Cerebellar Spiking Model For Real-time Closed-loop Sensorimotor Control

Project Information

SpikeControl

Grant agreement ID: 658479

DOI

10.3030/658479

Project closed

EC signature date 31 March 2015

Start date 1 September 2015

End date 31 August 2017

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 173 076,00

EU contribution

€ 173 076,00

Coordinated by

SORBONNE UNIVERSITE
France

Periodic Reporting for period 1 - SpikeControl (Cerebellar Spiking Model For Real-time Closed-loop Sensorimotor Control)

Reporting period: 2015-09-01 to 2017-08-31

Summary of the context and overall objectives of the project

Vestibular control coordination of eye movements is mostly mediated by the cerebellum, being the vestibular ocular reflex (VOR) the most studied reflex of them all. VOR produces rapid contralateral eye movements that stabilise the image on the fovea of the retina during head rotations and translations. VOR is therefore crucial to preserve clear vision, whilst it also plays a key role in maintaining balance through gaze stability. Changes in the VOR function can result in abnormal nygstamus, oscillopsia , visual vertigo and falls. Actually, falls in elderly have become a major public – health concern due to their social and economic costs. Since the vestibular control of eye movements helps to maintain equilibrium and spatial orientation, understanding the biological primitives in vestibular control becomes pivotal in combating falls in the elderly, abnormal nygstamus, oscillopsia or visual vertigo.
The VOR depends on the vestibular system, which detects head rotation. VOR’s nature is purely feed-forward since it induces prompt compensatory eye movements as consequence of head movements. VOR is mediated by a control system in which adaptation is directly driven by sensorimotor errors: the cerebellum. The existing mismatch between head movements (signalled by the vestibular organ) and the incoming information to the cerebellum about eye movements represents sensory errors, which are called retinal slips. The feed-forward adaptive control mediated by the cerebellum aims at minimising these retinal slips. The VOR, together with eye-blink classical conditioning, is broadly assumed as the paradigm that better reveals cerebellar learning.
During SPIKECONTROL, we modelled the neural basis of VOR control to provide a mechanistic understanding of the cerebellar functionality, which plays a key role in VOR adaptation. This work focused in testing the main theoretical hypotheses through this modelling approach. On the one hand, this work aimed at cross-linking data on VOR at behavioural and neural level. On the other hand, the developed VOR controller, based on cerebellar sensorimotor adaptation, was integrated within the simulated iCub and the actual iCub robot. Through the simulation of VOR control impairments, we examined possible consequences on the vestibular processing capabilities of the VOR model

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The ultimate step of this project was to integrate the cerebellar circuit modelling within real time RT behavioural tasks. This integrative approach aimed to study the cerebellar role under an embodied cognition scenario in which the cerebellum was responsible for solving and facilitating the body interaction with the environment. To that aim, we needed a neural structure facilitating the neural interaction, the cerebellar model, a front-end human body, the humanoid iCub robot and a behavioural task, the VOR. The cerebellar model was first dissociated from a traditional behavioural task to validate the operation of each cerebellar-subsystem individually

First, we focused in the inferior olive–Purkinje cell–cerebellar nuclei IO–PC–CN subcircuit, thus the granular layer was initially simplified. We tested the hypothesis of the CN afferents operating as a gain-controller and a learning consolidation site thanks to the Spike-timing dependent-plasticity mechanisms (STDP) underlying the cerebellar neural structure. STDP operates not only at parallel fibres PF–PC synapses but also at mossy fibres MF–CN and PC–CN afferents. We demonstrated how these STDP mechanisms contributed to create an internal adaptive gain controller at CN afferents whilst the synaptic distribution at PF–PC synapses were transferred to MF– CN synapses

Second, the focus was in the cerebellar cortex, concretely the granular layer. We studied how to evaluate the input activity within the cerebellar granule-cell layer. Supervised learning has long been attributed to several feed forward neural circuits within the brain, paying particular attention to the cerebellar granule-cell layer amongst them. The activity of cerebellar granule cells is conveyed by PF and translated into PC activity.We developed an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. We configured the granular layer activations according to the given output of this algorithm and then interconnected this layer to the IO-PC-CN sub circuit, thus creating the neural basis for our cerebellar model that was used in subsequence project stages

Third, once the cerebellar model working principles were tested, we further developed the cerebellar model as a VOR controller. This controller was embedded in a forward control loop that allowed cerebellar-dependent sensorimotor adaptation in a neuromimetic system

Finally, the cerebellar capability of performing adaptive information processing mediating sensorimotor control was evaluated in a specific VOR test through a closed-loop sensorimotor interaction in real time (RT). Simulating our cerebellar-based VOR model conjointly with the humanoid robotic platform helped assessing both qualitative and quantitative performance indicators to constructively cross-validate the model against neurophysiological and behavioural data. To that aim the cerebellar-based VOR model were implemented and tested first in the iCub simulator, and finally in the real humanoid iCub robot. The VOR controller worked in RT as the humanoid robot iCub moved its head

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The single overarching goal of SPIKECONTROL is to advance the understanding on the functionality of the cerebellar structure by first applying mathematical techniques to produce models of the cerebellum across different scales to understand the basic principles underlying its neural functions. Then, these models are engineered and embodied in neural systems to decipher how simplified models of neural networks can abstract essential features from larger scale models to remain biologically realistic. As a result, we found ways to construct spiking neural networks for efficiently solving behavioural tasks. We believe that the outcome of this project may have the potential to start questioning the fundamental limits on the performance and capabilities of conventional computing technology and it may contribute to the development of a completely new category of computing systems with ‘brain-like intelligence’, able to be ‘embodied’ into neurorobots

The cerebellum self-adapts better than artificial systems to manipulation tasks with changeable dynamics. Despite the tremendous progress in computational neuroscience, electronics, and mechatronics being made, building a synthetic controller able to outperform a human being when moving and interacting with others remains being a major challenge. Interestingly, this is true despite the enormous information processing capacity, in theory, that the 3rd generation of artificial neural networks holds (Spiking neural network). This strongly suggests that advances in embodied neuroscience will depend on removing current technical limitations, thus bringing spiking neural networks to ‘intelligent machines’ applications. To that aim, we developed within this project one amongst the first neuromorphic architectures that can run medium scale cerebellar simulations able to operate an iCub in RT

iCub performance in VOR before and after learning.Optical flow(green) tend to zero when optimal VOR