“Plants are amazing!” This is something I hear a lot from non-botanists. Of course, I know plants are awesome, but every time I turn around, I learn something new and exciting. This semester was no exception. Tasked with a project in my Biomimetic Design class, led by Dr. Petra Gruber, I walked into the meadow to find inspiration– literally.
On a very wet, cold, rainy day in October, I walked to a meadow within our field station property (Bath Nature Preserve, Bath Twp., Akron, Ohio) and found a section to investigate. Indian grass (Sorghastrum nutans) towering over my head, I decided to stop at 20 steps and set up a 1m x 1m plot to sample. October in a meadow doesn’t give you very much to identify, but goldenrod (Solidago spp.) and Indian grass (S. nutans) were plentiful among a few baby asters, Galium spp. (aka ‘Cleavers’ or ‘Bedstraw’), wild strawberry (Fragaria virginiana),clumps of unidentifiable grass and moss. I measured heights of stems and area covered, took the percent coverage to determine how much each species covered the plot,and took several picture views for record. After returning to campus, I created a hand-drawn schematic of the plot.
A few weeks later, I returned to the same plot. Apparently my methods of counting and direction are spot-on because my last step landed on a pen I had dropped on that rainy day a few weeks earlier! If you’ve ever done field work, you understand how amazing it is that I found a PEN in the middle of a meadow over 2 meters high! This time I was there to measure the ability of the meadow to hold a load. I admit, I didn’t think the stems would hold up… being so late in the year and being dried out. As usual, though, plants are amazing and surprised me yet again!
I decided to test the load by creating a 1m x 1m foam board that was sturdy, yet lightweight. I placed the board directly over the plot, placing flags on each corner. The flags allowed for a visual cue to observe movement of bot
h plants and the board, as well as giving a reference point at which to measure the height of the board after each addition of weight. After the foam board was placed on top of the plants, I measured the height at each corner (flag) for the “initial” height. I added one heavy book and measured the height at each corner. Subsequently, I added increasing weight and measured the heights. At 3 books (6.7kg), the system (the meadow plot) could no longer hold the weight. Because this was the same plants were used over the entire experiment, I believe more weight can be held by the plants in true form.
So how does this happen? Plants are amazing. In the meadow, plants grow up to 10 feet below ground (roots) and above ground. You can imagine how secure this makes these cantilever beams! Here, the Indian grass and Goldenrod grew 1.5m to 2.5m above ground. The stems reached diameters of 2-5mm. You may wonder how the stems did not break when the weight was added. Galileo was the first to record these observations, noting that bending is resisted in the outer layers, not the inner stem as some might think. Several studies have investigated this design, including F.O. Bower (1930) who compared plant stems to concrete, saying, “Ordinary herbaceous plants are constructed on the same principle. The sclerotic strands correspond to the metal straps, the surroundin
g parenchyma with its turgescent cells corresponds mechanically to the concrete.” Equisetum (Horsetail) is another champion plant for many reasons, but here, in this context, it’s a biomechanic superstar. “The hollow stem of Equisetum giganteum owes its mechanical stability to an outer ring of strengthening tissue, which provides stiffness and strength in the longitudinal direction, but also to an inner lining of turgid parenchyma, which lends resistance to local buckling. With a height >2.5 m isolated stems are mechanically unstable. However, in dense stands individual stems support each other by interlacing with their side branches, the typical growth habit of semi-self-supporters.” (Spatz, Kohler, Speck 1998). Again, plants are amazing.
After doing some mathematical calculations (very much estimated
in this case because of the imprecise nature of this ‘experiment’), it is expected that a single Goldenrod stem can support >118% of its biomass! Now, we’re not talking about the strength of steel or lead, but we can see that plants offer us new possibilities when we are designing or constructing new things! Imagine a support feature that is hollow inside and allows for storage in the “stem” as well has having the strength to support weight. Think on a smaller scale: imagine a space in which a stiff, lightweight outer covering is needed to secure something. Imagine the many possibilities that plants offer us to grow using Life’s Principles.
