MIIND is a software platform for easily and efficiently simulating the behaviour of interacting populations of point neurons governed by any 1D or 2D dynamical system. The simulator is entirely agnostic to the underlying neuron model of each population and provides an intuitive method for controlling the amount of noise which can significantly affect the overall behaviour. A network of populations can be set up quickly and easily using MIIND's XML-style simulation file format describing simulation parameters such as how populations interact, transmission delays, post-synaptic potentials, and what output to record. During simulation, a visual display of each population's state is provided for immediate feedback of the behaviour and population activity can be output to a file or passed to a Python script for further processing. The Python support also means that MIIND can be integrated into other software such as The Virtual Brain. MIIND's population density technique is a geometric and visual method for describing the activity of each neuron population which encourages a deep consideration of the dynamics of the neuron model and provides insight into how the behaviour of each population is affected by the behaviour of its neighbours in the network. For 1D neuron models, MIIND performs far better than direct simulation solutions for large populations. For 2D models, performance comparison is more nuanced but the population density approach still confers certain advantages over direct simulation. MIIND can be used to build neural systems that bridge the scales between an individual neuron model and a population network. This allows researchers to maintain a plausible path back from mesoscopic to microscopic scales while minimising the complexity of managing large numbers of interconnected neurons. In this paper, we introduce the MIIND system, its usage, and provide implementation details where appropriate.

The influence of proprioceptive feedback on muscle activity during isometric tasks is the subject of conflicting studies. We performed an isometric knee extension task experiment based on two common clinical tests for mobility and flexibility. The task was carried out at four pre-set angles of the knee and we recorded from five muscles for two different hip positions. We applied muscle synergy analysis using non-negative matrix factorisation on surface electromyograph recordings to identify patterns in the data which changed with internal knee angle, suggesting a link between proprioception and muscle activity. We hypothesised that such patterns 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 afferent input. The model produces the same synergy trends as observed in the data, driven by changes in the afferent input. In order to match the activation patterns from each knee angle and position of the experiment, the model predicts the need for three distinct inputs: two to control a non-linear bias towards the extensors and against the flexors, and a further input to control additional inhibition of rectus femoris. The results show that proprioception may be involved in modulating muscle synergies encoded in cortical or spinal neural circuits.

Proprioceptive feedback and its role in control of isometric tasks is often overlooked. In this study recordings were made from upper leg muscles during an isometric knee extension task. Internal knee angle was fixed and subjects were asked to voluntarily activate their rectus femoris muscle. Muscle synergy analysis of these recordings identified canonical temporal patterns in the data. These synergies were found to encode two separate features: one concerning the coordinated contraction of the recorded muscles and the other indicating agonistic/antagonistic interactions between these muscles. The second synergy changed with internal knee angle reflecting the influence of afferent activity. This is in contrast to previous studies of dynamic task experiments which have indicated that proprioception has a negligible effect on synergy expression. Using the MIIND neural simulation platform, we developed a spinal population model with an adjustable input representing proprioceptive feedback. The model is based on existing spinal population circuits used for dynamic tasks. When the same synergy analysis was performed on the output from the model, qualitatively similar muscle synergy patterns were observed. These results suggest proprioceptive feedback is integrated in the spinal cord to control isometric tasks via muscle synergies. Significance statementSensory feedback from muscles is a significant factor in normal motor control. It is often assumed that instantaneous muscle stretch does not influence experiments where limbs are held in a fixed position. Here, we identified patterns of muscle activity during such tasks showing that this assumption should be revisited. We also developed a computational model to propose a possible mechanism, based on a network of populations of neurons, that could explain this phenomenon. The model is based on well established neural circuits in the spinal cord and fits closely other models used to simulate more dynamic tasks like locomotion in vertebrates.

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