Continuing the theme of “why this version is better than the last”, I’m going to briefly talk about the new neurite system. In biology neurites include both axons and dendrites and grow out of neurons. The same applies for Distributed Neuron.

In the last version (I need to come up with a numbering scheme), neurite growth was very simplistic. Attractants and repellents were scattered around the growth space and influenced how far and in what direction the neurites grew. The static neurons then sprouted neurites which grew in their preferred direction. Once the neurite reached its destination, it randomly selected a neuron in the near vicinity of the neurite head.

There are two important problem with this scheme. The first arises from the fact that neurites only grow in one direction and do so in a completely straight line. This prevents the development of complex structures. Secondly, neurites only make one synapse at the termination point. If, for instance, a dendrite runs into an axon on the way to the destination, no synapse is made.

The new system is a bit more complicated but yields far superior results. After neurons have finished their migration phase, they enter a neurite growth phase. Neurites grow in a more three-dimensional manner now. Axons and dendrites have a direction they tend to grow in but can swerve from their path. In addition, they can also randomly branch along the main growth path, sprouting a new child neurite which continues growing. This branch can also branch, leading to an elaborate tree structure. This three-dimensional growth system allows multiple synapses. Anywhere an axon and dendrite collide a synapse is formed, resulting in a more realistic model.

The details of this (direction, length, branching probability, etc) are fully configurable and specific to the each cell type. Axons have separate parameters from dendrites, leading to different morphologies for each type of neurite. For instance, axons could have a high length value but zero branching leading to a long process. Conversely, dendrites could have medium length and high branching, leading to a large “tree”.

Of course, the neurites are not limited to the stereotypes we see in our brain. Evolution doesn’t really care as long as it works. It should be interesting to see the various cell types and morphologies associated as the system begins to evolve.

And of course, here is a pretty picture. You can see a single cell (magenta) with its axon and dendrite showing. The axon is grey, the dendrite cyan. I don’t know the parameters off hand, but they are relatively generic. The dendrite appears to have a higher branching factor while both appear to have relatively small lengths.

Click for big.

A lot of the work I’ve been doing in lab lately is related to brain development. Your brain is an absolutely amazing mess of pathways, fibers and tracts. The interconnected nature of the brain is far more sophisticated than simple dendrite trees in a big cluster. Furthermore, your brain and my brain are nearly identical, as far as gross anatomy is concerned. You have a hypothalamus, an amygdala, a corpus callosum and a suprachiasmatic nucleus, etc etc ad nauseum. The connections between the various brain regions is likewise similar. This simple fact is actually quite astonishing: every human brain is hardwired to organize into the same gross pattern.

Indeed, hordes of researchers are actively pursuing the mysteries of brain development. Many diseases are caused by improper maturation of the brain. It is becoming increasingly clear that the development of the brain, while complicated, is by no means magic. Various signaling chemicals and peptides direct axons to their destination, either attracting or repelling. Chemical gradients create complex migration behavior depending on your location in the gradient. Importantly, this intricate chemical dance is entirely reproducible - it is the reason your brain looks like mine.

The previous iteration of Distributed Neuron consisted of essentially random placement and a semi-random connection system (through the use of simple “chemical” attractants). This system left a lot to be desired. Results were unpredictable each iteration due to the inherent random nature. Gross anatomy was difficult to develop because neuron placement was random and fixed. Once a neuron was placed, it was permanent. The connection system allowed minor segregation by influencing the direction and distance neurites could travel. It was, in my opinion, too little of an affect to be worthwhile.

Enter the newest revision. Again, neurons are placed into a three-dimensional environment. Each neuron is a 1×1x1 cube. Initial placement is fixed and predictable. This iteration, instead of immediately growing neurites for connections, enters a “migration” phase. Each neuron has a base migration value that defines how far and in what direction it travels. Chemo-emitters are strewn about the three-dimensional space. As neurons migrate, they may change their differentiation pattern according to what chemo-emitters are nearby. This differentiation change affects the base migration properties, altering migration direction. Importantly, no randomness is involved so the results are identical each time.