“If the brain were so simple we could understand it, we would be so simple we couldn’t.” Lyall Watson
Summer time! For me it means working on bio-inspired algorithms, one in particular I’ve been spending some time on is Artificial Neural Networking (ANN). This had me asking my sister (who is working on her PhD in neuroscience) about how synapses, pathways, etc. work. This post will be on how ANN was inspired and some of the materials I found interesting on it. Let’s start with the obsession with neural network and why it matters? Machines do complicated mathematical calculations in a matter of seconds, yet they have difficulty performing some easy tasks such as recognizing faces, understanding and speaking in local languages, passing theTurning test. OK, let’s compare machines to our brain: A single transistor in your home computer is quite fast; only limited by speed of light and the physical distance to propagate a signal. A signal(Ions) in the neuron, on the other hand, propagates on a fraction of the speed (Flake, 1999). This begs the question, which is better? A good comparison can be found here. One main fact is that our brain makes use of a massive parallelism; it’s this massive interaction between axons and dendrites that contribute to how our brain works. Many argue that the comparison to computers is not very useful as they work differently from each other. Can we make a digital reconstruction of human brain? I follow Blue Brain project for this. Hence, as you can guess ANN algorithm is a simple imitation of how our neurons work. It works by feed forward and back propagations to learn patterns. Originally proposed as McCulloch-Putts neuron in the 1940s and 1980s by invention of Hopfield-Tank feedback neuron network. The 1960s had an good optimistic start on neural networks with the work of Frank Rosenblatt’s perceptron (a pattern classification device). However, by 1969 there was a decline in this research and publication of Perceptrons by Marvin Minsky and Seymour Papert caused it to almost die off. Minsky and Papert showed how a single perceptron was insufficient with any learning algorithm by giving it mathematical proofs. It took a while and many independent works till the value of Neural Networking came to light again. One main contribution is the two-volume book titled Parallel Distributed Processing by James L. McClelland and David E. Rumelhart and their collaborators. In this work, they changed the proposed unit step function proposed to a smooth sigmoid function and added a backward error signal propagation using weights of some hidden neurons called back propagation (Flake, 1999). Reading through chapter 20 of Parallel Distributed Processing written by F. Crick and C. Asanuma, I read about physiology and anatomy of the cerebral cortex. It shows different neural profiles.
It talks about different layers in the cortex such as the superficial, upper, middle, and deep layer, axons, synapses, neurotransmitters. The more I read, the more I come to appreciate the complexity of our brain and wonder about the simplicity of Artificial Neural Network algorithms, and can’t help but feel amazed by what Blue Brain Project is aiming to do.
Like a house-cat exploring its environment, lets dive into narrow unexplored places…
Flake, G. W. The computational beauty of nature, 1999
McClelland, J. L. Rumelhart, D. E. Parallel distributed processing, Volume 2. Psychological and biological models, 1989
Uniting the sciences is not that trivial.
I’d argue physics has done a lot in terms of breaking down the barriers between the sciences. Each science has their own physics—certain equations of phenomenon that work for their own field.
So in a sense, I can imagine physics as the center of the sciences. Only because physics brings both numbers and theory (math only brings the numbers), and it’s the theory that makes it all make sense.
To give some context, consider all of the physics off-shoots of our central fields: physical chemistry, biomechanics, biophysics, geophysics, etc. Not to mention physics’ attempt at a theory of everything—which is really just a theory of the small (which if correct is technically everything).
But I’m not convinced physics is the best intersection.
I see the problem though stemming from the way we convey physics (not that it isn’t a great choice for an intersection of the sciences). We teach it as separate things, each phenomenon has its own set of equations and rules, though they can be derived from some starting principles (newton, thermodynamics). Ultimately, by building it up as separate ideas, with clearly different models, the unity is lost: how can they work together?
This brings me finally to Biomimicry.
Biology isn’t just a good resource for solutions, it also creates great examples of the separate concepts can work together.
Biology is the application of physics. There are too many organisms that utilize the many types of physics to accomplish a goal. In a sense I would bet that anything we teach in class could be found in an organism.
The point of this is to unify the sciences not through a theory of everything, but rather a unified subject of study. Such that when we learn about physics/chemistry/engineering/mechanics it’s in the context of biology.
Unification through a common application rather than a common equation.
I think this would be a good foundation for someone who is considering an interdisciplinary path; where things are seldom purely one thing.