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 …

Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal
to the beautiful recordings of today, neuroscientists have been striving to unravel the …

Neural network models are potential tools for improving our understanding of complex brain
functions. To address this goal, these models need to be neurobiologically realistic …

Inspired by the principles of the biological nervous system, neuromorphic engineering has
brought a promising alternative approach to intelligence computing with high energy …

Half a century since their discovery by Llinás and colleagues, dendritic spikes have been
observed in various neurons in different brain regions, from the neocortex and cerebellum to …

Research on dendrites has been conducted for decades, providing valuable information for
the development of dendritic computation. Creating an ideal neuron model is crucial for …

Neuromorphic technologies typically employ a point neuron model, neglecting the
spatiotemporal nature of neuronal computation. Dendritic morphology and synaptic …

Brain neurons exhibit far more sophisticated and powerful information‐processing
capabilities than the simple integrators commonly modeled in neuromorphic computing. A …

As dendrites are essential parts of neurons, they are crucial factors for neuronal activities to
follow multiple timescale dynamics, which ultimately affect information processing and …

Neuron models with explicit dendritic dynamics have shed light on mechanisms for
coincidence detection, pathway selection and temporal filtering. However, it is still unclear …