Neuroscience History
neuroscience history
The history, future and ethics of self-experimentation
Modern day “neurohackers” are radically self-experimenting, attempting genomic modification with CRISPR-Cas9 constructs and electrode insertion into their cortex amongst a host of other things. Institutions wanting to avoid the risks bought on by these procedures, generally avoid involvement with self-experimenting research. Modern day “neurohackers” are radically self-experimenting, attempting genomic modification with CRISPR-Cas9 constructs and electrode insertion into their cortex amongst a host of other things. Institutions wanting to avoid the risks bought on by these procedures, generally avoid involvement with self-experimenting research. But what is the ethical thing to do? Should researchers be allowed or encouraged to self-experiment? Should institutions support or hinder them? Where do you think that this process of self-experimentation could take us? This presentation by Dr Matt Lennon and Professor Zoltan Molnar of the University of Oxford, will explore the history, future and ethics of self-experimentation. It will explore notable examples of self-experimenters including Isaac Newton, Angelo Ruffini and Oliver Sacks and how a number of these pivotal experiments created paradigm shifts in neuroscience. The presentation will open up a forum for all participants to be involved asking key ethical questions around what should and should not be allowed in self-experimentation research.
Dr Lindsay reads from "Models of the Mind : How Physics, Engineering and Mathematics Shaped Our Understanding of the Brain" 📖
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.