It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.

The cerebellum permits a wide range of behaviors that involve sensorimotor integration. We have been investigating how specific cellular and synaptic specializations of cerebellar neurons measured in vitro, give rise to circuit activity in vivo. We have investigated these issues by studying Purkinje neurons as well as the large neurons of the mouse cerebellar nuclei, which form the major excitatory premotor projection from the cerebellum. Large CbN cells have ion channels that favor spontaneous action potential firing and GABAA receptors that generate ultra-fast inhibitory synaptic currents, raising the possibility that these biophysical attributes may permit CbN cells to respond differently to the degree of temporal coherence of their Purkinje cell inputs. In vivo, self-initiated motor programs associated with whisking correlates with asynchronous changes in Purkinje cell simple spiking that are asynchronous across the population. The resulting inhibition converges with mossy fiber excitation to yield little change in CbN cell firing, such that cerebellar output is low or cancelled. In contrast, externally applied sensory stimuli elicits a transient, synchronous inhibition of Purkinje cell simple spiking. During the resulting strong disinhibition of CbN cells, sensory-induced excitation from mossy fibers effectively drives cerebellar outputs that increase the magnitude of reflexive whisking. Purkinje cell synchrony, therefore, may be a key variable contributing to the “positive effort” hypothesized by David Marr in 1969 to be necessary for cerebellar control of movement.

The vIRt nucleus in the medulla, composed of mainly inhibitory neurons, is necessary for whisking rhythm generation. It innervates motoneurons in the facial nucleus (FN) that project to intrinsic vibrissa muscles. The nearby pre-Bötzinger complex (pBötC), which generates inhalation, sends inhibitory inputs to the vIRt nucleus which contribute to the synchronization of vIRt neurons. Lower-amplitude periodic whisking, however, can occur after decay of the pBötC signal. To explain how vIRt network generates these “intervening” whisks by bursting in synchrony, and how pBötC input induces strong whisks, we construct and analyze a conductance-based (CB) model of the vIRt circuit composed of hypothetical two groups, vIRtr and vIRtp, of bursting inhibitory neurons with spike-frequency adaptation currents and constant external inputs. The CB model is reduced to a rate model to enable analytical treatment. We find, analytically and computationally, that without pBötC input, periodic bursting states occur within a certain ranges of network connectivities. Whisk amplitudes increase with the level constant external input to the vIRT. With pBötC inhibition intact, the amplitude of the first whisk in a breathing cycle is larger than the intervening whisks for large pBötC input and small inhibitory coupling between the vIRT sub-populations. The pBötC input advances the next whisk and shortens its amplitude if it arrives at the beginning of the whisking cycle generated by the vIRT, and delays the next whisks if it arrives at the end of that cycle. Our theory provides a mechanism for whisking generation and reveals how whisking frequency and amplitude are controlled.