Now You See Me
Evolution of the nervous system allowed us to see the world around us. Understanding it may allow us to see the next big technological advancement.
What does it mean to perceive? We take for granted our breathtakingly advanced nervous system, which senses our surroundings with pinpoint precision and integrates an inordinate amount of information. It’s estimated that upwards of 11,000,000 bits of sensory information is processed by the nervous system (Zimmermann, 1986) and that a staggering 1,000,000,000,000,000 bits (a petabit) of information is exchanged across the brain every second (Yu & Yu, 2017), providing us with an immersive perceptual understanding of our environment. But this is not something that animals have always had at their disposal; indeed, many animals still outperform us without a nervous system. The tale of how the nervous system evolved is one of energy envy and predation pressure over millions of years, forcing constant adaptation through many generations of natural selection, eventuating in the quirky electrochemical system which is quintessential to our existence and survival as a species. From the first neural networks appearing in ancient sponge-like species, to the massive potential that nervous system evolution represents for computational advancements, the tale of how we began to see and perceive the world around us is pivotal to how we understand and shape our future.
Energetic Evolution
Evolution is about energy. Modern evolutionary theories explain evolution as an energetic process; free energy exists in the environment and natural selection essentially selects for organisms which have higher return on energy investments (Yun, Lee, Doux, & Conley, 2005). This ‘return on energy’ is a term penned by researchers as ‘ROE’ in 2005 in an attempt to further our understanding of evolutionary advancements (Yun et al., 2005). The cycle of life and death over many generations allows organisms to gradually develop new methods of increasing ROE, while adaptations that yield low ROE eventually lead to extinction (Yun et al., 2005). For example, nervous systems are very costly to organisms, requiring energy and other resources constantly for maintenance, development and growth (Wang & Clandinin, 2016). However, they can also enable organisms to reap a greater return on this energy investment by allowing for sensation, interpretation and prediction of the external environment in order to maximize energy and nutrient intake whilst avoiding potential predation and harm (Wang & Clandinin, 2016). Even the smallest of advantages have a monumental impact on the trajectory of evolution, and as a result, nervous systems have evolved in a variety of animals.
“Even the smallest of advantages have a monumental impact on the trajectory of evolution.”
As we follow the evolutionary history of the nervous system, it is important to note how each adaptation along the way allows for an improved ROE, as this will provide insight into how we ended up with the nervous system we have, and give us clues as to how we can best improve ROE in our technologies and medicines.
Emergence of Ion Channels
The key to a nervous system is communication, and in order for cells to communicate, ion channels are essential. It is thought that K+ channels were the first voltage gated channels to make an appearance (Kristan, 2016). These channels may have evolved from the union of a voltage-gating module and pore module, both of which existed prior to voltage gated channel itself (Moran, Barzilai, Liebeskind, & Zakon, 2015). Evolutionary biologists believe that these early channels responded to stretching of the cell membrane, allowing the first multicellular organisms to regulate their internal volume (Kristan, 2016). The later evolution of Ca2+ channels allowed organisms to begin regulating their internal metabolism (Kristan, 2016). In modern nervous systems, voltage-gated Ca2+ channels initiate action potentials, the ion fluxes which allow for communication along axons of nerve cells (Catterall, 2011), and voltage-gated K+ channels terminate the action potential (Kristan, 2016) (note both channels also have various other functions). However, the Na+ channels which create the potential difference in an action potential appear to have evolved later, though still more than a billion years ago (Moran et al., 2015). From the paradigm of energy efficiency, the evolution of these channels would have enabled early organisms to regulate ion flow in and out of their membranes, maintaining electro-chemical gradients and thus increasing their energy carrying capacity.
