Abstract
Proprioception is one of the least understood senses, yet fundamental for the control of movement. Even basic questions of how limb pose is represented in the somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. Although not fitted to neural data, our model reproduces two sets of observations from monkey centre-out reaching: 1. The shape and velocity dependence of proprioceptive receptive fields in hand-centered coordinates despite the model having no knowledge of arm kinematics or hand coordinate systems. 2. The distribution of neuronal preferred directions (PDs) recorded from multi-electrode arrays. The model makes several testable predictions: 1. Encoding across the cortex has a blob-and-pinwheel-type geometry of PDs. 2. Few neurons will encode just a single joint. Our model provides a principled basis for understanding of sensorimotor representations, and the theoretical basis of neural manifolds, with applications to the restoration of sensory feedback in brain-computer interfaces and the control of humanoid robots.
Author Summary It is well established that proprioception is essential for effective for motor control, yet our understanding of proprioceptive coding in somatosensory cortex is far behind that of more established sensory modalities such as vision and touch. Here, we use unsupervised learning of a deep neural network imposed with biological constraints to reproduce coding properties of proprioceptive neurons in area 2 of primary somatosensory cortex. With this model, we demonstrate that the tendency for area 2 neurons in close physical proximity to share similar directional tuning can be explained by local topographic organisation driven by Mexican hat lateral effects, a phenomenon that is otherwise unobservable with the spatial resolution of available electrode microarray recordings. This provides, to the best of our knowledge, the first evidence of local topographic organisation in area 2 proprioceptive neurons. We also predict that the structure of proprioceptive receptive fields are typically multi-joint rather than single-joint in nature and provide, to the best of our knowledge, and find that our model best reproduces the tuning properties of neural data when training on natural kinematic recordings, rather than those from a stereotyped task, underlining the importance of training on data that reflect the natural distribution of sensory stimuli.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The work evolved further requiring to update the main text and the results. This include improvements to figure legends to improve clarity of number of model seeds tested, and model architecture. Further addition of supplemental figures outlining controls for spatial organisation constrains in latent space, hyper parameter tuning and sensitivity of model results to hyperparameters. Update to referencing style. Change of authorship order to account of ongoing effort by Max D Grogan on the manuscript as agreed by all authors.
