Elusive challenge: touch

Saturday 8th July 2017
"Touch" courtesy Google search

Pianist Victor Borge (1909-2000) was famed for the sensitivity of his keyboard touch. But that delicacy of touch is proving elusive in the evolution of robots. And it is proving very hard to design a robot with that level of durability, reliability, and sensitivity. Scientists are  having a hard time understanding the light touch, let alone imitating it.

Scientists are having a hard time understanding the light touch, let alone imitating it reports Evolution News Organisation Four researchers from the Universities of Chicago and Sheffield (UK) have made major progress over previous attempts to model the sense of touch. In a paper Proceedings of the National Academy of Sciences, “Simulating tactile signals from the whole hand with millisecond precision,” they declare of their new mathematical model of a single hand’s neural responses under a variety of fingertip-touch experiments, hoping to assist robotics engineers wishing to imitate human touch response.

Note the words code and information:
When we grasp an object, thousands of tactile nerve fibres become activated and inform us about its physical properties (eg shape, size, texture). Although the properties of individual fibres are described, our understanding of how object information is encoded in populations of fibres remains primitive. To fill this gap, we developed a simulation of tactile fibres that incorporates much of what is known about skin mechanics and tactile nerve fibres. We show that simulated fibres match biological ones across a wide range of conditions sampled from literature. We then show how this simulation can reveal previously unknown ways in which  populations of nerve fibres cooperate to convey sensory information discussing the implications for bionic hands. 

Unlike previous experiments that attempted to measure neural spikes from individual sensors in the skin of monkeys or humans, the new model simulates the responses of thousands of sensors, based on knowledge of their classifications and distributions in the skin of the human hand.

The team incorporated three classes of nerve fibres into the model:

  • Slowly adapting (SA) sensors: respond primarily to spatial information from the stimulus.
  • Rapidly adapting (RA) sensors: twice as densely packed as SA sensors, these provide a mix of spatial and vibration responses.
  • Pacinian sensors: less densely packed than the other types, these neurons are sensitive to vibrations and waves generated by movement across the skin.

Each of these fibres produces spike trains that encode different aspects of the stimulus, such as edges, compression, and vibration. One type alone might not convey much but put together, they give the brain a rich array of data.

Interpreted correctly, this information allows the brain to draw conclusions about size, shape, and texture of an object by touch alone. A blind person can thus “see” Braille letters with the fingertips where these neurons are most densely packed: “each fingertip contains just under 1,000 fibres,” the paper states, providing fine resolution, especially from the high-resolution SA1 fibres.

The spike trains become more complex as the fingertip is moved into or across the source, activating more of the RA and PC fibers. Simply pressing a key on a computer keyboard is a complex act, with surrounding neurons becoming involved as pressure is applied or released. Moving a finger across a surface sets up waves that propagate throughout the hand, activating more sensors along the length of the finger and into the palm.

This all happens within milliseconds, as it must when you consider the fast action of typing or playing a rapid piano piece. Even though PC fibres are less densely populated, their activity “dwarfs that of active SA1 or RA fibres,” say the authors, since they almost all become activated during a grasping operation or when feeling vibrations.

The authors describe efforts to “tune” or “fit” their model to known facts about neurons in the hand. They achieved a good match for things like edge detection, edge orientation, and direction of motion for simple actions eventually. But, they omitted important capabilities such as temperature or pain — two important inputs that can generate reflex actions that activate arm muscles to jerk the hand away before the brain is aware of danger. Their model completely overlooks sweat glands, blood vessels, immune cells, and the other equipment packed into a fingertip.

The new model reflects admirable progress in understanding the sense of touch. It will undoubtedly help engineers seeking to improve prosthetic devices and robotic capabilities, but there are a number of limitations to the model. For instance, they tuned their model to information from rhesus macaques, knowing that humans have an additional type of tactile sensor called the SA2 fibre. They fit their model to compression actions, but not to sliding actions. They didn’t take fingerprints into account.

The skin mechanics model treats the skin as a flat surface, when it is not. The 3D shape of the skin matters as during large deformations of the fingertip e.g. pressing the fingerpad on a flat surface causes the skin on the side of the fingertip to bulge out, and in turn, causes receptors located there to respond. Such complex mechanical effects can be replicated using finite element mechanical models, but not by using the CM (continuum mechanics) model. To the extent that friction is a critical feature of a stimulus — as when sliding a finger across a smooth, sticky surface — or finger geometries play a critical role in the interaction between skin and stimulus — as in the high-force loading already described — the accuracy is compromised.

Another limitation may be more significant. No  account was taken of the networking of responses in adjacent nerves. The model treats an affected area as an isotropic “hotspot” where all the fibres react the same way, but nerve fibres are known to branch out and affect neighboring fibres producing  complex interactions between neurons, and adding to the encoded tactile information the brain receives.

At a deeper cellular level a neuron embedded in the skin does not see anything. It “feels” the outer skin deforming slightly, because it contains mechano-sensitive portals in its membranes. Portals let some ions in, and others out, creating a wave train of signals down the cell’s length, the electrical “spike” the authors talk about. But it doesn’t just happen without each neural cell first being equipped with molecular machines able to respond to pressure, and able to quickly reset and re-fire as the source changes.

As the signals propagate toward the brain, neurons must cross synapses that convert the electrical signals to chemical signals and back, preserving the information and the timing of the signals as in the case of 3-D hearing. Once again, the simplest, ordinary action of touching a fingertip on a surface is vastly more complex than we could conceive, challenging scientists to come up with simplified models to understand it. With this in mind, try an experiment: with your eyes closed, touch your index finger to a variety of surfaces around you: a table top, clothing, bread, liquid, the skin of your arm, a puff of air from your lips. Try to discern by touch alone information about each object’s friction, temperature, smoothness, shape, and hardness.

Think of all those thousands of sensors providing that information to the brain with millisecond precision! Imagine what the brain has to deal with you when you plunge your whole body into a cold pool on a hot summer day. The authors do not mention evolution in their paper. Design is so abundantly obvious in the human body, that our best engineers cannot even conceive of approximating to that level of functional coherence, performance and integration!

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