Task-driven neural network models predict neural dynamics of proprioception

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: June 16, 2023

Proprioception tells the brain state of body based on distributed sensors in body. However, principles that govern proprioceptive processing from those are poorly understood. Here, we employ a task-driven neural network modeling approach to investigate code neurons both cuneate nucleus (CN) and somatosensory cortex area 2 (S1). We simulated muscle spindle signals through musculoskeletal generated large-scale, naturalistic movement repertoire train thousands models 16 behavioral tasks, each reflecting hypothesis about computations ascending pathway. found network’s internal representations developed task-optimization generalize synthetic data predict single-trial activity CN S1 primates performing center-out reaching. Task-driven outperform linear encoding data-driven models. Behavioral which aim limb position velocity were best areas. Architectures better at solving tasks also predicting data. Last, since develops during active but not passively movements, hypothesize is top-down modulated goal-directed movements.

Language: Английский

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Modeling Sensorimotor Processing with Physics-Informed Neural Networks

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 15, 2024

Proprioception is essential for planning and executing precise movements. Muscle spindles, the key mechanoreceptors proprioception, are principle sensory neurons enabling this process. Emerging evidence suggests spindles act as adaptable processors, modulated by gamma motor to meet task demands. Yet, specifics of modulation remain unknown. Here, we present a novel, physics-informed neural network model that integrates biomechanics dynamics capture spindle function with high fidelity efficiency, while maintaining computational tractability. Through validation across multiple experimental datasets species, our not only outperforms existing approaches but also reveals drivers variability in responses, offering new insights into proprioceptive mechanisms.

Language: Английский

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Edge Computing in Nature: Minimal pre-processing of multi-muscle ensembles of spindle signals improves discriminability of limb movements

Frontiers in Physiology, Journal Year: 2023, Volume and Issue: 14

Published: June 29, 2023

Multiple proprioceptive signals, like those from muscle spindles, are thought to enable robust estimates of body configuration. Yet, it remains unknown whether spindle signals suffice discriminate limb movements. Here, a simulated 4-musculotendon, 2-joint planar model produced repeated cycles five end-point trajectories in forward and reverse directions, which generated Ia II afferent (proprioceptors for velocity length, respectively) each musculotendon. We find that cross-correlation the 8D time series raw firing rates (four Ia, four II) cannot among most movement pairs (∼ 29% accuracy). However, projecting these onto their 1

Language: Английский

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An alpha- to gamma-motoneurone collateral can mitigate velocity-dependent stretch reflexes during voluntary movement: A computational study

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 10, 2023

The primary motor cortex does not uniquely or directly produce alpha motoneurone (

Language: Английский

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Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(12), P. e1012614 - e1012614

Published: Dec. 4, 2024

Proprioception is one of the least understood senses, yet fundamental for control movement. Even basic questions how limb pose represented in somatosensory cortex are unclear. We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute putative cortical map from large set natural movement data. Although not fitted neural data, our model reproduces two sets observations monkey centre-out reaching: 1. The shape and velocity dependence proprioceptive receptive fields hand-centered coordinates despite having no knowledge arm kinematics or hand coordinate systems. 2. distribution neuronal preferred directions (PDs) recorded multi-electrode arrays. makes several testable predictions: Encoding across has blob-and-pinwheel-type geometry PDs. Few neurons will encode just single joint. Our provides principled basis understanding sensorimotor representations, theoretical manifolds, applications restoration sensory feedback brain-computer interfaces humanoid robots.

Language: Английский

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The mechanism at hand

Published: Dec. 23, 2024

Chapter 1 2009) go hand in with its manual capabilities.In part, we may observe this the large proportion of cortical homunculus dedicated to hands (Catani, 2017;Penfield & Boldrey, 1937).In light pivotal role body and specifically play human cognition, present thesis aims push boundaries sensorimotor neuroscience by modeling dexterity.Specifically, a total three empirical chapters, will assembly tools (Chapter 2), creation process 3), analysis 4) an ambitious top-down model that spans regions involved dexterity.We show presented can generate interesting hypotheses about neurocomputational principles are firmly grounded functional structural validity.The following introduction motivate our approach two philosophies mind: embodied enactive cognition.These reject view mind entirely discrete entities, perspective rooted Cartesian dualism (Descartes, 1985;Skirry, 2005;Thibaut, 2018) is still popular cognitive science today (Gallagher, 2023).They also computationalism, which oppose nonphysical mind, but locates cognition nervous system, where it merely implemented, not driven physicality (Shapiro, 2007;Shapiro Spaulding, 2021).In contrast both, modern philosophy spearheaded approach, rejects any type dichotomy considers brain, rest constitute as

Language: Английский

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