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Lesson title:

The probability of a hypothesis, given data.

Difficulty level: Beginner
Duration: : 7:57
Speaker: : Barton Poulson
Course Name: : Foundations of Data Science
Lesson title:

Why math is useful in data science.

Difficulty level: Beginner
Duration: : 1:35
Speaker: : Barton Poulson
Course Name: : Foundations of Data Science
Lesson title:

Why statistics are useful for data science.

Difficulty level: Beginner
Duration: : 4:01
Speaker: : Barton Poulson
Course Name: : Foundations of Data Science
Lesson title:

Statistics is exploring data.

Difficulty level: Beginner
Duration: : 2:23
Speaker: : Barton Poulson
Course Name: : Foundations of Data Science
Lesson title:

Graphical data exploration

Difficulty level: Beginner
Duration: : 8:01
Speaker: : Barton Poulson
Course Name: : Foundations of Data Science
Lesson title:

Numerical data exploration

Difficulty level: Beginner
Duration: : 5:05
Speaker: : Barton Poulson
Course Name: : Foundations of Data Science
Lesson title:

Simple description of statistical data.

Difficulty level: Beginner
Duration: : 10:16
Speaker: : Barton Poulson
Course Name: : Foundations of Data Science
Lesson title:

Basics of hypothesis testing.

Difficulty level: Beginner
Duration: : 06:04
Speaker: : Barton Poulson
Course Name: : Foundations of Data Science
Lesson title:

In this lecture, the speaker demonstrates Neurokernel's module interfacing feature by using it to integrate independently developed models of olfactory and vision LPUs based upon experimentally obtained connectivity information.

Difficulty level: Intermediate
Duration: : 29:56
Speaker: : Aurel A. Lazar
Course Name: : Open collaboration in computational neuroscience
Lesson title:

This lecture covers computational principles that growth cones employ to detect and respond to environmental chemotactic gradients, focusing particularly on growth cone shape dynamics.

Difficulty level: Intermediate
Duration: : 26:12
Speaker: : Geoff Goodhill
Course Name: : Building the Brain
Lesson title:

In this lecture you will learn that in developing mouse somatosensory cortex, endogenous Btbd3 translocate to the cell nucleus in response to neuronal activity and oriented primary dendrites toward active axons in the barrel hollow.

Difficulty level: Intermediate
Duration: : 27:32
Speaker: : Tomomi Shimogori
Course Name: : Building the Brain
Lesson title:

In this presentation, the speaker describes some of their recent efforts to characterize the transcriptome of the developing human brain, and and introduction to the BrainSpan project.

Difficulty level: Intermediate
Duration: : 30:45
Speaker: : Nenad Sestan
Course Name: : Building the Brain
Lesson title:

How does the brain learn? This lecture discusses the roles of development and adult plasticity in shaping functional connectivity.

Difficulty level: Beginner
Duration: : 1:08:45
Speaker: : Clay Reid
Course Name: : Coding and Vision 101
Lesson title:

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.

Difficulty level: Beginner
Duration: : 45:47
Course Name: : Brain medicine for non-specialists
Lesson title:

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.

Difficulty level: Beginner
Duration: : 1:36:27
Speaker: : Thomas Kluyver.
Course Name: : Jupyter Notebook
Lesson title:

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.

Difficulty level: Beginner
Duration: : 38:49
Course Name: : Research, ethics, and societal impact
Lesson title:

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.

Difficulty level: Beginner
Duration: : 46:12
Speaker: : Bernd Stahl
Course Name: : Research, ethics, and societal impact
Lesson title:

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?

Difficulty level: Beginner
Duration: : 31:35
Speaker: : Alan Winfield
Course Name: : Research, ethics, and societal impact
Lesson title:

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.

Difficulty level: Beginner
Duration: : 50:15
Speaker: : Nikolas Rose
Course Name: : Research, ethics, and societal impact
Lesson title:

In this lecture, I consider some of the key social and ethical issues raised by the ‘big brain projects’ currently under way in Europe, the USA, China, Japan and many other regions. I will draw upon our own experience in the ‘ Foresight Lab’ of the HBP to discuss the ways in which these can usefully be approached from the perspective of responsible research and innovation and the AREA approach - anticipation, reflection, engagement and action. These include data protection, privacy and data governance; the search for ‘neural signatures’ of psychaitric and neurological disorders; ‘dual use’ or the military use of developments initially intended for clinical and civilian purposes; brain-computer interfaces and neural prosthetics; and the use of animals in brain research. Following a brief discussion of the challenges of translation from the lab to the real world, I will conclude by arguing that success in contemporary scientific research and innovation is best assured by openness, collaboration, sharing with fellow researchers; robust systems of data governance involving lay persons; frankness about realities of scientific research and innovation with fellow citizens; realism about complexities of links between researchers, publics and private enterprise; and understanding and engaging with the realities of science today in the real world.

Difficulty level: Beginner
Duration: : 53:08
Speaker: : Nikolas Rose
Course Name: : Research, ethics, and societal impact