Saccadic eye-movements play a crucial role in visuo-motor control by allowing rapid foveation onto new targets. However, the neural processes governing saccades adaptation are not fully understood. Saccades, due to the short-time of execution (20–100 ms) and the absence of sensory information for online feedback control, must be controlled in a ballistic manner. Incomplete measurements of the movement trajectory, such as the visual endpoint error, are supposedly used to form internal predictions about the movement kinematics resulting in predictive control. In order to characterize the synaptic and neural circuit mechanisms underlying predictive saccadic control, we have reconstructed the saccadic system in a digital controller embedding a spiking neural network of the cerebellum with spike timing-dependent plasticity (STDP) rules driving parallel fiber—Purkinje cell long-term potentiation and depression (LTP and LTD). This model implements a control policy based on a dual plasticity mechanism, resulting in the identification of the roles of LTP and LTD in regulating the overall quality of saccade kinematics: it turns out that LTD increases the accuracy by decreasing visual error and LTP increases the peak speed. The control policy also required cerebellar PCs to be divided into two subpopulations, characterized by burst or pause responses. To our knowledge, this is the first model that explains in mechanistic terms the visual error and peak speed regulation of ballistic eye movements in forward mode exploiting spike-timing to regulate firing in different populations of the neuronal network. This elementary model of saccades could be extended and applied to other more complex cases in which single jerks are concatenated to compose articulated and coordinated movements.

Learning-based modeling and control of soft robots is advantageous due to neural network's ability to capture complex dynamical effects with low computational cost. Continual Learning techniques add further value to these methods by allowing networks to learn from continuously available data without incurring into catastrophic forgetting. In the context of soft robotic control, such capability can be exploited to design controllers able to continuously adapt to changes in robot dynamics, frequently due to material degradation or external interactions. This should be done without forgetting the control under normal working conditions which can be recovered as soon as the external interactions return to normal. In this paper elastic weight consolidation is used to continuously re-tune a neural network-based controller while changing the external loading of a soft robot. We demonstrate experimentally on a soft robot arm that this method outperforms plain stochastic gradient descent in tracking tasks, in the context of a continuously changing loading condition. We also show that the proposed control architecture can improve its performances when exposed to loading conditions already experienced. This paper represents a first step towards the introduction of continual learning methods in the soft robot control field.

Planning and execution of voluntary movement depend on the contribution of distinct classes of neurons in primary motor and premotor areas. However, the specific functional role of GABAergic cells remains only partly understood. Here, electrophysiological and computational analyses are employed to compare directly the response properties of putative pyramidal (PNs) and fast-spiking, GABAergic neurons (FSNs) during licking and forelimb retraction in mice. Recordings from anterolateral motor cortex and rostral forelimb area, reveal that FSNs fire earlier and for a longer duration than PNs, with the exception of a subset of early-modulated PNs in deep layers. Computational analysis reveals that FSNs carry vastly more information than PNs about the onset of movement. While PNs differently modulate their discharge during distinct motor acts, most FSNs respond with a stereotyped increase in firing rate. Accordingly, the informational redundancy was greater among FSNs than PNs. These data suggest that a global rise of inhibition contributes to early action planning.