The new lab at UCSD, directed by Uri, is opening positions for machine learning/computer vision scientists. The lab is part of a new “Technology Sandbox” at UCSD, which includes a ThermoFisher cryoEM and mass spec center, a Nikon Imaging Center, and computational resources.

SPKI is expanding, and is in search of a highly motivated data scientist / ML engineer who wants to contribute to the development and implementation of new artificial intelligence (AI) tools for health. The work will be done in a highly interdisciplinary environment, and you will collaborate with a team consisting of clinicians, scientists from the university and technologists, legal experts, industry partners, as well as personnel responsible for ICT, data security, privacy concerns and more. This environment also includes researchers at The Machine Learning Group and Visual Intelligence.

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.

Deep learning provides new data-driven tools to relate neural activity to perception and cognition, aiding scientists in developing theories of neural computation that increasingly resemble biological systems both at the level of behavior and of neural activity. But what in a deep neural network should correspond to what in a biological system? This question is addressed implicitly in the use of comparison measures that relate specific neural or behavioral dimensions via a particular functional form. However, distinct comparison methodologies can give conflicting results in recovering even a known ground-truth model in an idealized setting, leaving open the question of what to conclude from the outcome of a systems comparison using any given methodology. Here, we develop a framework to make explicit and quantitative the effect of both hypothesis-driven aspects—such as details of the architecture of a deep neural network—as well as methodological choices in a systems comparison setting. We demonstrate via the learning dynamics of deep neural networks that, while the role of the comparison methodology is often de-emphasized relative to hypothesis-driven aspects, this choice can impact and even invert the conclusions to be drawn from a comparison between neural systems. We provide evidence that the right way to adjudicate a comparison depends on the use case—the scientific hypothesis under investigation—which could range from identifying single-neuron or circuit-level correspondences to capturing generalizability to new stimulus properties

Guidelines concerning the potentially harmful effects of scientific studies have historically focused on minimizing risk for participants. However, studies can also indirectly inflict harm on individuals and social groups through how they are designed, reported, and disseminated. As evidenced by recent criticisms and retractions of high-profile studies dealing with a wide variety of social issues, there is a scarcity of resources and guidance on how one can conduct research in a socially responsible manner. As such, even motivated researchers might publish work that has negative social impacts due to a lack of awareness. To address this, we proposed 10 recommendations (“simple rules”) for researchers who wish to conduct more socially responsible science. These recommendations cover major considerations throughout the life cycle of a study from inception to dissemination. They are not aimed to be a prescriptive list or a deterministic code of conduct. Rather, they are meant to help motivated scientists to reflect on their social responsibility as researchers and actively engage with the potential social impact of their research.

With this webinar series, the ALBA Disability & Accessibility Working Group aims to bring down the ivory tower of ableism among the brain research community, one extraordinary neuroscientist at a time. These webinars give a platform to scientists with disabilities across the globe and neuroscience disciplines, while reflecting on how to promote inclusive working environments and accessibility to research. For this 4th episode, Dr. Maria José Diógenes (iMM - ULisboa, PT) will talk about how her personal story changed her professional life: from the pharmacy to the laboratory bench and from ageing to Rett Syndrome.

With this webinar series, the ALBA Disability & Accessibility Working Group aims to bring down the ivory tower of ableism among the brain research community, one extraordinary neuroscientist at a time. These webinars give a platform to scientists with disabilities across the globe and neuroscience disciplines, while reflecting on how to promote inclusive working environments and accessibility to research. For this 3rd episode, Dr. Donna Rose Addis (Rotman Research Institute, Baycrest & University of Toronto, Canada) will talk about her research and experience.

