Online learning

…and feedback pathways, and (c) learning rules that combine locally available information from pre- and -post-synaptic sources. Inspired by loc

Working memory is inherently limited in capacity and duration, which may hamper learning, including in online learning environments. Because working memory capacity varies among individuals, cognitive load in online learning may not have the same impact for neurotypical and neurodivergent students. This study investigated the relationship between neurodivergent traits and cognitive load during online learning. Seventy participants, including 35 neurotypical students and 35 neurodivergent students with a diagnosis of attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD

With recent advances in learning algorithms, recurrent networks of spiking neurons are achieving performance competitive with standard recurrent neural networks. Still, these learning algorithms are limited to small networks of simple spiking neurons and modest-length temporal sequences, as they impose high memory requirements, have difficulty training complex neuron models, and are incompatible with online learning.Taking inspiration from the concept of Liquid Time-Constant (LTCs), we introduce a novel class of spiking neurons, the Liquid Time-Constant Spiking Neuron (LTC-SN), resulting in fu

…This requires fast, online learning. What features of cerebellar circuit structure make it particularly suited to online learning? The cerebellum has a distinctive circuit architecture in which each mossy fibre input typically projects to 250 granule cells, a population that comprises more than half of the neurons in the brain. Each granule cell forms ~4 synapses with mossy fibres. The main hypotheses for this sparse input expansion are that it facilitates pattern separation1 and smoo