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 primer on optogenetics primer discusses how to manipulate neuronal populations with light at millisecond resolution and offers possible applications such as curing the blind and "playing the piano" with cortical neurons.
The "connectome" is a term, coined in the past decade, that has been used to describe more than one phenomenon in neuroscience. This lecture explains the basics of structural connections at the micro-, meso- and macroscopic scales.
Introduction to the types of glial cells, homeostasis (influence of cerebral blood flow and influence on neurons), insulation and protection of axons (myelin sheath; nodes of Ranvier), microglia and reactions of the CNS to injury.
Introduction to neurons, synaptic transmission, and ion channels.
2nd part of the lecture. Introduction to cell receptors and signalling cascades
Introduction to the origin and differentiation of myelinating cell types, molecular mechanisms defining onset and progression of myelination, demyelination and remyelination after injury.
This lecture covers: integrating information within a network, modulating and controlling networks, functions and dysfunctions of hippocampal networks, and the integrative network controlling sleep and arousal.
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.
How genetics can contribute to our understanding of psychiatric phenotypes.
Tutorial describing the basic search and navigation features of the Allen Mouse Brain Atlas
Tutorial describing the basic search and navigation features of the Allen Developing Mouse Brain Atlas
This tutorial demonstrates how to use the differential search feature of the Allen Mouse Brain Atlas to find gene markers for different regions of the brain and to visualize this gene expression in three-dimensional space. Differential search is also available for the Allen Developing Mouse Brain Atlas and the Allen Human Brain Atlas.
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?