ReviewFrontiers in cellular neuroscience2015
Contribution of sublinear and supralinear dendritic integration to neuronal computations.
Review in Frontiers in cellular neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers.
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75 citing papers in PubMed.
- Hierarchical feature binding in a spiking neural network model of the primate ventral visual pathway.PLoS computational biology · 2026Article
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- Motor cortex directly excites the substantia nigra pars reticulata, the basal ganglia output nucleus.Nature communications · 2026Article
- Flexible integration of natural stimuli by auditory cortical neurons.Journal of neurophysiology · 2026Article
- The integrated information Φ of an integrate and fire network.PLoS computational biology · 2026Article
- Neuronal heterogeneity of normalization strength in a circuit model.Science advances · 2026Article
- Enhanced information processing in the human neocortex: cellular mechanisms and translational perspectives.Frontiers in synaptic neuroscience · 2026Review
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- Bimodal nonlinear dendrites in PV+ basket cells drive distinct memory-related oscillations.iScience · 2025Article
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- Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing.Nature neuroscience · 2025Article
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- Contributions of action potentials to scalp EEG: Theory and biophysical simulations.PLoS computational biology · 2025Article
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- Sub-cellular population imaging tools reveal stable apical dendrites in hippocampal area CA3.Nature communications · 2025Article
- The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis.PLoS computational biology · 2025Article
- Temporal integration on the dendrites of fast-spiking basket cells.Scientific reports · 2024Article
- A dendritic substrate for temporal diversity of cortical inhibition.bioRxiv : the preprint server for biology · 2024Article
15 more citing papers are in PubMed but not listed here.
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6 authors.
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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.
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