The use of molecular imaging, particularly PET and SPECT, has significantly transformed the treatment of schizophrenia with antipsychotic drugs since the late 1980s. It has offered insights into the links between drug target engagement, clinical effects, and side effects. A therapeutic window for receptor occupancy is established for antipsychotics, yet there is a divergence of opinions regarding the importance of blood levels, with many downplaying their significance. As a result, the role of therapeutic drug monitoring (TDM) as a personalized therapy tool is often underrated. Since molecular imaging of antipsychotics has focused almost entirely on D2-like dopamine receptors and their potential to control positive symptoms, negative symptoms and cognitive deficits are hardly or not at all investigated. Alternative methods have been introduced, i.e. to investigate the correlation between approximated receptor occupancies from blood levels and cognitive measures. Within the domain of antidepressants, and specifically regarding ketamine's efficacy in depression treatment, there is limited comprehension of the association between plasma concentrations and target engagement. The measurement of AMPA receptors in the human brain has added a new level of comprehension regarding ketamine's antidepressant effects. To ensure precise prescription of psychotropic drugs, it is vital to have a nuanced understanding of how molecular and clinical effects interact. Clinician scientists are assigned with the task of integrating these indispensable pharmacological insights into practice, thereby ensuring a rational and effective approach to the treatment of mental health disorders, signaling a new era of personalized drug therapy mechanisms that promote neuronal plasticity not only under pathological conditions, but also in the healthy aging brain.

This is the first session in a series of three online fireside chats organised by the ALBA Network with the aims of understanding what’s not working in existing mentoring programmes in (neuro)science and identify the challenges and unmet mentorship needs of the next generation of scientists across the globe. The feedback from these sessions will be used to develop a tailored ALBA mentoring programme.

In this webinar, Dr David Pagliaccio discusses the findings of his recent pre-print on workplace bias and discrimination faced by LGBTQIA+ brain scientists in the US.

With this webinar series, the ALBA Disability & Accessibility Working Group aims to bring down the ivory tower of ableism among the brain research community, one extraordinary neuroscientist at a time. These webinars give a platform to scientists with disabilities across the globe and neuroscience disciplines, while reflecting on how to promote inclusive working environments and accessibility to research. For this 2nd episode, Prof. Philip Haydon (Tufts University School of Medicine, Boston, USA) will talk about his research and experience.  Prof. Philip runs an active laboratory researching a multitude of neurological disorders (including epilepsy). He is also President of Sail For Epilepsy. His mission is to inspire people with epilepsy, raise funds to support research for a cure, promote awareness of epilepsy and educate the public.

Following up on last year's webinar - What it takes to succeed as a neuroscientist in Africa, this panel discussion aims at creating a guide to the skill set needed to be a neuroscientist in the African continent. Chairs and panelists will illustrate different areas of expertise as part of the "Toolkit" by matching them to real life experience and solutions that they had to find while building their career as scientists.

Life Perceives is a symposium bringing together scientists and artists for an open exploration of how “perception” can be understood as a phenomenon that does not only belong to humans, or even the so-called “higher organisms”, but exists across the entire spectrum of life in a myriad of forms. The symposium invites leading practitioners from the arts and sciences to present unique insights through short talks, open discussions, and artistic interventions that bring us slightly closer to the life worlds of plants and fungi, microbial communities and immune systems, cuttlefish and crows. What do we mean when we talk about perception in other species? Do other organisms have an experience of the world? Or does our human-centred perspective make understanding other forms of life on their own terms an impossible dream? Whatever your answers to these questions may be, we hope to unsettle them, and leave you more curious than when you arrived.

Theory of mind, which consists of reasoning about the knowledge, belief, desire, and similar mental states of others, is a key component of social reasoning and social interaction. While it has been studied by cognitive scientists for decades, none of the prevailing theories of the processes that underlie theory of mind reasoning and development explain the breadth of experimental findings. I propose that this is because theory of mind is, like much of human reasoning, inherently analogical. In this talk, I will discuss several theory of mind findings from the psychology literature, the challenges they pose for our understanding of theory of mind, and bring in evidence from the Analogical Theory of Mind (AToM) cognitive model that demonstrates how these findings fit into an analogical understanding of theory of mind reasoning.

Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned. Cognitive scientists have long hypothesized the possibility of a compositional neural code, where complex neural computations are made up of constituent components; however, the neural substrate underlying this structure remains elusive in biological and artificial neural networks. Here we identified an algorithmic neural substrate for compositional computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses of networks revealed learned computational strategies that mirrored the modular subtask structure of the task-set used for training. Dynamical motifs such as attractors, decision boundaries and rotations were reused across different task computations. For example, tasks that required memory of a continuous circular variable repurposed the same ring attractor. We show that dynamical motifs are implemented by clusters of units and are reused across different contexts, allowing for flexibility and generalization of previously learned computation. Lesioning these clusters resulted in modular effects on network performance: a lesion that destroyed one dynamical motif only minimally perturbed the structure of other dynamical motifs. Finally, modular dynamical motifs could be reconfigured for fast transfer learning. After slow initial learning of dynamical motifs, a subsequent faster stage of learning reconfigured motifs to perform novel tasks. This work contributes to a more fundamental understanding of compositional computation underlying flexible general intelligence in neural systems. We present a conceptual framework that establishes dynamical motifs as a fundamental unit of computation, intermediate between the neuron and the network. As more whole brain imaging studies record neural activity from multiple specialized systems simultaneously, the framework of dynamical motifs will guide questions about specialization and generalization across brain regions.

Monash Biomedical Imaging is part of the new $71.2 million Australian Precision Medicine Enterprise (APME) facility, which will deliver large-scale development and manufacturing of precision medicines and theranostic radiopharmaceuticals for industry and research. A key feature of the APME project is a high-energy cyclotron with multiple production clean rooms, which will be located on the Monash Biomedical Imaging (MBI) site in Clayton. This strategic co-location will facilitate radiochemistry, PET and SPECT research and clinical use of theranostic (therapeutic and diagnostic) radioisotopes produced on-site. In this webinar, MBI’s Professor Gary Egan and Dr Maggie Aulsebrook will explain how the APME will secure Australia’s supply of critical radiopharmaceuticals, build a globally competitive Australian manufacturing hub, and train scientists and engineers for the Australian workforce. They will cover the APME’s state-of-the-art 30 MeV and 18-24 MeV cyclotrons and radiochemistry facilities, as well as the services that will be accessible to students, scientists, clinical researchers, and pharmaceutical companies in Australia and around the world. The APME is a collaboration between Monash University, Global Medical Solutions Australia, and Telix Pharmaceuticals. Professor Gary Egan is Director of Monash Biomedical Imaging, Director of the ARC Centre of Excellence for Integrative Brain Function and a Distinguished Professor at the Turner Institute for Brain and Mental Health, Monash University. He is also lead investigator of the Victorian Biomedical Imaging Capability, and Deputy Director of the Australian National Imaging Facility. Dr Maggie Aulsebrook obtained her PhD in Chemistry at Monash University and specialises in the development and clinical translation of radiopharmaceuticals. She has led the development of several investigational radiopharmaceuticals for first-in-human application. Maggie leads the Radiochemistry Platform at Monash Biomedical Imaging.

Children need inference in order to learn the meanings of words. They must infer the referent from the situation in which a target word is said. Furthermore, to be able to use the word in other situations, they also need to infer what other referents the word can be generalized to. As verbs refer to relations between arguments, verb learning requires relational analogical inference, something which is challenging to young children. To overcome this difficulty, young children recruit a diverse range of cues in their inference of verb meanings, including, but not limited to, syntactic cues and social and pragmatic cues as well as statistical cues. They also utilize perceptual similarity (object similarity) in progressive alignment to extract relational verb meanings and further to gain insights about relational verb meanings. However, just having a list of these cues is not useful: the cues must be selected, combined, and coordinated to produce the optimal interpretation in a particular context. This process involves abductive reasoning, similar to what scientists do to form hypotheses from a range of facts or evidence. In this talk, I discuss how children use a chain of inferences to learn meanings of verbs. I consider not only the process of analogical mapping and progressive alignment, but also how children use abductive inference to find the source of analogy and gain insights into the general principles underlying verb learning. I also present recent findings from my laboratory that show that prelinguistic human infants use a rudimentary form of abductive reasoning, which enables the first step of word learning.

The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.

