Author: sssaneurorobotics

Sensory models for the simulated mouse in the NRP

A biologically inspired translation model for proprioceptive sensory information was developed. The translation is achieved implementing a computational model of neural activity of type Ia and type II sensory fibers connected to muscle spindles. The model also includes activity of both static and dynamic gamma-motoneurons, that provide fusimotor activation capable of regulating the sensitivity of the proprioceptive feedback, through the contraction of specific intrafusal fibers (Proske, 19971).

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Figure 1 Intrafusal fibers

The proposed model is an extension of a state-of-the art computational models of muscle spindle activity (Mileusnic, 20062). The model developed by Mileusnic and colleagues, albeit complete and validated against neuroscientific data, was completely rate based, thus it was modified in order to be integrated in a spiking neural network simulation. In particular, a spike integration technique was employed to compute fusimotor activation and the generated rate was used to generate spike trains.

The proprioceptive model is implemented on NEST, in order to provide an easy integration inside the NRP, and on SpiNNaker, for supporting real-time robotic applications. The proposed component can be coupled to both biomechanical models, like musculo-skeletal systems, and common robotic platforms (via suitable conversions from encoder values to simulated muscle length). In particular, this model will be used, as part of CDP1, to provide sensory feedback from the virtual mouse body.

1 Proske, U. (1997). The mammalian muscle spindle. Physiology, 12(1), 37-42.

2 Mileusnic, M. P., Brown, I. E., Lan, N., & Loeb, G. E. (2006). Mathematical models of proprioceptors. I. Control and transduction in the muscle spindle. Journal of neurophysiology, 96(4), 1772-1788.

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The virtual M-Platform

Preclinical animal studies can offer a significant contribution to gain knowledge about brain function and neuroplastic mechanisms (i.e. the structural and functional changes of the neurons following inner or external stimuli). For example, an external stimulus as a cortical infarct (i.e. stroke) can produce a cascade of similar neural changes both in a human and animal (i.e. monkeys, rodents etc) brains. And even further stimuli such as input provided during a rehabilitative training can have this impact. The possibility to exploiting the neural plasticity, addressing the treatments in combination with technological advanced methods (e.g. robot-based therapy) is one goal that the HBP is pursuing.

The Neurorobotics Platform is fully part of this picture and is providing an environment that will be an important benchmark for these studies. Two labs from the Scuola Superiore Sant’Anna, in Pisa, are tightly working to develop a virtual model of a experiment carried on in a real neuroscientific environment. The core of this set up is the M-Platform (Spalletti and Lai et al. 2013), a device able to train mice to perform a retraction-pulling task with their forelimb (Figure 1A). During last months, the device has been characterized and upgraded to improve its repeatability (Figure 1B). Meanwhile, a first example of the virtual M-Platform (Figure 1C) has been developed.

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Figure: The real M-Platform (A); the CAD design of the main component of the M-Platform, i.e. actuation and sensing, (B) and its virtual model in the NRP (C)

The  main components of the M-Platform (i.e. linear actuator, linear slide, handle) have been converted in a suitable format for the Gazebo simulator. Properties of the model such as link weights, joint limits and frictions have been adjusted according to the real characteristics of the slide. The actuator was connected to a PID controller whose parameters have been tuned to reproduce the behavior of the real motor.

A simple experiment has thus been designed in the NRP (currently installed on a local machine), for testing the behavior of the obtained model. The experiment includes a 100 neurons brain model, divided in two populations of 90 and 10 neurons respectively. In this closed loop experiment, the first neuron population spikes randomly, and the spike rate of the population is converted to a force value picked out of a predefined range, compatible with the range of forces possibly performable by the mouse through its forelimb.

The computed force values are continuously applied to the handle and can move the slide until the starting position. Once there, the second neural population, wired to suppress the first population spike rate when active, is triggered, so there’s no more force acting on the slide. The motor pushes the slide until the maximum extension position and it then comes back to its starting position, letting the loop start again (see video).