Classes of dendritic information processing

• Published in: Current opinion in neurobiology Vol. 58; pp. 78 - 85
• Main Authors: Payeur, Alexandre, Béïque, Jean-Claude, Naud, Richard
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.10.2019
• Subjects: Action Potentials; Dendrites; Learning; Models, Neurological; Neural Networks, Computer
• ISSN: 0959-4388; 1873-6882
• DOI: 10.1016/j.conb.2019.07.006

•Dendritic compartmentalization is controlled and avoidable.•Compartmentalization combined with amplification allows neurons to withhold information distinct from what is being communicated by the cell body.•Network state, inhibition and neuromodulation can route information withheld in dendrites.•Dendrite-dependent burst firing can multiplex multiple streams of information. Dendrites are much more than passive neuronal components. Mounting experimental evidence and decades of computational work have decisively shown that dendrites leverage a host of nonlinear biophysical phenomena and actively participate in sophisticated computations, at the level of the single neuron and at the level of the network. However, a coherent view of their processing power is still lacking and dendrites are largely neglected in neural network models. Here, we describe four classes of dendritic information processing and delineate their implications at the algorithmic level. We propose that beyond the well-known spatiotemporal filtering of their inputs, dendrites are capable of selecting, routing and multiplexing information. By separating dendritic processing from axonal outputs, neuron networks gain a degree of freedom with implications for perception and learning.