#CCNeuro asks: “How can we find out how the brain works?”

The organizers of the upcoming conference Cognitive Computational Neuroscience (#CCNeuro) have done a very cool thing ahead of the meeting. They asked their keynote speakers the same set of 5 questions, and posted their responses on the conference blog.

The first of these questions is “How can we find out how the brain works?”. In addition to recommending reading the insightful responses of the speakers, I offer here my own unsolicited suggestion.

A common theme among the responses is the difficulty posed by the complexity of the brain and the extraordinary expanse of scales across which it is organized.

The most direct approach to this challenge may be to focus on the development of recording technologies to measure neural activity that more and more densely span the scales until ultimately the entire set of neural connections and synaptic weights is known. At that point the system would be known but not understood.

In the machine learning world, this condition (known but not understood) is just upon us with AlphaGo and other deep networks. While it has not been proven that AlphaGo works like a brain, it seems close enough that it would be silly not to use as a testbed for any theory that tries to penetrate the complexity of the brain a system that has human level performance in a complex task, is perfectly and noiselessly known, and was designed to learn specifically because we could not make it successful by programming it to execute known algorithms (contrast Watson).

Perhaps the most typical conceptual approach to understanding the brain is based on the idea (hope) that the brain is modular in some fashion, and that models of lower scale objects such as cortical columns may encapsulate their function with sufficiently few parameters that the models can be built up hierarchically and arrive at a global model whose complexity is in some way still humanly understandable, whatever that means.

I think that modularity, or something effectively like modularity is necessary in order to distill understanding from the complexity. However, the ‘modularity’ that must be exploited in understanding the brain will likely need to be at a higher level of abstraction than spatially contiguous structures such as columns, built up into larger structures. The idea of brain networks that can be overlapping is already such an abstraction, but considering the density of long range connections witnessed by the volume of our white matter, the distributed nature of representations, and the intricate coding that occurs at the individual neuron level, it is likely that the concept of overlapping networks will be necessary all the way down to the neuron, and that the brain is like an extremely fine sparse sieve of information flow, with structure at all levels, rather than a finite set of building blocks with countable interactions.

Review of “Incognito: The Secret Lives of the Brain” by David Eagleman

Most of our brain activity is not conscious –  from processes that maintain our basic physiology to those that determine how we catch a baseball and play a piano well. Further, these unconscious processes include those that influence our basic perceptions of the world. Our opinions and deepest held beliefs – those that we prefer to feel that our conscious mind completely determines –  are shaped largely by unconscious processes. The book, “Incognito: Secret Lives of the Brain” by David Eagleman, is an engaging account of those processes – packed with practical and interesting examples and insight. Eagleman is not only a neuroscientist, but an extremely clear and engaging writer. His writing, completely accessible to the non expert, is filled with solid neuroscience, packaged in a way that not only provides interesting information, but also builds perspective. It’s the first book that I’ve encountered that delves deeply into this particular subject. We mostly think of our brains as generating conscious thought, but, as he explains it’s just the small tip of the iceberg.  

Continue reading “Review of “Incognito: The Secret Lives of the Brain” by David Eagleman”

Mini Book Review: “Explaining the Brain,” by Carl Craver

Explaining the Brain” is a 2007 book by Carl Craver, who applies philosophical principles to comment on the current state of neuroscience. This is my first and only exposure to the philosophy of science, so my viewpoint is very naive, but here are some main points from the book that I found insightful.

The book starts by making a distinction between two broad goals in neuroscience: explanation, which is concerned with how the brain works; and control, which is concerned with practical things like diagnosis, repair, and augmentation of the brain. In my previous post on this blog, I tried to highlight that same distinction. This book focuses on explanation, which is essentially defined as the ability to fully describe the mechanisms by which a system operates.

A major emphasis is on the question of what it takes to establish a mechanism, and the notion of causality is integral to this question.

Continue reading “Mini Book Review: “Explaining the Brain,” by Carl Craver”

Understanding ‘Understanding’: Comments on “Could a neuroscientist understand a microprocessor?”

The 6502 processor evaluated in the paper. Image from the Visual6502 project.

In a very revealing paper: Could a neuroscientist understand a microprocessor?”, Jonas and Kording tested a battery of neuroscientific methods to see if they were useful in helping to understand the workings of a basic microprocessor. This paper has already stirred quite a response, including from Numenta, the Spike, Arstechnica, the Atlantic, and lots of chatter on Twitter.

This is a fascinating paper. To a large degree, the answer to the title question as addressed by their methods (connectomics, lesion studies, tuning properties, LFPs, Granger causality, and dimensionality reduction), is simply ‘no’, but perhaps even more importantly, the paper brings focus to the question of what it means to ‘understand’ something that processes information, like a brain or a microprocessor. Continue reading “Understanding ‘Understanding’: Comments on “Could a neuroscientist understand a microprocessor?””

My Wish List for the Ultimate fMRI System

 

The ultimate MRI scanner cake my wife made about 6 years ago to celebrate both the 50th birthday of my colleague Sean Marrett and the installation of our new 7T scanner.

I recently had a meeting where the topic discussed was: “What would we like to see in the ideal cutting edge and future-focussed fMRI/DTI scanner?” While those who use fMRI are used to some progress being made in pulse sequences and scanner hardware, the technological capability exists to create something substantially better than we have now.

In this blog posting, I start out with a brief overview of what 
we currently have now in terms of scanner technology. The second part of this blog is then focussed on what my ideal fMRI system would have. Lastly, the article ends with a summary outline of my wish list – so if you want to get the gist of this blog, scroll to the list at the bottom. Enjoy and enter your comments! Feedback, pushback, and more ideas are welcome! 

Continue reading “My Wish List for the Ultimate fMRI System”

Ten Unique Characteristics of fMRI

A motivation for this blog is that since our graduate student days, Eric Wong and I have had hundreds of great conversations about MRI, fMRI, brain imaging, neuroscience, machine learning, and more. We finally decided to go ahead and start posting some of these, as well as thoughts of our own. It’s better – for us and hopefully others – to publicly share our thoughts, perspectives, and questions, than to keep them to ourselves. The posts are varied in topic and format. In certain areas, we know what we’re talking about, and in other others, we might be naïve or just wrong, so we welcome feedback! We also welcome guest blogs as we hope to grow the list of guest contributors and readers.  Continue reading “Ten Unique Characteristics of fMRI”

What does it mean to understand the brain?

Thanks to Peter Bandettini for the idea of starting a blog, and for offering to let me partner with him in this endeavor. We hope you find it interesting.

In this my first contribution to theBrainBlog, I would like to outline some of my initial thoughts about what a useful understanding of the human brain might look like. Continue reading “What does it mean to understand the brain?”