Movement, oscillations and emerging patterns

(Hot off the press: Today I had my Project Proposal resubmission approved. So, I have finally completed Part 1. I am now an official doctoral candidate – exciting and scary.)

I have got to ‘Movement’ in my textbook and whilst I am fascinated in how the body coordinates itself, I am definitely feeling uncomfortable reading about the animal experiments that have led to this knowledge. ‘Ablate’ is a fancy word for ‘destroying’ or ‘removing’ a function and I am frequently reading that word in this chapter. Also, cats are being used a lot which is unpleasant. My sons will laugh at this as I regularly moan about cats and chase them away from the birdfeeders in the garden.

On the movement side what is very interesting, if I understand it correctly, is that when your muscles contract, basically each cell within that muscle contracts. The basis of muscle contraction is about overlapping thin F-actin and thicker Myosin protein filaments, which are laid out in this pattern:

____________                ______________  F-actin

                   ̻̻  ̻̻  ̻̻__________  ̻̻  ̻̻  ̻̻                                      Myosin

____________                ______________

                   ̻̻  ̻̻  ̻̻__________  ̻̻  ̻̻  ̻̻

____________                ______________

 

The thicker Myosin filaments have little bundles at each end. These have an extended ‘finger’ which, when excited, ‘ratchet’ the overlapping layers inwards towards each other, bit by bit, thus contracting the cell.

I have also managed to read “The Oscillating Brain” by Timothy Sheehan which is well written and was an easy read overviewing brain oscillations. This topic seems to be causing excitement in the neuroscience world so I thought I’d better get a heads up on it. It also covers a good overview of complex systems theory, which is easier to conceptually understand than I thought.

The oscillations, seem to be the pattern caused by all the neurons that are firing. A bit like a Mexican wave at a sports stadium which can vary the speed with which it goes around and around. Although formed of individual people there is a ripple effect that can be seen from afar rather than the individual people.

There seems to be a regular resting baseline of Alpha oscillations (8-12Hz) which increase to Beta oscillations (13-30Hz) when we get sensory inputs. Once consciousness kicks in we get Gamma Oscillations at 30-70Hz which means that the neurons are firing more rapidly. I read another technical paper on neural oscillations that talks about how the oscillation networks coupled with the electrical circuit characteristics of neurons, enable filters to be created which amplify certain aspects and suppress others. (More third year electrical engineering degree material) One example it gives is how the cortical and thalamus oscillations progressively decrease neural responsiveness to external factors thus creating deeper sleep. Inhibitory neurons feature a lot.

In the brain, there appear to be a lot of inhibitory neurons which help to sharpen the difference between what’s on and off. (Used a lot in vision). Therefore, once one set of neurons are excited they inhibit other neurons so it is even less likely they will fire (it takes a bigger input signal to activate the inhibited neurons) thus sharpening the definition between them. A good understanding of decision maths or logic circuits is useful as the brain seems to use these a lot.

I must admit I had been putting off reading about Complex Systems Theory as it sounded a bit heavy going. Also, I feel that there are two phrases within complex system theory that people seem to love throwing around: complex system and emergent properties. The good news is that complex system theory as overviewed by Timothy Sheehan is easier to follow than I thought.

The ‘complex’ part refers to the system rather than the theory (I am sure that will follow) and in essence the theory attempts to make complex systems simpler. A good example of a complex system is the weather. There is a lot going on and there are a lot of things that affect it such that you cannot really predict the weather patterns off just one variable. Also, the patterns of interaction between the variables are hard to predict in a precise manner. That definitely sounds like the weather forecast.

The term emergent property seems to be about the most predominant trend(s) or pattern(s). That is, if you overlay all the individual aspects what is the most predominate trend(s) that appears? An example of this is if you take Euston Railway Station concourse: During the day people seemingly walk across all parts of it but if you superimposed all their paths you’d probably find that most people use just 4 or 5 main routes through it. Across the day these trends might vary (rush hour verses off-peak) but still stay within a limited number of options. I think a ‘Wordle’ is another example as it pictorially simplifies the information about how often a word is used and makes it easy to see the trends.

A complex system also has some themes. Firstly, repetitive activity underlies the stability of the system along with feedback loops. Also, it follows a ‘S’ shaped curve. They mainly operate on the more vertical part of the ‘S’ which gives them their steady-states. For example, our weather is predominately a temperate climate with our winters generally colder than our summers. However sometimes the system can move towards either end of the curve. At one end, it becomes ‘weak’ and easily affected by other factors more than it usually would be. At the other end, the system can become overloaded and break down, giving rise to unexpected behaviour. Thus, sometimes complex systems do things which are very unlike them.

This also characterises people. Over time, we form assumptions of people’s behaviour but in times of stress they do ‘out of character’ things, like becoming unusually forceful, rigid or argumentative. When they are feeling ‘fragile’ they seem more easily overcome by daily occurrences. So, a complex system is simply something which has lots of variables and is not easy to predict precisely, although it has a steady state trend(s) and occasionally does something unexpected. Like our weather or us. Although I am not sure if this helps us understand people or is just another analogy.