Proprioceptive Feedback through a Neuromorphic Muscle Spindle Model.

Lorenzo Vannucci,Egidio Falotico,Cecilia Laschi

doi:10.3389/fnins.2017.00341

Lorenzo Vannucci, Egidio Falotico + Show 1 more

Open Access

https://doi.org/10.3389/fnins.2017.00341

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Abstract

Connecting biologically inspired neural simulations to physical or simulated embodiments can be useful both in robotics, for the development of a new kind of bio-inspired controllers, and in neuroscience, to test detailed brain models in complete action-perception loops. The aim of this work is to develop a fully spike-based, biologically inspired mechanism for the translation of proprioceptive feedback. The translation is achieved by implementing a computational model of neural activity of type Ia and type II afferent fibers of muscle spindles, the primary source of proprioceptive information, which, in mammals is regulated through fusimotor activation and provides necessary adjustments during voluntary muscle contractions. As such, both static and dynamic γ-motoneurons activities are taken into account in the proposed model. Information from the actual proprioceptive sensors (i.e., motor encoders) is then used to simulate the spindle contraction and relaxation, and therefore drive the neural activity. To assess the feasibility of this approach, the model is implemented on the NEST spiking neural network simulator and on the SpiNNaker neuromorphic hardware platform and tested on simulated and physical robotic platforms. The results demonstrate that the model can be used in both simulated and real-time robotic applications to translate encoder values into a biologically plausible neural activity. Thus, this model provides a completely spike-based building block, suitable for neuromorphic platforms, that will enable the development of sensory-motor closed loops which could include neural simulations of areas of the central nervous system or of low-level reflexes.

Highlights

In recent years, the development of action-perception closed loops that include biologically inspired neural network has risen at a rapid pace (Knoll and Gewaltig, 2016)
We presented a spike-based proprioceptive feedback transmission mechanism able to produce biologically realistic firing activity, as produced by muscle spindles, that can be fully integrated in spiking neural network simulation and on neuromorphic hardware
The mechanism, which emulates the dynamics of muscle spindles under stretch and γ-motoneurons activation, was implemented on two different simulators: NEST, a commonly employed spiking neural network simulator, and SpiNNaker, a neuromorphic hardware platform

Summary

Introduction

The development of action-perception closed loops that include biologically inspired neural network has risen at a rapid pace (Knoll and Gewaltig, 2016). On the neuroscientific side, such loops could provide the necessary input/output connections, in a rich environment, for detailed, full-scale neural simulations that model a specific part of the nervous system, such as the cerebral cortex (Potjans and Diesmann, 2014). To close these loops, it is crucial to find ways to connect physical or simulated embodiments (i.e., robots or musculoskeletal systems) to these networks that mimic neural behaviors.

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