Illuminating dendritic function with computational models

Panayiota Poirazi

doi:10.1038/s41583-020-0301-7

2020-05-11

125

PRE

Panayiota Poirazi *

Athanasia Papoutsi

DOI：10.1038/s41583-020-0301-7

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摘要

En 中文

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 mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires - and drives - new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities. Models of dendrites have been instrumental in our understanding of their functions. Poirazi and Papoutsi review the major contributions of dendritic models, including those already proved or waiting to be experimentally verified, and highlight successful interactions between the modelling and experimental neuroscience communities.

Keyword:

DISTAL APICAL DENDRITES

PYRAMIDAL NEURON

ACTION-POTENTIALS

SYNAPTIC INTEGRATION

BASAL DENDRITES

ACTIVE DENDRITES

CORTICAL-NEURONS

NMDA SPIKES

ORIENTATION SELECTIVITY

CALCIUM TRANSIENTS