perceptual reality monitoring" (PRM) theory, a member of the "higher-order" family in which conscious sensory content is not re-represented, only tagged with a temporal index and marked as reliable. I argue that the first/higher-order distinction has become so blurred that it is no longer particularly useful. Moreover, the conventional wisdom about animals should not be trusted. It could be, for example, that the distribution of PRM in the animal kingdom is wider than the distribution of global broadcasting.

The Equation of State of a Tissue

An equation of state is something you hear about in introductory thermodynamics, for example, the Ideal gas equation. The ideal gas equation relates the pressure, volume, and the number of particles of the gas, to its temperature, uniquely defining its state. This description is possible in physics when the system under investigation is in equilibrium or near equilibrium. In biology, a tissue is modeled as a fluid composed of cells. These cells are constantly interacting with each other through mechanical and chemical signaling, driving them far from equilibrium. Can an equation of state exist for such a messy interacting system? In this talk, I show that the presence of strong cell-cell interaction in tissues gives rise to a novel non-equilibrium, size-dependent surface tension, something unheard of for classical fluids. This surface tension, in turn, modifies the packing of cells inside the tissue generating a size-dependent density and pressure. Finally, we show that a combination of these non-equilibrium pressure and densities can yield an equation of state for biological tissues arbitrarily far from equilibrium. In the end, I discuss how this new paradigm of size-dependent biological properties gives rise to novel modes of cellular motion in tissues

What does time of day mean for vision?

Profound changes in the visual environment occur over the course of the day-night cycle. There is therefore a profound pressure for cells and circuits within the visual system to adjust their function over time, to match the prevailing visual environment. Here, I will discuss electrophysiological data collected from nocturnal and diurnal rodents that reveal how the visual code is ‘temporally optimised’ by 1) the retina’s circadian clock, and 2) a change in behavioural temporal niche.

Mutation targeted gene therapy approaches to alter rod degeneration and retain cones

My research uses electrophysiological techniques to evaluate normal retinal function, dysfunction caused by blinding retinal diseases and the restoration of function using a variety of therapeutic strategies. We can use our understanding or normal retinal function and disease-related changes to construct optimal therapeutic strategies and evaluate how they ameliorate the effects of disease. Retinitis pigmentosa (RP) is a family of blinding eye diseases caused by photoreceptor degeneration. The absence of the cells that for this primary signal leads to blindness. My interest in RP involves the evaluation of therapies to restore vision: replacing degenerated photoreceptors either with: (1) new stem or other embryonic cells, manipulated to become photoreceptors or (2) prosthetics devices that replace the photoreceptor signal with an electronic signal to light. Glaucoma is caused by increased intraocular pressure and leads to ganglion cell death, which eliminates the link between the retinal output and central visual processing. We are parsing out of the effects of increased intraocular pressure and aging on ganglion cells. Congenital Stationary Night Blindness (CSNB) is a family of diseases in which signaling is eliminated between rod photoreceptors and their postsynaptic targets, rod bipolar cells. This deafferents the retinal circuit that is responsible for vision under dim lighting. My interest in CSNB involves understanding the basic interplay between excitation and inhibition in the retinal circuit and its normal development. Because of the targeted nature of this disease, we are hopeful that a gene therapy approach can be developed to restore night vision. My work utilizes rodent disease models whose mutations mimic those found in human patients. While molecular manipulation of rodents is a fairly common approach, we have recently developed a mutant NIH miniature swine model of a common form of autosomal dominant RP (Pro23His rhodopsin mutation) in collaboration with the National Swine Resource Research Center at University of Missouri. More genetically modified mini-swine models are in the pipeline to examine other retinal diseases.

Playing StarCraft and saving the world using multi-agent reinforcement learning!

This is my C-14 Impaler gauss rifle! There are many like it, but this one is mine!" - A terran marine If you have never heard of a terran marine before, then you have probably missed out on playing the very engaging and entertaining strategy computer game, StarCraft. However, don’t despair, because what we have in store might be even more exciting! In this interactive session, we will take you through, step-by-step, on how to train a team of terran marines to defeat a team of marines controlled by the built-in game AI in StarCraft II. How will we achieve this? Using multi-agent reinforcement learning (MARL). MARL is a useful framework for building distributed intelligent systems. In MARL, multiple agents are trained to act as individual decision-makers of some larger system, while learning to work as a team. We will show you how to use Mava (https://github.com/instadeepai/Mava), a newly released research framework for MARL to build a multi-agent learning system for StarCraft II. We will provide the necessary guidance, tools and background to understand the key concepts behind MARL, how to use Mava building blocks to build systems and how to train a system from scratch. We will conclude the session by briefly sharing various exciting real-world application areas for MARL at InstaDeep, such as large-scale autonomous train navigation and circuit board routing. These are problems that become exponentially more difficult to solve as they scale. Finally, we will argue that many of humanity’s most important practical problems are reminiscent of the ones just described. These include, for example, the need for sustainable management of distributed resources under the pressures of climate change, or efficient inventory control and supply routing in critical distribution networks, or robotic teams for rescue missions and exploration. We believe MARL has enormous potential to be applied in these areas and we hope to inspire you to get excited and interested in MARL and perhaps one day contribute to the field!

