de Kamps, M. | Search bioRxiv

bioRxiv2019

Muscles Recruited During an Isometric Knee Extension Task is Defined by Proprioceptive Feedback

The influence of proprioceptive feedback on muscle activity during isometric tasks is the subject of conflicting studies. To better understand the relationship, we performed an isometric knee extension task experiment at four pre-set angles of the knee, recording from five muscles, and for two different hip positions. We applied muscle synergy analysis using NMF on the sEMG recordings to identify structure in the data which changed with internal knee angle, suggesting a link between proprioception and muscle activity. We hypothesised that such patterns in the data arise from the way proprioceptive and cortical signals are integrated in neural circuits of the spinal cord. Using the MIIND neural simulation platform, we developed a computational model based on current understanding of spinal circuits with an adjustable proprioceptive input. The model produces the same synergy patterns as observed in the experimental data indicating that such synergies are indeed encoded in the neural connectivity of the spinal cord and modulated by the proprioceptive input. When matching the proprioceptive input to the knee angles of the experiment, the model predicts the need for three distinct inputs: two to control the normal reciprocity between the agonist and antagonist muscles, and an additional to match the non-linear trend towards the limit of extension. Finally, we discuss the risks of using NMF for synergy analysis and demonstrate how to increase confidence in its results. Future modelling of human motor outputs should include interneuronal spinal circuits such as this to distinguish the modulatory role of supraspinal and peripheral afferent inputs to the spinal cord, during both passive and dynamic tasks. Significance statementSensory feedback from muscles has a significant role in motor control, but its role in tasks where limbs are held in a fixed position is disputed, because the effect is reduced when muscles are not stretched. Here, we first identified patterns of muscle activity during such tasks which changed with different leg positions. We developed a computational spinal motor circuit model with adjustable muscle stretch input, which reproduced the same patterns of activity as observed experimentally. The model predicts three distinct muscle stretch signals required to produce the activity patterns for all leg positions. Because the connections in the model are based on well known spinal circuits, it is likely the observed activity patterns are generated in the spinal cord.

neuroscience↗

bioRxiv2018

Computational Geometry for Modeling Neural Populations: from Visualization to Simulation

The importance of a mesoscopic description level of the brain has now been well established. Rate based models are widely used, but have limitations. Recently, several extremely efficient population-level methods have been proposed that go beyond the characterization of a population in terms of a single variable. Here, we present a method for simulating neural populations based on two dimensional (2D) point spiking neuron models that defines the state of the population in terms of a density function over the neural state space. Our method differs in that we do not make the diffusion approximation, nor do we reduce the state space to a single dimension (1D). We do not hard code the neural model, but read in a grid describing its state space in the relevant simulation region. Novel models can be studied without even recompiling the code. The method is highly modular: variations of the deterministic neural dynamics and the stochastic process can be investigated independently. Currently, there is a trend to reduce complex high dimensional neuron models to 2D ones as they offer a rich dynamical repertoire that is not available in 1D, such as limit cycles. We will demonstrate that our method is ideally suited to investigate noise in such systems, replicating results obtained in the diffusion limit and generalizing them to a regime of large jumps. The joint probability density function is much more informative than 1D marginals, and we will argue that the study of 2D systems subject to noise is important complementary to 1D systems.\n\nAuthor SummaryA group of slow, noisy and unreliable cells collectively implement our mental faculties, and how they do this is still one of the big scientific questions of our time. Mechanistic explanations of our cognitive skills, be it locomotion, object handling, language comprehension or thinking in general - whatever that may be - is still far off. A few years ago the following question was posed: Imagine that aliens would provide us with a brain-sized clump of matter, with complete freedom to sculpt realistic neuronal networks with arbitrary precision. Would we be able to build a brain? The answer appears to be no, because this technology is actually materializing, not in the form of an alien kick-start, but through steady progress in computing power, simulation methods and the emergence of databases on connectivity, neural cell types, complete with gene expression, etc. A number of groups have created brain-scale simulations, others like the Blue Brain project may not have simulated a full brain, but they included almost every single detail known about the neurons they modelled. And yet, we do not know how we reach for a glass of milk.\n\nMechanistic, large-scale models require simulations that bridge multiple scales. Here we present a method that allows the study of two dimensional dynamical systems subject to noise, with very little restrictions on the dynamical system or the nature of the noise process. Given that high dimensional realistic models of neurons have been reduced successfully to two dimensional dynamical systems, while retaining all essential dynamical features, we expect that this method will contribute to our understanding of the dynamics of larger brain networks without requiring the level of detail that make brute force large-scale simulations so unwieldy.

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