Any neuron can perform linearly non-separable computations

Romain Cazé (1, 2)

1 IEMN - Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520
2 CSAM - IEMN - Circuits Systèmes Applications des Micro-ondes - IEMN

Multiple studies have shown how dendrites enable some neurons to perform linearly non-separable computations. These works focus on cells with an extended dendritic arbor where voltage can vary independently, turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the nervous system unexplored. Many neurons, e.g. granule cells, have modest dendritic trees and are electrically compact. It is impossible to decompose them into multiple independent subunits. Here, we upgraded the integrate and fire neuron to account for saturating dendrites. This artificial neuron has a unique membrane voltage and can be seen as a single layer. We present a class of linearly non-separable computations and how our neuron can perform them. We thus demonstrate that even a single layer neuron with dendrites has more computational capacity than without. Because any neuron has one or more layer, and all dendrites do saturate, we show that any dendrited neuron can implement linearly non-separable computations.

Mots clés: Dendrites; computation; linearly non-separable; neuroscience

Domaines: Sciences de l'ingénieur [physics]

Romain Cazé. Any neuron can perform linearly non-separable computations. F1000Research, 2021, 10, pp.539. ⟨10.12688/f1000research.53961.1⟩. ⟨hal-03408395⟩