The event is open to everyone interested in Neuroscience, Cell Biology, Drug Discovery, Disease Modeling, and Bio/Neuroengineering! This meeting is a platform bringing scientists from all over the world together and fostering scientific exchange and collaboration.

The SynAGE committee members are thrilled to host ISYNC, the International SynAGE conference on healthy ageing, on 28-30 March 2022 in Magdeburg, Germany. This conference has been entirely organised from young scientists of the SynAGE research training group RTG 2413 (www.synage.de) and represents a unique occasion for researchers from all over the world to bring together and join great talks and sessions with us and our guests. A constantly updated list of our speakers can be found on the conference webpage: www.isync-md.de. During the conference, attendees will have access to a range of symposia which will deal with Glia, Biomarkers and Immunoresponses during ageing to neurodegeneration brain integrity and cognitive function in health and diseases. Moreover, the conference will offer social events especially for young researchers and the possibility to network together in a beautiful and suggestive location where our conference will take place: the Johanniskirche. The event will be happening in person, but due to the current pandemic situation and restrictions we are planning the conference as a hybrid event with lots of technical support to ensure that every participant can follow the talks and take part in the scientific discussions. The registration to our ISYNC conference is free of charge. However, the number of people attending the conference in person is restricted to 100. Afterwards, registrations will be accepted for joining virtually only. The registration is open until 15.02.2022. Especially for PhD and MD Students: Check our available Travel Grants, Poster Prize and SynAGE Award Dinner: https://www.isync-md.de/index.php/phd-md-specials/ If you need any further information don’t hesitate to contact us via email: contact@synage.de. We are looking forward to meet you in 2022 in Magdeburg to discuss about our research and ideas and bless together science. Your ISYNC organization Committee

This talk looks at the subject of my biography, the German physiologist Emil du Bois-Reymond (1818–1896). With respect to his philosophy of biological reduction, his methods of electrophysiological experiment, and his co-discovery of the action potential, du Bois-Reymond is generally considered one of the founders of neuroscience. Less well known are the origins of his innovation: French writers shaped his outlook on science, just as French scientists shaped his practice in the laboratory. I contend that du Bois-Reymond’s originality is the product of his synthesis of French traditions with German concerns.

Join the Brain Conference on 24-25 February 2022 for the opportunity to hear from neurology’s leading scientists and clinicians. The two-day virtual programme features clinical teaching talks and research presentations from expert speakers including neuroscientist Professor Gina Poe, and the winner of the 2021 Brain Prize, neurologist Professor Peter Goadsby." "Tickets for The Brain Conference 2022 cost just £30, but register with promotional code BRAINCONEM20 for a discounted rate of £25.

Join the Brain Conference on 24-25 February 2022 for the opportunity to hear from neurology’s leading scientists and clinicians. The two-day virtual programme features clinical teaching talks and research presentations from expert speakers including neuroscientist Professor Gina Poe, and the winner of the 2021 Brain Prize, neurologist Professor Peter Goadsby." "Tickets for The Brain Conference 2022 cost just £30, but register with promotional code BRAINCONEM20 for a discounted rate of £25.

We understand the brain in representational terms. E.g., we understand spatial navigation by appealing to the spatial properties that hippocampal cells represent, and the operations hippocampal circuits perform on those representations (Moser et al., 2008). Philosophers have been concerned with the nature of representation, and recently neuroscientists entered the debate, focusing specifically on neural representations. (Baker & Lansdell, n.d.; Egan, 2019; Piccinini & Shagrir, 2014; Poldrack, 2020; Shagrir, 2001). We want to know what representations are, how to discover them in the brain, and why they matter so much for our understanding of the brain. Those questions are framed in a traditional philosophical way: we start with explanations that use representational notions, and to more deeply understand those explanations we ask, what are representations — what is the definition of representation? What is it for some bit of neural activity to be a representation? I argue that there is an alternative, and much more fruitful, approach. Rather than asking what representations are, we should ask what the use of representational *notions* allows us to do in neuroscience — what thinking in representational terms helps scientists do or explain. I argue that this framing offers more fruitful ground for interdisciplinary collaboration by distinguishing the philosophical concerns that have a place in neuroscience from those that don’t (namely the definitional or metaphysical questions about representation). And I argue for a particular view of representational notions: they allow us to impose the structure of one domain onto another as a model of its causal structue. So, e.g., thinking about the hippocampus as representing spatial properties is a way of taking structures in those spatial properties, and projecting those structures (and algorithms that would implement them) them onto the brain as models of its causal structure.

