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.
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).