Unknown

Dataset Information

0

Passive dendrites enable single neurons to compute linearly non-separable functions.


ABSTRACT: Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendritic sub-units are only saturating. To determine if such neurons can also compute linearly non-separable functions, we enumerate, for a given parameter range, the Boolean functions implementable by a binary neuron model with a linear sub-unit and either a single spiking or a saturating dendritic sub-unit. We then analytically generalize these numerical results to an arbitrary number of non-linear sub-units. First, we show that a single non-linear dendritic sub-unit, in addition to the somatic non-linearity, is sufficient to compute linearly non-separable functions. Second, we analytically prove that, with a sufficient number of saturating dendritic sub-units, a neuron can compute all functions computable with purely excitatory inputs. Third, we show that these linearly non-separable functions can be implemented with at least two strategies: one where a dendritic sub-unit is sufficient to trigger a somatic spike; another where somatic spiking requires the cooperation of multiple dendritic sub-units. We formally prove that implementing the latter architecture is possible with both types of dendritic sub-units whereas the former is only possible with spiking dendrites. Finally, we show how linearly non-separable functions can be computed by a generic two-compartment biophysical model and a realistic neuron model of the cerebellar stellate cell interneuron. Taken together our results demonstrate that passive dendrites are sufficient to enable neurons to compute linearly non-separable functions.

SUBMITTER: Caze RD 

PROVIDER: S-EPMC3585427 | biostudies-other | 2013

REPOSITORIES: biostudies-other

altmetric image

Publications

Passive dendrites enable single neurons to compute linearly non-separable functions.

Cazé Romain Daniel RD   Humphries Mark M   Gutkin Boris B  

PLoS computational biology 20130228 2


Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR. Other neuron classes, such as interneurons, may possess only a few independent dendritic sub-units, or only passive dendrites where input summation is purely sub-linear, and where dendri  ...[more]

Similar Datasets

| S-EPMC5985318 | biostudies-literature
| S-EPMC8384225 | biostudies-literature
| S-EPMC3432406 | biostudies-literature
2022-01-21 | GSE180414 | GEO
| S-EPMC8325157 | biostudies-literature
2022-05-11 | GSE180413 | GEO
| S-EPMC6401120 | biostudies-literature
2022-05-11 | GSE180412 | GEO
| S-EPMC3382010 | biostudies-literature
2021-01-15 | GSE157204 | GEO