The aim of this half day virtual meeting is to consider what is currently achievable with existing techniques and to explore where advancements can be made in the short and medium term. Leading scientists in the field will highlight the questions currently being addressed using hard X-ray imaging techniques, volume electron microscopy and their combination with other imaging modalities, with a forward look to areas of opportunity becoming accessible as a result of the recent and upcoming synchrotron upgrades. We expect an exciting day filled with science focused talks and lively discussions on how the field will develop over the next few years.

Autopilot is a Python framework for performing complex behavioral neuroscience experiments by coordinating a swarm of Raspberry Pis. It was designed to not only give researchers a tool that allows them to perform the hardware-intensive experiments necessary for the next generation of naturalistic neuroscientific observation, but also to make it easier for scientists to be good stewards of the human knowledge project. Specifically, we designed Autopilot as a framework that lets its users contribute their technical expertise to a cumulative library of hardware interfaces and experimental designs, and produce data that is clean at the time of acquisition to lower barriers to open scientific practices. As autopilot matures, we have been progressively making these aspirations a reality. Currently we are preparing the release of Autopilot v0.4.0, which will include a new plugin system and wiki that makes use of semantic web technology to make a technical and contextual knowledge repository. By combining human readable text and semantic annotations in a wiki that makes contribution as easy as possible, we intend to make a communal knowledge system that gives a mechanism for sharing the contextual technical knowledge that is always excluded from methods sections, but is nonetheless necessary to perform cutting-edge experiments. By integrating it with Autopilot, we hope to make a first of its kind system that allows researchers to fluidly blend technical knowledge and open source hardware designs with the software necessary to use them. Reciprocally, we also hope that this system will support a kind of deep provenance that makes abstract "custom apparatus" statements in methods sections obsolete, allowing the scientific community to losslessly and effortlessly trace a dataset back to the code and hardware designs needed to replicate it. I will describe the basic architecture of Autopilot, recent work on its community contribution ecosystem, and the vision for the future of its development.

The ALBA Network is organizing a webinar on LGBTQIA+ inclusion and visibility. This special event will feature a panel of established scientists in brain research who identify as LGBTQIA+. Speaker will discuss their goals, challenges and successes while navigating academia as part of the LGBTQIA+ community. Registration is free but mandatory.

The Addgene AAV Data Hub was launched to help scientists share data and protocols obtained from AAV experiments. Our longterm goal is to provide scientists with a resource to help guide AAV selection and use by providing data from individual labs on AAV performance.

Open-source tools are gaining an increasing foothold in neuroscience. The rising complexity of experiments in systems neuroscience has led to a need for multiple parts of experiments to work together seamlessly. This means that open-source tools that freely interact with each other and can be understood and modified more easily allow scientists to conduct better experiments with less effort than closed tools. Open Ephys is an organization with team members distributed all around the world. Our mission is to advance our understanding of the brain by promoting community ownership of the tools we use to study it. We are making and distributing cutting edge tools that exploit modern technology to bring down the price and complexity of neuroscience experiments. A large component of this is to take tools that were developed in academic labs and helping with documentation, support, and distribution. More recently, we have been working on bringing high-quality manufacturing, distribution, warranty, and support to open source tools by partnering with OEPS in Portugal. We are now also establishing standards that make it possible to combine methods, such as miniaturized microscopes, electrode drive implants, and silicon probes seamlessly in one system. In the longer term, our development of new tools, interfaces and our standardization efforts have the goal of making it possible for scientists to easily run complex experiments that span from complex behaviors and tasks, multiple recording modalities, to easy access to data processing pipelines.

