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Low-rate firing limit for neurons with axon, soma and dendrites driven by spatially distributed stochastic synapses
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Gowers, Robert, Timofeeva, Yulia and Richardson, Magnus J. E. (2020) Low-rate firing limit for neurons with axon, soma and dendrites driven by spatially distributed stochastic synapses. PLOS Computational Biology, 16 (4). e1007175. doi:10.1371/journal.pcbi.1007175
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Official URL: https://doi.org/10.1371/journal.pcbi.1007175
Abstract
Analytical forms for neuronal firing rates are important theoretical tools for the analysis of network states. Since the 1960s, the majority of approaches have treated neurons as being electrically compact and therefore isopotential. These approaches have yielded considerable insight into how single-cell properties affect network activity; however, many neuronal classes, such as cortical pyramidal cells, are electrically extended objects. Calculation of the complex flow of electrical activity driven by stochastic spatio-temporal synaptic input streams in these structures has presented a significant analytical challenge. Here we demonstrate that an extension of the level-crossing method of Rice, previously used for compact cells, provides a general framework for approximating the firing rate of neurons with spatial structure. Even for simple models, the analytical approximations derived demonstrate a surprising richness including: independence of the firing rate to the electrotonic length for certain models, but with a form distinct to the point-like leaky integrate-and-fire model; a non-monotonic dependence of the firing rate on the number of dendrites receiving synaptic drive; a significant effect of the axonal and somatic load on the firing rate; and the role that the trigger position on the axon for spike initiation has on firing properties. The approach necessitates only calculating the mean and variances of the non-thresholded voltage and its rate of change in neuronal structures subject to spatio-temporal synaptic fluctuations. The combination of simplicity and generality promises a framework that can be built upon to incorporate increasing levels of biophysical detail and extend beyond the low-rate firing limit treated in this paper.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QP Physiology | ||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Science > Mathematics | ||||||||
| SWORD Depositor: | Library Publications Router | ||||||||
| Library of Congress Subject Headings (LCSH): | Neurons, Neurons -- Mathematical models, Axons, Dendrites, Neural networks (Neurobiology) | ||||||||
| Journal or Publication Title: | PLOS Computational Biology | ||||||||
| Publisher: | Public Library of Science | ||||||||
| ISSN: | 1553-7358 | ||||||||
| Official Date: | 2020 | ||||||||
| Dates: |
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| Volume: | 16 | ||||||||
| Number: | 4 | ||||||||
| Article Number: | e1007175 | ||||||||
| DOI: | 10.1371/journal.pcbi.1007175 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | |||||||||
| Access rights to Published version: | Open Access | ||||||||
| RIOXX Funder/Project Grant: |
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| Contributors: |
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