In this way, neurons not only migrate to different positions but also change their cell fate depending on their location. The non-random nature makes testing/analysis easy because the results are identical from one run to the next. Small variations in the placement and quantity of chemo-emitters can drastically alter the gross anatomy of the network, providing an infinite supply of variations.

There are still pieces I would like to iron out, but for now, the system works very well and can produce some interesting results. Here is one such sample network, from two different views. It consists of 10,000 neurons and has already performed its “migration” phase.

Colors indicate different cell “types”. This particular network has 4 cell types (red, magenta, blue, and one lonely green). As you can see, the network formed a thick magenta sheet. Pressed against this are two separate sheets, one blue and one red. It should be noted that the initial placement of neurons was actually in the bottom-right corner of the screen, meaning the neurons migrated top-left. It should also be known that initial placement was in a small cube, which proceeded to flatten and elongate into a sheet.

Click for big.

I’ve been working in a laboratory almost a full year now. And I have learned a very crucial fact: experiments rarely work as you like and often don’t work at all.

Considering my last post was four months ago, it is time to evaluate the success of this experiment. This blog was an experiment in the world of science writing. It started as a journal to document my progress on Distributed Neuron. Life and school got in the way, limiting my programming time. Wanting to keep this blog alive, I began writing about science. This was quite enjoyable but alas required too much time and commitment. My updates slacked, slowed and finally disappeared.

The summer is almost upon me. Exams are bearing down but hope is on the horizon. I’m staying at my university this summer to continue my research. Ten weeks of blissful, uninterrupted research (read: frustrating results and unexpected failures intermittently interspersed with glimmers of hope). It will be good.

As always, Distributed Neuron lumbers on. Inch by inch the code is being laid down. Much of the internals have been changed or rewriting. Infact, the entire goal of the project has drastically shifted (several times). I am, however, confident that my current iteration and vision is the one that will make it to the finish line. If the finish line is a Nobel Peace Prize or the dumpster remains to be seen.

This summer will be no less busy than the school year but I desire having a forum to speak about my ideas and updates, regardless of viewership. I am moving this blog back to the original foundation - a journal to document my progress on Distributed Neuron. Perhaps an article or two on science will be written but the bulk will be small updates regarding my code, ideologies and philosophic quandaries encountered while programming.

Onwards.

I just finished my last exam, which is fantastic. This has been the semester from hell. I apologize for my lack of posts on Distributed Neuron. Considering my…

• 21 credit coursework (Organic Chemistry, Physics II, Genetics, Biology, General Psychology)
• Independent lab work
• Fraternity responsibilities
• BPR3 coding

…free time has been a rare commodity. Something had to go and it unfortunately was Distributed Neuron on the chopping block (not to mention my social life!).

But don’t worry, I’ve got a veritable stockpile of papers that I want to discuss. I’m also resuming work on Distributed Neuron the project. I’ve got some interesting new ideas which I think are both novel and clever. More details on that in the future.

For now, I need to go pack. Be on the lookout for some articles in the near future!

The latest Encephalon is up over at Brain In A Vat. This issue includes herpes related Alzheimer’s, brain structure of ADHD patients, language in infants and debunking of pseudoscience. Great issue, chock full of interesting articles. Go check it out.

Neuroscience and human behavior still remain mainly a mystery. Because of that, they have accrued an unusually high number of theories. Take any intro to psychology class to see the diversity and breadth of theories that supposedly explain human behavior. So hey, why not add one more?

Two Carnegie Mellon scientists have come up with a new theory, this one based off a computational slant. I haven’t read their paper yet (saving that for my 5 hour layover in Detroit later this week) but the press release makes me dubious. Their theory involves regions of the brain “volunteering” themselves for tasks. When a primary region is damaged, lesser apt regions volunteer to take the load and restore partial function. This mimics how the brain recovers from damage by switching brain activity to a undamaged region.