Enigmatic Ediacarans
During the Cryogenian period, the Earth was in the middle of the last ‘Snowball Earth ice age’ (Brocks et al., 2017). Though simple life existed, the conditions were hostile, unforgiving and prohibitive of much more than basic algae evolving (Brocks et al., 2017). Then, around 560 million years ago (Ma), the oceans started to clear, and things started looking brighter for life on the sea floor (Feinberg & Mallatt, 2016). Sunlight could now penetrate the shallows, and a thick mat of photosynthetic microbes began to spread, heralding the beginning of the Ediacaran period (Feinberg & Mallatt, 2016). Most animals were flat and slow, if not entirely immobile (Feinberg & Mallatt, 2016). Their flat structure enabled them to consume the microbial mat by external digestion, releasing digestive acids to break down their meals. There were no predators for these animals, so understanding the world around them and developing escape mechanisms were entirely unnecessary. Instead, efficiency of eating was the goal.
One of the animals which dominated this landscape was Dickinsonia. Dickinsonia were reasonably large (Ivantsov & Malakhovskaya, 2002), flat animals with a ciliated, mucus covered ventral surface (Arendt, Benito-Gutierrez, Brunet, & Marlow, 2015). This surface was multipurposed; it allowed for external digestion and possible movement across the microbial mat (Arendt et al., 2015). For Dickinsonia and similar animals, the world was essentially two-dimensional; they moved only across the mat, consuming whatever was directly beneath them but never venturing below the mat, and entirely unaware of what lay above them (Arendt et al., 2015). This lifestyle negated the need for a nervous system; with no major predators and a relatively simple environment, Dickinsonia did not need such a system to efficiently consume nutrients and move across the microbial mat (Arendt et al., 2015).
From an evolutionary perspective, the main competitors of the time would have most likely been conspecifics (organisms of the same species) or similar animals, who were also dieting on the same energy source; the microbial mat (Droser & Gehling, 2015). This pressure to eat most efficiently may have been what lead to several key adaptations. It is thought that Dickinsonia may have operated using a system of cilia moving in response to chemosensory stimulus detected by specialised epithelial cells like the modern Calliactus parasitica (Arendt et al., 2015). They may have also transported nutrients using a network of gastrovascular canals like that of many cnidarians and ctenophores (Arendt et al., 2015). However, this specialisation of cells (especially excretory cells on the mucociliary ventral surface, contractile cells that allowed for movement and enterocytes which allowed for distribution of nutrients) was the first real groundwork towards a functioning nervous system (Arendt et al., 2015).
“Specialisation of cells… was the first real groundwork towards a functioning nervous system.”
The First Nervous System
Another notable in the nervous system hall-of-fame is Trichoplax adhaerens, a possible evolutionary descendant of Dickinsonia (though this relationship is still ambiguous) (Sperling & Vinther, 2010). Animals with nervous systems utilise three different families of voltage-gated calcium channels in order to regulate cellular excitability; CaV1, CaV2 and CaV3 (Smith et al., 2017). However, the discovery that Trichoplax has the genes for all three of these channels, but does not have a nervous system may show us how the transition from simple ion channels to complex nervous systems took place (Smith et al., 2017). First discovered on the side of a fish tank over 135 years ago (Jorgensen, 2014), Trichoplax is a flat, tiny marine animal, barely a few millimetres wide at largest, which might easily be mistaken for an amoeba to the untrained eye (Moran et al., 2015).
However, this seemingly insignificant animal navigates with remarkable coordination in response to sensory stimulus (Smith, Pivovarova, & Reese, 2015) as it glides around snacking on organic debris and pieces of micro-algae (Miller & Ball, 2005). Genetic sequencing of Trichoplax suggests that it contains all of the necessary genes for the components of a functioning nervous system, but it appears to lack most of the physical apparatus required — it has all of the ‘software’ without the right ‘hardware’ (Jorgensen, 2014).
By the late Ediacaran period, just before the well-known Cambrian explosion, the first evidence of an actual nervous system appeared (Arendt et al., 2015). It’s still up for debate exactly when the first nervous system developed and which organism owned it. However, by towards the end of the Ediacaran, burrows and foraging patterns began to appear in ordered, efficient manners, suggesting that some organisms were integrating sensory information and motor output with the help of a nervous system (Carbone & Narbonne, 2014). It is theorised by many that the simple nets of nerves like those found in cnidarians may have been the first to emerge, as they appear to be the next plausible step from the specialisation of cells for sensing and digesting found in animals like Dickinsonia and Trichoplax (Arendt et al., 2015). For neural networks to be beneficial, animals must have had usable sensory input (for understanding the world around them) and some form of musculature (for manipulating the world around them) (Arendt, Tosches, & Marlow, 2016). For this reason, it is thought that the emergence of the nervous system must have coincided or been preceded by the development of gastrula-like developmental stages (found in many organisms, this developmental method involves the separate development and differentiation of different cell layers within a growing organism) (Arendt et al., 2016).
