ArticleFrontiers in computational neuroscience2013
Nonlinear multiplicative dendritic integration in neuron and network models.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Physical and functional convergence of the autism risk genes Scn2a and Ank2 in neocortical pyramidal cell dendrites.Neuron · 2024Article
- A multilayer-multiplexer network processing scheme based on the dendritic integration in a single neuron.AIMS neuroscience · 2022Article
- Solving Constraint-Satisfaction Problems with Distributed Neocortical-Like Neuronal Networks.Neural computation · 2018Article
- Article
- Extending Integrate-and-Fire Model Neurons to Account for the Effects of Weak Electric Fields and Input Filtering Mediated by the Dendrite.PLoS computational biology · 2016Article
- Neural Computations in a Dynamical System with Multiple Time Scales.Frontiers in computational neuroscience · 2016Article
- The influence of sensory afferent input on local motor cortical excitatory circuitry in humans.The Journal of physiology · 2015Article
- Article
- Study of GABAergic extra-synaptic tonic inhibition in single neurons and neural populations by traversing neural scales: application to propofol-induced anaesthesia.Journal of computational neuroscience · 2014Article
- Volterra dendritic stimulus processors and biophysical spike generators with intrinsic noise sources.Frontiers in computational neuroscience · 2014Article
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Authors and funding
4 authors.
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Abstract
Neurons receive inputs from thousands of synapses distributed across dendritic trees of complex morphology. It is known that dendritic integration of excitatory and inhibitory synapses can be highly non-linear in reality and can heavily depend on the exact location and spatial arrangement of inhibitory and excitatory synapses on the dendrite. Despite this known fact, most neuron models used in artificial neural networks today still only describe the voltage potential of a single somatic compartment and assume a simple linear summation of all individual synaptic inputs. We here suggest a new biophysical motivated derivation of a single compartment model that integrates the non-linear effects of shunting inhibition, where an inhibitory input on the route of an excitatory input to the soma cancels or "shunts" the excitatory potential. In particular, our integration of non-linear dendritic processing into the neuron model follows a simple multiplicative rule, suggested recently by experiments, and allows for strict mathematical treatment of network effects. Using our new formulation, we further devised a spiking network model where inhibitory neurons act as global shunting gates, and show that the network exhibits persistent activity in a low firing regime.
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