Neuromimetic sensorimotor processing

ESR5

Objectives

The tactile features that generate the space of tactile inputs correspond to specific contact conditions governed by contact mechanics encoded by the brain. At a fundamental level these features correspond to mathematical invariants which are hypothesized to be learned by the neural circuits of the brain, and for which we recently provided experimental evidence. In this project, we will use a (artificial) deep neural network to learn these invariants from neural recordings and data obtained from stimulating artificial skin.

Expected Results

Identification of neural coding invariants of haptic sensing.

Planned secondments

ICL: to learn about predictive coding

ULUND: to investigate deep neural network to learn invariants from neural recordings

Placement

Host institution: Actronika

Enrolments (in Doctoral degree): Lunds Universitet

Supervisors

Vincent Hayward, Etienne Burdet