If these early nervous system innovators reflected the structure of similarly simple systems found in modern organisms, it is likely that they formed neural nets (Arendt, Bertucci, Achim, & Musser, 2019) in the ectoderm (outer developmental cell layer) which directly controlled longitudinal muscle fibres on the peripheral of the animal, whilst also communicating with inner circular muscle fibres by hormonal secretion (similar to the secretory systems seen earlier) (Arendt et al., 2015). These first animals with neural nets are known as ‘neuralians’ (Arendt et al., 2015). Neuralian ancestors such as the cnidarian-bilaterians evolved more advanced neural nets, and specialised cells began to segregate, with a mechanosensory cells forming a sensory-integration centre and contractile and motor cilia cells congregating around the gastric cavity (Arendt et al., 2015). This system may have allowed the cnidarians to sense and direct water flow containing nutrients in order to maintain a more constant supply of energy (Arendt et al., 2015). For the first time, complex stimuli could be sensed, integrated and produce beneficial actions for the animal using a nervous system (Arendt et al., 2016).
The Cambrian Arms Race
As the sun set on the Ediacarans and the Cambrian period began, predation pressure become a major driver of evolution. The quest for the best return on energy investment had led to a variety of animals preying on each other, as it was often more efficient to consume the energy another organism had gathered than slowly gather it themselves (Carbone & Narbonne, 2014). This in turn led to a remarkably quick disappearance of most Ediacaran organisms, as they were forced to change by the growing environmental pressures (Droser & Gehling, 2015). A variety of defence mechanisms were developed throughout the Cambrian arms race in order to maintain ROE without becoming someone else’s meal. Hard exoskeletons, borrowing behaviours become popular among some, whilst improved predation mechanisms and weaponry become commonplace among others (Hoyle, 1975). This new world of advanced warfare opened up the third dimension, as animals began to chase and escape by swimming upwards, borrowing downwards and fleeing forwards at faster speeds (Carbone & Narbonne, 2014).
This new world of advanced warfare opened up the third dimension, as animals began to chase and escape by swimming upwards, borrowing downwards and fleeing forwards at faster speeds.
It was in this context that nervous systems were suddenly invaluable; the ability to see further, predict more accurately and make more efficient decisions were the advantages that determined who achieved the greatest ROE and lived to see the next generation.
With their newfound nervous systems, animals were becoming more aware of their environments, but this was not without its challenges. Early chemoreception, electroreception and other sensory inputs were not completely accurate (no sensory cell ever is) in their perception of the world, as there are limitations on the sensitivity, accuracy and memory of the nervous system, among other factors. The information available to organisms was now plentiful, but interpreting this information in a meaningful way was the next step in improving ROE and there were many trade-offs to be made (Ryan & Chiodin, 2015).
For instance, increasing the number of sensory cells allows for more accurate perception of a predator or preys’ location, but requires more energy to maintain and a more advanced nervous system to integrate; depending on an animal’s environment, they may not need to pay these costs as a simple system may suffice. Similarly, increased capacity for memory allows for better prediction of the state of the world when variables remain relatively constant.
However, having such a memory in a quickly changing, fast paced environment may actually lead to less accurate predictions, as the information gathered yesterday may not be relevant today, or even information sensed seconds ago may be misleading in the present. The reliability of sensory input was also an issue; due to the inaccuracies in the system, it simply wouldn’t be energy efficient for animals to flee whenever they sensed prey, as false alarms due to biological noise and limitations meant that they would be in an almost constant state of escape, wasting valuable energy and losing opportunities to consume nutrients. However, the opposite is also true; if they entirely ignored sensory stimuli, they were no better off than their competitors without nervous systems. This required more complex integration centres, allowing animals to determine when the amplitude of sensory input was significant enough to flee the scene, despite the potential energy loss.
