Walking, it is complicated
One might think it is so obvious how we walk. Some might take it so for granted. However, biology of walking is far from being simple. Perfected by evolution, walking involves a super complex interplay between so many brain structures many of whose role is not even partly clear.
Let me take a diversion before I come back to saying how well I know that I don’t know the physiology of walking. It is easy to make a simple pendulum passively walk over an inclined plane. Many have developed simple passive walkers that do that. They have simply attached two sticks, set it up at some reasonably good initial conditions and let it walk over a plane. And it start walking. However, it turns out eventually the system runs out of energy and stops. Ah! So now all we need is to supply some energy to this system so that it doesn’t eventually fall down. Now back to human walking. In our body, ankles supply energy at every step pushing us the uphill while trekking. But the ankles need to know when to give the forces when not isn’t it? Also, what to do when there is an obstacle ahead. This is when things get interesting. The key word is coordinated though because when one ankle supplies the energy the other has to remain silent. The coordinated.. alternating.. oscillatory..inputs.
We have a set of neurons in the spinal cord known as central pattern generators. As the name suggest it provide a pattern alternating…oscillatory…of inputs to the muscles in the hope of controlling them. This is where the rivalry begins.. On, one side we have the mechanical system with ever so complicated input patterns under the influence of bumpy roads and obstacles and the other side we have neurons trying to control it to generate very predictable movement patterns.
Nature has a very weird way of dealing with these problems. Evolution. From simple life forms with rather simpler locomotion patterns to complicated ones like us design iteratively, but with one catch. Nature may not know clearly what to let go and what to keep when it comes to design. While Samsung galaxy Note 20 ultra let go of the older say 2x zoom optical sensor with the 5x zoom one for pro video modes, nature might note be able to that. It probably will keep both lenses or probably try to improve older one iteratively.
This iterative design results in a lot of components in the brain which supply to the pattern generators which was previously designed for the likes of swimming and crawling.. modified to accommodate quadrupeds … which again remodelled to accommodate bipeds. So we might never know exactly what is the minimum number of structures that can result in normal walking.
CPGs.. the pattern generators described earlier is supplied with the inputs from the midbrain reticular formation (MRF) and locus coeruleus (LC) (i told you). It turns out MRF is divided into NRGc and NRMc providing inhibitory input and the locomotor inputs to the CPGs respectively. PRF controlled by PPN supplies NRGc to provide that inhibitory input probably trying to suppress the muscle tone. MLR region kinda controls LC and NRMc in an excitatory way i think but don’t take my word for it. So now we have a crude excitatory and inhibitoryish systems controlling the CPG. This doesn’t end here.. These structures in turn regulated by the basal ganglia which has many substructures like STN, GPi, SNc etc. It is neurons of SNc which die in Parkinson’s disease.
Obviously, feedback plays a lot of role in walking. The primary afferents of the of the CPG supply them directly while sensory input also go to the Cerbellum and motor cortex (visual feedback)for further procrssing. Cerebellum is speculated to have developed a system to compute the inverse model and supply the information necessary to generate the correct torques to MRF. Amygdala, hippocampus, SLR, VTA, NAc supply some emotional component to it. People with anxiety causes freezing in Parkinson’s. And here goes one plausible explanation. But the exact workings are obviously unclear.
The point is the rest is pretty complicated.
More to follow as I understand the last part better.
Posts
Policy Gradient Algorithm
Visit to Weston Park Sheffield
Notes on Inverse transform sampling
Eigenvalues and poles
Back Prop Algorithm - What remains constant in derivatives
Wordpress to Jekyll Conversion
Phase functions
Solving Dynamical Systems in Javascript
Javascript on markdown file
Walking data
Walking, it is complicated
PRC
Isochrone
Walking, it's complicated
Newtons iteration as a map - Part 2
Newton's iteration as map - Part 1
ChooseRight
Mathematica for machine learning - Learning a map
Prediction and Detection, A Note
Why we walk ?
The equations that fall in love!
Oru cbi diarykkuripp(ഒരു സിബിഐ ഡയറിക്കുറിപ്പ്)
A way to detect your stress levels!!
In search of the cause in motor control
Compressive sensing - the most magical of signal processing.
Machine Learning using python in 5 lines
Can we measure blood pressure from radial artery pulse?
subscribe via RSS