Decoding the Cortical Transformations for Visually Guided Reaching in 3D Space

TLDR

The study trains a 4‑layer feed‑forward neural network to model the 3‑D visuomotor transformation for reaching from visual hand and target positions across varying eye and head orientations. The authors trained the network and performed reference‑frame analyses of each unit by simulating RF mapping, motor field mapping, and microstimulation to probe input–output relationships. The network’s intermediate layers display gain‑field–like modulations and position‑dependent receptive‑field shifts, and unit‑level reference‑frame analyses show that different electrophysiological methods reveal distinct properties, yet fixed input–output relationships within each layer and unit support local transformation modules that combine in a gain‑field–like fashion to implement the global 3‑D visuomotor transformation.

Abstract

To explore the possible cortical mechanisms underlying the 3-dimensional (3D) visuomotor transformation for reaching, we trained a 4-layer feed-forward artificial neural network to compute a reach vector (output) from the visual positions of both the hand and target viewed from different eye and head orientations (inputs). The emergent properties of the intermediate layers reflected several known neurophysiological findings, for example, gain field–like modulations and position-dependent shifting of receptive fields (RFs). We performed a reference frame analysis for each individual network unit, simulating standard electrophysiological experiments, that is, RF mapping (unit input), motor field mapping, and microstimulation effects (unit outputs). At the level of individual units (in both intermediate layers), the 3 different electrophysiological approaches identified different reference frames, demonstrating that these techniques reveal different neuronal properties and suggesting that a comparison across these techniques is required to understand the neural code of physiological networks. This analysis showed fixed input–output relationships within each layer and, more importantly, within each unit. These local reference frame transformation modules provide the basic elements for the global transformation; their parallel contributions are combined in a gain field–like fashion at the population level to implement both the linear and nonlinear elements of the 3D visuomotor transformation.