The many variables in the varying environments of the Cambrian (and later) species lead to diverse applications of the nervous system structure, specialising to increase ROE and decrease rates of predation by mapping probability functions of the world around them in sensory-integration centres. In some animals, this resulted in losing their nervous system, or parts of it, as it became a bad energy investment due to environmental factors of the animal’s niche (Ryan & Chiodin, 2015). Some researchers suggest that organisms like the previously discussed Trichoplax adherens and ctenophores were actually descendants of animals which had nervous systems, but later lost them as there were not beneficial within their niche (Ryan & Chiodin, 2015). This idea is plausible, but both sides of the argument lack conclusive evidence. Proponents of the argument that they did in fact lose their nervous system suggest that neuronal projections may have interfered with efficient filtration of small particles in sponge species, the high metabolic cost of maintaining neurons, or the potential parasitic lifestyle that Trichoplax and Porifera may have embraced (Ryan & Chiodin, 2015).
Evolutionary Energy Efficiency
Understanding how our nervous system evolved not only allows us to examine our past, but provides us with insight into the future, and may hold the key to new medical treatments and technological advancements. Evolution of the nervous system was driven by energy efficiency; the system is only beneficial provided it can enable gathering of more energy than it consumes itself. Over millions of years of evolutionary pressure, the nervous system is an excellent designed, incredibly efficient precision machine. Modern computing, however, has been driven by the race to improve processing power (bits per second), not processing efficiency (bits per joule). Recent attempts to simulate animal nervous systems using the latest computing technology reveal just how energy efficient and advanced the brain really is.
In 2009, IBM’s research department endeavoured to eventually simulate neural computation similar to that of a human brain. They have successfully modelled a brain with the approximately the complexity of a cat’s cortex (Brodkin, 2009). This simulation was an impressive feat by today’s standards, with over 10 trillion synapses modelled with 147,456 processors and 144TB of memory (Brodkin, 2009).
However, this simulation is still only approximately 4.5% as powerful as the human cerebral cortex and runs a hundred time slower and more than a million times less efficiently than an actual feline brain (Howard, 2012). For comparison, whilst the human brain runs perfectly on the same energy a light bulb uses, the closest theoretical computer capable of mimicking the brains abilities would require the entire energy output of a small hydroelectric plant; current computing solutions are abysmally inefficient compared to the human nervous system (Howard, 2012). As IBM lead researched explained on completing the feline cerebral cortex simulation, the animal brain “is more efficient than our computers by a factor of a billion, and it has the uncanny ability to integrate sight, hearing, taste, touch, smell… and act on it.” (Brodkin, 2009).
“[the animal brain] is more efficient than our computers by a factor of a billion, and it has the uncanny ability to integrate sight, hearing, taste, touch, smell… and act on it.” -Dharmendra Modha, Lead IBM Researcher
By developing an understanding of how animal nervous systems evolved, we are slowly revealing the various mechanisms they have developed to increase efficiency of space, time and energy. A recent study by Yu & Yu in 2017 examined this closely and discovered a huge variety of mechanisms which have evolved for efficiency due to predation pressure and competition with conspecifics (animals of the same species). They found that some invertebrates have relatively inefficient systems compared to mammalian neurons, which have evolved to have almost the theoretically smallest possible energy consumption for transferring information (Yu & Yu, 2017). The pressures of natural selection in a mammalian environment have led to an extremely low ratio of sodium entry and total sodium load compared the physical minimum, allowing them to maintain a large, complex nervous system without unnecessarily wasting energy (Yu & Yu, 2017).
Interestingly though, it appears that the nervous systems of invertebrates, which followed a different evolutionary path, have not achieved the same efficiency (Yu & Yu, 2017). However, they note that further research should be done on this topic before making a conclusion regarding invertebrates, whilst also allowing for better understanding of why the systems differ (Yu & Yu, 2017). Another adaptation which increases efficiency is the trend across the nervous system of increasing the diameter of axons proportional to the rate of firing in that neuron (Yu & Yu, 2017). This maintains a high level of efficiency, and has resulted in most axons in human nervous systems being so thin that they are nearing the physical limit of interference caused by ion channel noise (Faisal, White, & Laughlin, 2005).
