Understanding how our nervous system is designed may not only allows us to examine ourselves, but provide 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).
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).
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