Addressing the multiscale organization of the brain, which is fundamental to the dynamic
repertoire of the organ, remains challenging. In principle, it should be possible to model …

Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

The cerebellum is a central brain structure deeply integrated into major loops with the
cerebral cortex, brainstem, and spinal cord. The cerebellum shows a complex regional …

Molecular layer interneurons (MLIs) play an important role in cerebellar information
processing by controlling Purkinje cell (PC) activity via inhibitory synaptic transmission. A …

The failure of shallow neural network architectures in replicating human intelligence led the
machine learning community to focus on deep learning, to computationally match human …

The cerebellum operates exploiting a complex modular organization and a unified
computational algorithm adapted to different behavioral contexts. Recent observations …

The cerebellar network is renowned for its regular architecture that has inspired foundational
computational theories. However, the relationship between circuit structure, function and …

Reconstructing neuronal microcircuits through computational models is fundamental to
simulate local neuronal dynamics. Here a scaffold model of the cerebellum has been …

Neuronal inhibition can be defined as a spatiotemporal restriction or suppression of local
microcircuit activity. The importance of inhibition relies in its fundamental role in shaping …

A bioinspired adaptive model, developed by means of a spiking neural network made of
thousands of artificial neurons, has been leveraged to control a humanoid NAO robot in real …