I’ll withhold criticism until I read the paper, but it sounds far too simplistic. I highly doubt the regions in our brain are volunteering on a moment-to-moment basis over who can perform which task. Plus, I doubt their model incorporates regions growing or expanding, rather than just being damaged or not. And I doubt it includes the capability to incorporate new regions.

Carnegie Mellon University neuroscientist Marcel Just and Stanford postdoctoral fellow Sashank Varma have put forward a new computational theory of brain function that provides answers to one of the central questions of modern science: How does the human brain organize itself to give rise to complex cognitive tasks such as reading, problem solving and spatial reasoning? Just and Varma’s theory, called 4CAPS, is described in the fall issue of the journal Cognitive, Affective, and Behavioral Neuroscience.

[...]

Just and Varma, however, propose that the evidence reveals a more complex picture in which thinking is a network function — a collaboration of several brain areas that is constantly adapting itself, based on the task at hand and the brain’s own resources and biological limitations. The collaborating parts of the brain, according to Just, are like members of a sports team whose players substitute in and out of the action. 4CAPS (an acronym for Capacity Constrained Concurrent Cortical Activation-based Production System), proposes a decentralized process by which members of the cortical team volunteer themselves when their strengths are called for, but also permits less efficient but capable members to step forward when the primary player is injured or disabled, as might occur as a result of a stroke. Just and Varma have constructed a number of computational models to demonstrate this process, such as a model that understands English sentences.

I know, my blog has been less than spectacular lately, receiving very little attention. I’ve been incredibly busy. Luckily I should have some more time in the near future and even more time next semester.

Suffice to say, coursework and labwork is making my life busy. My pet project, Distributed Neuron, has gotten even less attention. I do have some major changes in the pipeline which I’ll blog about at a later date. For now though, my time is being diverted to scholarship and lab work.

And of course, I’m also working on BPR3. I’m really pleased how things are turning out. Uriel designed us a beautiful style for the site and I’ve been coding my busy little college butt off trying to get everything running. We are currently alpha testing with a small group of enthusiastic bloggers. Mum’s been the word lately, but Dave posted a teaser snippet at BPR3 so I suppose I can show it as well. Enjoy:

Unrelated, but this was my 100th post at Distributed Neuron. Hooray!

Here is a short but sweet report regarding neural progenitors that I’ve been meaning to write about for a while. Previous work from this lab showed that neural progenitors could be implanted into the brains of mice and help recover function after stroke. They showed that the implanted progenitors had migrated into the stroke region. This study is an extension of the previous work and attempted to determine if migration was due to a signal provoked by a stroke.

More details after the jump.
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Suffice to say, I’m stoked. This has been a good last two weeks in terms of seminars. First Cori Bargmann stops by to talk to us about her fascinating work on C. Elegans, then Kathryn Anderson shows up and gives a wonderful talk on her recent work. Now, when I didn’t think life could get any better, Jeff Hawkins is coming to town. It’s like Christmas, but with more neuroscience and less fruitcake.

RPI: News & Events - Rensselaer To Host Lecture by PalmPilot Inventor Jeff Hawkins
Jeff Hawkins, best known as the co-founder of the Palm and Handspring companies and as the architect of computing products such as the PalmPilot and Treo smartphone, will be on the Rensselaer campus Wednesday, Nov. 14 to discuss a new technology platform based on a theory of the human neocortex.
Jeff Hawkins

It will also be webcast live at: the Vollmer Fries Lecture Web site. Be sure to check it out, Jeff Hawkins is one of the movers and shakers of the artificial intelligence field. He has some interesting theories (presented in his book, On Intelligence). Some I agree with, others I don’t. it should be an interesting lecture. I’ll post my notes afterwards.

The brain is a complicated place and one small mutation could set things on fire. Epilepsy is an excellent example. There is no single gene that is the source of epilepsy. Rather, epilepsy can develop from any number of mutations that affect the various processes in the brain. But what happens when you have multiple mutations for known epilepsy genes? Recent research shows two mutations that actually compliment each other to restore normal function.

More details after the jump.
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