There are still many mysteries surrounding the efficiency of animal nervous systems; for instance, it is a known fact that dendrites and specialised synapses are very cost-efficient at conducting signals in different ways, but many of the exact mechanisms are entirely unknown (Yu & Yu, 2017). Examining the impacts that pressures of evolution have had on energy conservation in the nervous system will provide us with invaluable insights into how a nonlinear multi-faceted computational device could one day be created in order to mimic the brain’s incredible energy efficiency and integrated computing power (Yu & Yu, 2017).
The nervous system has a remarkable evolutionary history. Ion channels first allowed a level of control over the internal environment of early organisms, and as animals began to develop more complex behaviours (like Dickinsonia and Trichoplax), the scaffolding for a nervous system began to evolve. Guided by the race for energy efficiency and driven by pressure from both conspecifics and predators, this primitive system advanced from a nerve net to a more specialised system, differing across different species in different environments. As a result, the nervous system we have inherited is an incredibly efficient, complex and finely tuned adapting network which has allowed us to perceive, describe and manipulate the world around us. Understanding the manner by which this system evolved holds great potential for how our technology ‘evolves’ into the future, and continuing to research and uncover our evolutionary history may enable great strides forward in the energy efficiency of computing.
References & Further Reading
Arendt, D., Benito-Gutierrez, E., Brunet, T., & Marlow, H. (2015). Gastric pouches and the mucociliary sole: Setting the stage for nervous system evolution. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1684), 20150286. https://doi.org/10.1098/rstb.2015.0286
Arendt, D., Bertucci, P. Y., Achim, K., & Musser, J. M. (2019). Evolution of neuronal types and families. Current Opinion in Neurobiology, 56, 144–152. https://doi.org/10.1016/j.conb.2019.01.022
Arendt, D., Tosches, M. A., & Marlow, H. (2016). From nerve net to nerve ring, nerve cord and brain — Evolution of the nervous system. Nature Reviews Neuroscience, 17(1), 61–72. https://doi.org/10.1038/nrn.2015.15
Brocks, J. J., Jarrett, A. J. M., Sirantoine, E., Hallmann, C., Hoshino, Y., & Liyanage, T. (2017). The rise of algae in Cryogenian oceans and the emergence of animals. Nature, 548(7669), 578–581. https://doi.org/10.1038/nature23457
Brodkin, J. (2009). IBM brain simulations exceed scale of cat’s cortex: ‘Historic milestone’ on way toward simulations of human brain. Network World.
Carbone, C., & Narbonne, G. M. (2014). When Life Got Smart: The Evolution of Behavioral Complexity Through the Ediacaran and Early Cambrian of NW Canada. Journal of Paleontology, 88(2), 309–330. https://doi.org/10.1666/13-066
Catterall, W. A. (2011). Voltage-Gated Calcium Channels. Cold Spring Harbor Perspectives in Biology, 3(8). https://doi.org/10.1101/cshperspect.a003947
Chan, M. A. (2014, April 1). 01 April: Adelaide — 2014 GSA Distinguished International Lecture Tour. Retrieved 20 August 2019, from Speaking of Geoscience: The Geological Society of America’s Guest Blog website: https://speakingofgeoscience.org/2014/04/01/01-april-adelaide-2014-gsa-distinguished-international-lecture-tour/
Droser, M. L., & Gehling, J. G. (2015). The advent of animals: The view from the Ediacaran. Proceedings of the National Academy of Sciences, 112(16), 4865–4870. https://doi.org/10.1073/pnas.1403669112
Faisal, A. A., White, J. A., & Laughlin, S. B. (2005). Ion-Channel Noise Places Limits on the Miniaturization of the Brain’s Wiring. Current Biology, 15(12), 1143–1149. https://doi.org/10.1016/j.cub.2005.05.056
Feinberg, T. E., & Mallatt, J. M. (2016). The Ancient Origins of Consciousness: How the Brain Created Experience. Retrieved from https://books.google.co.nz/books?id=X-zxCwAAQBAJ
Howard, Dr. N. (2012). The Energy Paradox of the Brain [White Paper]. Nuffield Department of Surgical Sciences: University of Oxford Computational Neuroscience Lab.
