Computational models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, exchange, and re-use of models. Successful projects have created standards for describing complex models in neuroscience and provide open source tools to address these issues. This lecture provides an overview of these projects and make a case for expanded use of resources in support of reproducibility and validation of models against experimental data.
NWB: An ecosystem for neurophysiology data standardization
A brief overview of the Python programming language, with an emphasis on tools relevant to data scientists. This lecture was part of the 2018 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.
This lecture focuses on how the immune system can target and attack the nervous system to produce autoimmune responses that may result in diseases such as multiple sclerosis, neuromyelitis and lupus cerebritis manifested by motor, sensory, and cognitive impairments. Despite the fact that the brain is an immune-privileged site, autoreactive lymphocytes producing proinflammatory cytokines can cause active brain inflammation, leading to myelin and axonal loss.
This lecture will highlight our current understanding and recent developments in the field of neurodegenerative disease research, as well as the future of diagnostics and treatment of neurodegenerative diseases
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
How genetics can contribute to our understanding of psychiatric phenotypes.
The lecture focuses on rationale for employing neuroimaging methods for movement disorders
An overview of some of the essential concepts in neuropharmacology (e.g. receptor binding, agonism, antagonism), an introduction to pharmacodynamics and pharmacokinetics, and an overview of the drug discovery process relative to diseases of the Central Nervous System.
Introduction to the principal of anatomical organization of neural systems in the human brain and spinal cord that mediate sensation, integrate signals, and motivate behavior.
This lecture focuses on the comprehension of nociception and pain sensation. It highlights how the somatosensory system and different molecular partners are involved in nociception and how nociception and pain sensation are studied in rodents and humans and the development of pain therapy.
Neuroethics has been described as containing at least two components - the neuroscience of ethics and the ethics of neuroscience. The first involves neuroscientific theories, research, and neuro-imaging focused on how the brain arrives at moral decisions and actions, which challenge existing descriptive theories of how humans develop moral thinking and make ethical decisions. The second, ethics of neuroscience, involves applying normative theories about what is right, good and fair to ethical questions raised by neuroscientific research and new technologies, such as how to balance the public benefit of “big data” neuroscience while protecting individual privacy and norms of informed consent.
The HBP as an ICT flagship project crucially relies on ICT and will contribute important input into the development of new computing principles and artefacts. Individuals working on the HBP should therefore be aware of the long history of ethical issues discussed in computing. The discourse on ethics and computing can be traced back to Norbert Wiener and the very beginning of digital computing. From the 1970s and 80s it has developed into an active discussion involving academics from various disciplines, professional bodies and industry.
Like any transformative technology, intelligent robotics has the potential for huge benefit, but is not without ethical or societal risk. In this lecture, I will explore two questions. Firstly, the increasingly urgent question of the ethical use of robots: are there particular applications of robots that should be proscribed, in eldercare, or surveillance, or war fighting for example? When intelligent autonomous robots make mistakes, as they inevitably will, who should be held to account? Secondly, I will consider the longer-term question of whether intelligent robots themselves could or should be ethical. Seventy years ago Isaac Asimov created his fictional Three Laws of Robotics. Is there now a realistic prospect that we could build a robot that is Three Laws Safe?
In the face of perceived public concerns about technological innovations, leading national and international bodies increasingly argue that there must be ‘dialogue' between policy makers, scientific researchers, civil society organizations and members of the public, to shape the pathways of technology development in a way that meets societal needs and gains public trust. This is not new, of course, and such concerns go back at least to the debates over the development of nuclear technologies and campaigns for social responsibility in science. Major funding bodies in the UK, Europe and elsewhere are now addressing this issue by insisting on Responsible Research and Innovation (RRI) in the development of emerging technology. Biotechnologies such as synthetic biology and neurotechnologies have become a particular focus of RRI, partly because of the belief that these are risky technologies involving tinkering with the very building blocks of life, and perhaps even with human nature. With my fellow researchers, I have been involved in trying to develop Responsible Research and Innovation in these technologies for several years.