The cerebellar granular layer plays a central role in sensory processing and pattern separation through its distinctive feedforward architecture. Here, we present a biologically …
The striatum plays a central role in action selection and reinforcement learning, integrating cortical inputs with dopaminergic signals encoding reward prediction errors. While …
The basal ganglia (BG), and more specifically the striatum, have long been proposed to play an essential role in action-selection based on a reinforcement learning (RL) paradigm. …
The basal ganglia (BG) represent a critical center of the nervous system for sensorial discrimination. Although it is known that Huntington’s disease (HD) affects this brain area, …
Computational modeling is becoming a widely used methodology in modern neuroscience. However, as the complexity of the phenomena under study increases, the analysis of the results …
The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on …
The cerebellum is known to play a critical role in learning relevant patterns of activity for adaptive motor control, but the underlying network mechanisms are only partly …
Adaptable gain regulation is at the core of the forward controller operation performed by the cerebro-cerebellar loops and it allows the intensity of motor acts to be finely tuned …
The way long-term synaptic plasticity regulates neuronal spike patterns is not completely understood. This issue is especially relevant for the cerebellum, which is endowed with …
In biological systems, instead of actual encoders at different joints, proprioception signals are acquired through distributed receptive fields. In robotics, a single and accurate …