Hoyle, G. (1975). Identified neurons and the future of neuroethology. Journal of Experimental Zoology, 194(1), 51–73. https://doi.org/10.1002/jez.1401940105
Ivantsov, A. Y., & Malakhovskaya, Y. E. (2002). Giant Traces of Vendian Animals. 385(6), 6.
Jorgensen, E. M. (2014). Animal Evolution: Looking for the First Nervous System. Current Biology, 24(14), R655–R658. https://doi.org/10.1016/j.cub.2014.06.036
Kristan, W. B. (2016). Early evolution of neurons. Current Biology, 26(20), R949–R954. https://doi.org/10.1016/j.cub.2016.05.030
Miller, D. J., & Ball, E. E. (2005). Animal Evolution: The Enigmatic Phylum Placozoa Revisited. Current Biology, 15(1), R26–R28. https://doi.org/10.1016/j.cub.2004.12.016
Moran, Y., Barzilai, M. G., Liebeskind, B. J., & Zakon, H. H. (2015). Evolution of voltage-gated ion channels at the emergence of Metazoa. Journal of Experimental Biology, 218(4), 515–525. https://doi.org/10.1242/jeb.110270
Rutger’s School of Engineering. (2012). Rutgers’ IBM Blue Gene P Computer [Photograph]. Retrieved from https://soe.rutgers.edu/story/rutgers’-ibm-blue-gene-p-computer-featured-online-article
Ryan, J. F., & Chiodin, M. (2015). Where is my mind? How sponges and placozoans may have lost neural cell types. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1684), 20150059. https://doi.org/10.1098/rstb.2015.0059
Sagasser, S. (2008). Trichoplax adhaerens Grell-BS-1999 v1.0 [Scanning Electron Microscope photograph]. Retrieved from https://mycocosm.jgi.doe.gov/Triad1/Triad1.home.html
Smith, C. L., Abdallah, S., Wong, Y. Y., Le, P., Harracksingh, A. N., Artinian, L., … Senatore, A. (2017). Evolutionary insights into T-type Ca 2+ channel structure, function, and ion selectivity from the Trichoplax adhaerens homologue. The Journal of General Physiology, 149(4), 483–510. https://doi.org/10.1085/jgp.201611683
Smith, C. L., Pivovarova, N., & Reese, T. S. (2015). Coordinated Feeding Behavior in Trichoplax, an Animal without Synapses. PLOS ONE, 10(9), e0136098. https://doi.org/10.1371/journal.pone.0136098
Sperling, E. A., & Vinther, J. (2010). A placozoan affinity for Dickinsonia and the evolution of late Proterozoic metazoan feeding modes: Placozoan affinity for Dickinsonia. Evolution & Development, 12(2), 201–209. https://doi.org/10.1111/j.1525-142X.2010.00404.x
Wang, I. E., & Clandinin, T. R. (2016). The Influence of Wiring Economy on Nervous System Evolution. Current Biology, 26(20), R1101–R1108. https://doi.org/10.1016/j.cub.2016.08.053
Yu, L., & Yu, Y. (2017). Energy-efficient neural information processing in individual neurons and neuronal networks: Energy Efficiency in Neural Systems. Journal of Neuroscience Research, 95(11), 2253–2266. https://doi.org/10.1002/jnr.24131
Yun, A. J., Lee, P. Y., Doux, J. D., & Conley, B. R. (2005). A general theory of evolution based on energy efficiency: Its implications for diseases. Medical Hypotheses, 66(3), 664–670. https://doi.org/10.1016/j.mehy.2005.07.002
Zimmermann, M. (1986). Neurophysiology of Sensory Systems. In R. F. Schmidt (Ed.), Fundamentals of Sensory Physiology (pp. 68–116). https://doi.org/10.1007/978-3-642-82598-9_3
