The researchers at the Prakash Lab at Stanford University have been studying the mysterious multicellular organism, Trichoplax, for the past seven years. Trichoplax, a species of placozoa, is the simplest known animal and lacks a nervous system, making it a living fossil from a critical time in life's evolution. Despite its simplicity, Trichoplax exhibits complex behaviors, such as movement, food finding, and reproduction.
The researchers discovered that Trichoplax's cells, called cilia, exhibit dynamics similar to those seen in individual neurons. When stimulated, a neuron executes a spike that passes information through the network. Similarly, in Trichoplax, a cilia cell transitions from a stall state to a walking state when it's pulled in the right direction.
The researchers also found that the cilia cells in Trichoplax synchronize their movement like a flock of birds, a phenomenon they described as "flocking dynamics". This coordinated activity is similar to a group of caster wheels attached by springs, where a force applied to the system will eventually reorient each individual wheel so that they all point in the same direction.
The researchers concluded that basic mechanics could give rise to the complex behavior we normally associate with brains. The observation of Trichoplax's ciliary flocking suggests that mechanical systems could be intelligent, capable of seeking out food, coming to consensus across many cells, and keeping an organism alive as a whole instead of just its individual parts. This discovery may have set the stage for the rise of animals with nervous systems by embodying simple mechanics that work together to produce complexity.
1. Researchers at the Prakash Lab at Stanford University have been studying trichoplax, a tiny and mysterious multicellular organism, for the past seven years.
2. Trichoplax is a species of placozoa, the simplest animals at the base of the tree of life.
3. Trichoplax does not have a nervous system, making it a living fossil from a critical time in life's evolution.
4. When researchers observe trichoplax under a microscope, they are shocked to see its millions of cells engaged in complex activity.
5. Trichoplax is able to coordinate its movement, strategize how to find food, and even reproduce by ripping itself in two.
6. The question arises: how can trichoplax exhibit these complex behaviors without a brain to call the shots?
7. The answer to this question could shed light on the origins of the nervous system.
8. Traditionally, when you think about intelligence, you think of the brain. Trichoplax stretches this intuition.
9. Researchers are trying to understand the simple mechanics of the trichoplax, starting with the tiny waving filaments called cilia.
10. The Prakash Lab discovered that trichoplax does not swim; it actually walks.
11. The researchers designed a mathematical model to measure the orientation, height, and beat frequency of each individual psyllium as it balanced torque from its neighboring cells with the force of its attachment to the surface below.
12. The result was a regular pattern of locomotion that the researchers called walking.
13. As the cilia walked, they exhibited dynamics similar to those seen in individual neurons when you stimulate the neuron with a voltage.
14. The cilia cells coordinated their movement in a way that was surprising and surreal, like a flock of birds.
15. The researchers found that individual cells could self-assemble into collective systems capable of a wide range of behaviors, including some that allowed them to be responsive to their environment.
16. The observation of trichoplax's ciliary flocking brought to the fore the idea that mechanical systems could somehow be intelligent.
17. Trichoplax may have set the stage for the rise of animals with nervous systems by taking an important first step: embodying simple mechanics that work together to produce complexity.
18. One of the implications of the research is that it has pulled us in new directions as we think about computation and intelligence in physical matter.
19. The researchers are now interested in making soft robotic self-oscillators, caster robots that come to consensus and work together, and predictive machines.