This lecture covers modeling the neuron in silicon, modeling vision and audition and sensory fusion using a deep network.
Presentation of past and present neurocomputing approaches and hybrid analog/digital circuits that directly emulate the properties of neurons and synapses.
Presentation of the Brian neural simulator, where models are defined directly by their mathematical equations and code is automatically generated for each specific target.
The lecture covers a brief introduction to neuromorphic engineering, some of the neuromorphic networks that the speaker has developed, and their potential applications, particularly in machine learning.
This lecture provides an overview of depression (epidemiology and course of the disorder), clinical presentation, somatic co-morbidity, and treatment options.
Part 1 of 2 of a tutorial on statistical models for neural data
What is the difference between attention and consciousness? This lecture describes the scientific meaning of consciousness, journeys on the search for neural correlates of visual consciousness, and explores the possibility of consciousness in other beings and even non-biological structures.
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.
JupyterHub is a simple, highly extensible, multi-user system for managing per-user Jupyter Notebook servers, designed for research groups or classes. This lecture covers deploying JupyterHub on a single server, as well as deploying with Docker using GitHub for authentication.
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.