Sensory-motor control, cognition and brain evolution: exploring the links

Drawing on recent findings from evolutionary anthropology and neuroscience, professor Barton will lead us through the amazing story of the evolution of human cognition. Usingstatistical, phylogenetic analyses that tease apart the variation associated with different neural systems and due to different selection pressures, he will be addressing intriguing questions like ‘Why are there so many neurons in the cerebellum?’, ‘Is the neocortex the ‘intelligent’ bit of the brain?’, and ‘What explains that the recognition by humans of emotional expressions is disrupted by trancranial magnetic stimulation of the somatosensory cortex?’ Could, as professor Barton suggests, the cerebellum -modestly concealed beneath the volumetrically dominating neocortex and largely ignored- turn out to be the Cinderella of the study of brain evolution?

Vision for escape and pursuit

We want to understand how the visual system detects and tracks salient stimuli in the environment to initiate and guide specific behaviors (i.e., visual neuroethology). Predator avoidance and prey capture are central selection pressures of animal evolution. Mice use vision to detect aerial predators and hunt insects. I will discuss studies from my group that identify specific circuits and pathways in the early visual system (i.e., the retina and its subcortical targets) mediating predator avoidance and prey capture in mice. Our results highlight the importance of subcellular visual processing in the retina and the alignment of viewing strategies with region- and cell-type-specific retinal ganglion cell projection patterns to the brain.

Under Pressure: the role of PIEZO ion channels in interoception

PIEZO ion channels detect force in cellular membranes. They are expressed in a wide variety of mammalian tissues, including the vasculature, lymphatic system, and the nervous system. We have found that PIEZO2 in sensory neurons is required for the mechanical senses of touch and proprioception, but our understanding of internal organ sensing, interoception, is far behind. I will describe our findings on the role of PIEZO ion channels in the lesser-known interoceptive senses in multiple organ systems.

Multistable structures - from deployable structures to robots

Multistable structures can reversibly change between multiple stable configurations when a sufficient energetic input is provided. While originally the field focused on understanding what governs the snapping, more recently it has been shown that these systems also provide a powerful platform to design a wide range of smart structures. In this talk, I will first show that pressure-deployable origami structures characterized by two stable configurations provide opportunities for a new generation of large-scale inflatable structures that lock in place after deployment and provide a robust enclosure through their rigid faces. Then, I will demonstrate that the propagation of transition waves in a bistable one-dimensional linkage can be exploited as a robust mechanism to realize structures that can be quickly deployed. Finally, while in the first two examples multistability is harnessed to realize deployable architectures, I will demonstrate that bistable building blocks can also be exploited to design crawling and jumping robots. Unlike previously proposed robots that require complex input control of multiple actuators, a simple, slow input signal suffices to make our system move, as all features required for locomotion are embedded into the architecture of the building blocks.

Neurocircuits in control of integrative physiology

This open colloquia session is part of the special workshop entitled "Obesity at the Interface of Neuroscience and Physiology II". Abstract: Proopiomelanocortin (POMC)- and agouti related peptide (AgRP)-expressing neurons in the arcuate nucleus of the hypothalamus (ARH) are critical regulators of food intake and energy homeostasis. They rapidly integrate the energy state of the organism through sensing fuel availability via hormones, nutrient components and even rapidly upon sensory food perception. Importantly, they not only regulate feeding responses, but numerous autonomic responses including glucose and lipid metabolism, inflammation and blood pressure. More recently, we could demonstrate that sensory food cue-dependent regulation of POMC neurons primes the hepatic endoplasmic reticulum (ER) stress response to prime liver metabolism for the postpramndial state. The presentation will focus on the regulation of these neurons in control of integrative physiology, the identification of distinct neuronal circuitries targeted by these cells and finally on the broad range implications resulting from dysregulation of these circuits as a consequence of altered maternal metabolism.

