A neural surveyor to map touch on the body

Area of Science:

• Neuroscience
• Computational Neuroscience
• Sensory Processing

Background:

• The somatosensory homunculus is a key neural map, but the computation linking neural activation to touch location remains unclear.
• Decoding somatotopic map activation requires a neural decoder to resolve spatial ambiguity.
• The neural mechanisms for tactile localization are not fully understood.

Purpose of the Study:

• To propose and investigate multilateration as a computational mechanism for tactile localization in the somatosensory system.
• To determine if neural networks can implement multilateration for decoding touch location.
• To identify computational signatures of multilateration in psychophysical data.

Main Methods:

• Developed a feedforward neural network model capturing somatosensory neuron properties.
• Simulated multilateration computation within the neural network model.
• Compared simulation results with human psychophysical experimental data.

Main Results:

• The neural network successfully implemented a Bayes-optimal multilateration computation.
• Simulations revealed a unique pattern of localization variability predicted by multilateration.
• This computational signature was identified in human psychophysical experiments.

Conclusions:

• Multilateration is a plausible computational mechanism for tactile localization.
• The somatosensory system may use distance estimation to body part boundaries for decoding touch.
• This study provides a candidate neural computation underlying how we perceive touch location.