Feldheim, J. | bioRxiv Science

bioRxiv2023

Ventral premotor cortex influences spinal cord activation during force generation

Force generation is a crucial element of dexterity and a highly relevant skill of the human motor system. How cerebral and spinal components interact and how spinal activation is influenced by cerebral primary motor and premotor areas is poorly understood. Here we conducted combined cortico-spinal functional MRI during a simple visually guided isometric force generation task in a group of 20 healthy young subjects. Activation was localized in the ipsilateral cervical spinal cord and contralateral primary motor and premotor areas. The main finding is that spinal activation was influenced by ventral premotor cortex activation. Spinal activation was furthermore significantly correlated with primary motor cortex activation while increasing target forces led to an increase in the amount of activation. These data indicate that human premotor areas such as the ventral premotor cortex might be functionally connected to the lower cervical spinal cord contributing to distal upper limb functions, a finding which extends our understanding about human motor function beyond the animal literature.

neuroscience↗

bioRxiv2019

Crossmodal Pattern Discrimination in Humans and Robots: A Visuo-Tactile Case Study

The quality of crossmodal perception hinges on two factors: The accuracy of the independent unimodal perception and the ability to integrate information from different sensory systems. In humans, the ability for cognitively demanding crossmodal perception diminishes from young to old age.\n\nTo research to which degree impediments of these two abilities contribute to the age-related decline and to evaluate how this might apply to artificial systems, we replicate a medical study on visuo-tactile crossmodal pattern discrimination utilizing state-of-the-art tactile sensing technology and artificial neural networks. We explore the perception of each modality in isolation as well as the crossmodal integration.\n\nWe show that in an artificial system the integration of complex high-level unimodal features outperforms the comparison of independent unimodal classifications at low stimulus intensities where errors frequently occur. In comparison to humans, the artificial system outperforms older participants in the unimodal as well as the crossmodal condition. However, compared to younger participants, the artificial system performs worse at low stimulus intensities. Younger participants seem to employ more efficient crossmodal integration mechanisms than modelled in the proposed artificial neural networks.\n\nOur work creates a bridge between neurological research and embodied artificial neurocognitive systems and demonstrates how collaborative research might help to derive hypotheses from the allied field. Our results indicate that empirically-derived neurocognitive models can inform the design of future neurocomputational architectures. For crossmodal processing, sensory integration on lower hierarchical levels, as suggested for efficient processing in the human brain, seems to improve the performance of artificial neural networks.

neuroscience↗