Though the term has many definitions, computational neuroscience is mainly about applying mathematics to the study of the brain. The brain—a jumble of all different kinds of neurons interconnected in countless ways that somehow produce consciousness—has been described as “the most complex object in the known universe”. Physicists for centuries have turned to mathematics to properly explain some of the most seemingly simple processes in the universe—how objects fall, how water flows, how the planets move. Equations have proved crucial in these endeavors because they capture relationships and make precise predictions possible. How could we expect to understand the most complex object in the universe without turning to mathematics? — The answer is we can’t, and that is why I wrote this book. While I’ve been studying and working in the field for over a decade, most people I encounter have no idea what “computational neuroscience” is or that it even exists. Yet a desire to understand how the brain works is a common and very human interest. I wrote this book to let people in on the ways in which the brain will ultimately be understood: through mathematical and computational theories. — At the same time, I know that both mathematics and brain science are on their own intimidating topics to the average reader and may seem downright prohibitory when put together. That is why I’ve avoided (many) equations in the book and focused instead on the driving reasons why scientists have turned to mathematical modeling, what these models have taught us about the brain, and how some surprising interactions between biologists, physicists, mathematicians, and engineers over centuries have laid the groundwork for the future of neuroscience. — Each chapter of Models of the Mind covers a separate topic in neuroscience, starting from individual neurons themselves and building up to the different populations of neurons and brain regions that support memory, vision, movement and more. These chapters document the history of how mathematics has woven its way into biology and the exciting advances this collaboration has in store.

Scientists work in laboratories; comfortable spaces which we equip and configure to be ideal for our needs. The scientific paradigm has been adopted by clinicians, who run diagnostic tests and treatments in fully equipped hospital facilities. Yet advances in technology mean that that increasingly many functions of a laboratory can be compressed into miniature devices, or even into a smartphone app. This has the potential to be transformative for healthcare in developing nations, allowing complex tests and interventions to be made available in every village. In this talk, I will give two examples of this approach from my recent work. In the field of stroke rehabilitation, I will present basic research which we have conducted in animals over the last decade. This reveals new ways to intervene and strengthen surviving pathways, which can be deployed in cheap electronic devices to enhance functional recovery. In degenerative disease, we have used Bayesian statistical methods to improve an algorithm to measure how rapidly a subject can stop an action. We then implemented this on a portable device and on a smartphone app. The measurement obtained can act as a useful screen for Parkinson’s Disease. I conclude with an outlook for the future of this approach, and an invitation to those who would be interesting in collaborating in rolling it out to in African settings.

The Brainstorms Festival is the No1 online neuroscience and AI event for scientists, businesses, investors and startups. Join and listen to talks from leading scientists, take part in interactive discussions, and network with the people driving neurotech and AI innovation globally. The festival provides a digital playground for visionaries with dozens of medical innovations, panel discussions, workshops, a hackathon, and a neuroethics panel discussion which is crucial topic for neurodiversity and disability rights. Register now and be part of our amazing crowd!

It has long been appreciated (and celebrated) that certain species have sensory capabilities that humans do not share, for example polarization, ultraviolet, and infrared vision. What is less appreciated however, is that our position as terrestrial human scientists can significantly affect our study of animal senses and signals, even within modalities that we do share. For example, our acute vision can lead us to over-interpret the relevance of fine patterns in animals with coarser vision, and our Cartesian heritage as scientists can lead us to divide sensory modalities into orthogonal parameters (e.g. hue and brightness for color vision), even though this division may not exist within the animal itself. This talk examines two cases from marine visual ecology where a reconsideration of our biases as sharp-eyed Cartesian land mammals can help address questions in visual ecology. The first case examines the enormous variation in visual acuity among animals with image-forming eyes, and focuses on how acknowledging the typically poorer resolving power of animals can help us interpret the function of color patterns in cleaner shrimp and their client fish. The second case examines the how the typical human division of polarized light stimuli into angle and degree of polarization is problematic, and how a physiologically relevant interpretation is both closer to the truth and resolves a number of issues, particularly when considering the propagation of polarized light