Building a synthetic cell: Understanding the clock design and function

Clock networks containing the same central architectures may vary drastically in their potential to oscillate, raising the question of what controls robustness, one of the essential functions of an oscillator. We computationally generate an atlas of oscillators and found that, while core topologies are critical for oscillations, local structures substantially modulate the degree of robustness. Strikingly, two local structures, incoherent and coherent inputs, can modify a core topology to promote and attenuate its robustness, additively. The findings underscore the importance of local modifications to the performance of the whole network. It may explain why auxiliary structures not required for oscillations are evolutionary conserved. We also extend this computational framework to search hidden network motifs for other clock functions, such as tunability that relates to the capabilities of a clock to adjust timing to external cues. Experimentally, we developed an artificial cell system in water-in-oil microemulsions, within which we reconstitute mitotic cell cycles that can perform self-sustained oscillations for 30 to 40 cycles over multiple days. The oscillation profiles, such as period, amplitude, and shape, can be quantitatively varied with the concentrations of clock regulators, energy levels, droplet sizes, and circuit design. Such innate flexibility makes it crucial to studying clock functions of tunability and stochasticity at the single-cell level. Combined with a pressure-driven multi-channel tuning setup and long-term time-lapse fluorescence microscopy, this system enables a high-throughput exploration in multi-dimension continuous parameter space and single-cell analysis of the clock dynamics and functions. We integrate this experimental platform with mathematical modeling to elucidate the topology-function relation of biological clocks. With FRET and optogenetics, we also investigate spatiotemporal cell-cycle dynamics in both homogeneous and heterogeneous microenvironments by reconstructing subcellular compartments.

Abstraction and Analogy in Natural and Artificial Intelligence

Learning by analogy is a powerful tool children’s developmental repertoire, as well as in educational contexts such as mathematics, where the key knowledge base involves building flexible schemas. However, noticing and learning from analogies develops over time and is cognitively resource intensive. I review studies that provide insight into the relationship between mechanisms driving children’s developing analogy skills, highlighting environmental inputs (parent talk and prior experiences priming attention to relations) and neuro-cognitive factors (Executive Functions and brain injury). I then note implications for mathematics learning, reviewing experimental findings that show analogy can improve learning, but also that both individual differences in EFs and environmental factors that reduce available EFs such as performance pressure can predict student learning.

A mechanism for nuclear size scaling based upon osmotic pressure

Information and Decision-Making

In recent years it has become increasingly clear that (Shannon) information is a central resource for organisms, akin in importance to energy. Any decision that an organism or a subsystem of an organism takes involves the acquisition, selection, and processing of information and ultimately its concentration and enaction. It is the consequences of this balance that will occupy us in this talk. This perception-action loop picture of an agent's life cycle is well established and expounded especially in the context of Fuster's sensorimotor hierarchies. Nevertheless, the information-theoretic perspective drastically expands the potential and predictive power of the perception-action loop perspective. On the one hand information can be treated - to a significant extent - as a resource that is being sought and utilized by an organism. On the other hand, unlike energy, information is not additive. The intrinsic structure and dynamics of information can be exceedingly complex and subtle; in the last two decades one has discovered that Shannon information possesses a rich and nontrivial intrinsic structure that must be taken into account when informational contributions, information flow or causal interactions of processes are investigated, whether in the brain or in other complex processes. In addition, strong parallels between information and control theory have emerged. This parallelism between the theories allows one to obtain unexpected insights into the nature and properties of the perception-action loop. Through the lens of information theory, one can not only come up with novel hypotheses about necessary conditions for the organization of information processing in a brain, but also with constructive conjectures and predictions about what behaviours, brain structure and dynamics and even evolutionary pressures one can expect to operate on biological organisms, induced purely by informational considerations.

Vagal sensory neurons that guard the airways

The vagus nerve contains a diversity of sensory neurons that detect peripheral stimuli such as blood pressure changes at the aortic arch, lung expansion during breathing, meal-induced stomach distension, and chemotherapeutics that induce nausea. Underlying vagal sensory mechanisms are largely unresolved at a molecular level, presenting tremendously important problems in sensory biology. We charted vagal sensory neurons by single cell RNA sequencing, identifying novel cell surface receptors and classifying a staggering diversity of sensory neuron types. We then generated a collection of ires-Cre knock-in mice to target each neuron type, and adapted genetic tools for Cre-based anatomical mapping, in vivo imaging, targeted ablation, and optogenetic control of vagal neuron activity. We found different sensory neuron types that innervate the lung and exert powerful effects on breathing, others that monitor and control the digestive system, and yet others that innervate that innervate the larynx and protect the airways. Together with Ardem Patapoutian, we also identified a critical role for Piezo mechanoreceptors in the sensation of airway stretch, which underlies a classical respiratory reflex termed the Hering-Breuer inspiratory reflex, as well as in the neuronal sensation of blood pressure and the baroreceptor reflex.