Format

Send to

Choose Destination
Front Cell Neurosci. 2015 Mar 24;9:67. doi: 10.3389/fncel.2015.00067. eCollection 2015.

Contribution of sublinear and supralinear dendritic integration to neuronal computations.

Author information

1
Unit of Dynamic Neuronal Imaging, Department of Neuroscience, CNRS UMR 3571, Institut Pasteur Paris, France.
2
Group for Neural Theory, LNC INSERM U960, Institut d'Etude de la Cognition de l'Ecole normale supérieure, Ecole normale supérieure Paris, France ; Department of Bioengineering, Imperial College London London, UK.
3
Unit of Dynamic Neuronal Imaging, Department of Neuroscience, CNRS UMR 3571, Institut Pasteur Paris, France ; Center for Research in Neuroscience, Department of Neurology and Neurosurgery, The Research Institute of the McGill University Health Centre, Montreal General Hospital Montreal, QC, Canada.
4
Sorbonne Universités, UPMC Univ Paris 6, UMR 8256 B2A, Team Brain Development, Repair and Aging Paris, France.
5
Group for Neural Theory, LNC INSERM U960, Institut d'Etude de la Cognition de l'Ecole normale supérieure, Ecole normale supérieure Paris, France ; Federal Research University Higher School of Economics Moscow, Russia.

Abstract

Nonlinear dendritic integration is thought to increase the computational ability of neurons. Most studies focus on how supralinear summation of excitatory synaptic responses arising from clustered inputs within single dendrites result in the enhancement of neuronal firing, enabling simple computations such as feature detection. Recent reports have shown that sublinear summation is also a prominent dendritic operation, extending the range of subthreshold input-output (sI/O) transformations conferred by dendrites. Like supralinear operations, sublinear dendritic operations also increase the repertoire of neuronal computations, but feature extraction requires different synaptic connectivity strategies for each of these operations. In this article we will review the experimental and theoretical findings describing the biophysical determinants of the three primary classes of dendritic operations: linear, sublinear, and supralinear. We then review a Boolean algebra-based analysis of simplified neuron models, which provides insight into how dendritic operations influence neuronal computations. We highlight how neuronal computations are critically dependent on the interplay of dendritic properties (morphology and voltage-gated channel expression), spiking threshold and distribution of synaptic inputs carrying particular sensory features. Finally, we describe how global (scattered) and local (clustered) integration strategies permit the implementation of similar classes of computations, one example being the object feature binding problem.

KEYWORDS:

Boolean analysis; binary neruons; dendrites; input-output transformation; neural computation; nonlinear transformations; uncaging; votlage activated channels

Supplemental Content

Full text links

Icon for Frontiers Media SA Icon for PubMed Central
Loading ...
Support Center