|Title||Classes of dendritic information processing.|
|Publication Type||Journal Article|
|Year of Publication||2019|
|Authors||Payeur, Alexandre, Jean-Claude Béïque, and Richard Naud|
|Journal||Curr Opin Neurobiol|
|Date Published||2019 Aug 13|
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.
|Alternate Journal||Curr. Opin. Neurobiol.|