Simplified dynamics of human and mammalian neocortical neurons.
Abstract
The behavior of human and mammalian neocortical neurons is governed by the interplay of approximately a dozen ion currents. Due to the complexity of the resulting dynamics, it is sometimes advantageous to resort to simpler systems in order to gain insight into the relationship between underlying dynamical principles and biophysics. This paper presents a new and extremely simple approximation to the dynamics of neocortical neurons based on just four simulated ion currents: I(Na), I(K), I(T), and I(AHP). The formulation incorporates Ohm's law plus explicit representation of Na(+), K(+), and Ca(2+)equilibrium potentials, yet mathematical simplicity is retained by restriction of the dynamics to cubic nonlinearities. The resulting equations produce a good approximation to spike shapes, firing rates, and bursting behavior throughout the physiological range. Analysis of the equations suggests that four unique dynamical regimes form the basis for different categories of neocortical neurons. Synaptic coupling between these model neurons demonstrates their potential utility in simulations by producing network models of bursting and of short-term memory function.
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To achieve a safe, reliable, high-performance B/CI system, a critical mission requirement is the initial establishment of intimate and stable connections to monitor the electrical firing patterns and waveforms of the ∼86 × 10^9 neurons and the ∼2 × 10^14 synapses of the human brain at a suitable repetition rate (400-800 Hz is the reported average maximum range).
“To achieve a safe, reliable, high-performance B/CI system, a critical mission requirement is the initial establishment of intimate and stable connections to monitor the electrical firing patterns and waveforms of the ∼86 × 109 neurons and the ∼2 × 1014 synapses of the human brain at a suitable repetition rate (400–800 Hz is the reported average maximum range) (Wilson, 1999; Contreras, 2004).”
From Human Brain/Cloud Interface (2019) · cites this paper as Wilson, 1999
Cites
Relationship between repetitive firing and afterhyperpolarizations in human neocortical neurons
Lorenzon, N. M., & Foehring, R. C. (1992) · Journal of Neurophysiology · doi:10.1152/jn.1992.67.2.350 · Cited by 145 · cited 2× · see citing works
“The approximately exponential time course of this adaptation agrees with data from both human (McCormick & Williamson, 1989; Lorenzon & Foehring, 1992) and other mammalian (Connors & Gutnick, 1990) RS neurons.”
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Martins, N. R. B., Angelica, A., Chakravarthy, K., Svidinenko, Y., Boehm, F. J., Opris, I., Lebedev, M. A., Swan, M., Garan, S. A., Rosenfeld, J. V., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00112 · Cited by 91 · see citing works
- Authors
- WILSON, H. R.
- Affiliations
- University of Chicago (US)
- Corresponding author
- WILSON, HUGH R.
- ORCID
- WILSON, HUGH R.
- Venue
- Journal of Theoretical Biology
- Publisher
- Elsevier BV
- Published
- 1999-10
- Volume
- 200
- Issue
- 4
- Pages
- 375-388
- ISSN
- 0022-5193 (ISSN-L)
- OpenAlex
- W1987664075
- Cited by
- 222
- References
- 1
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 10 · 2023: 21 · 2022: 20 · 2021: 12 · 2020: 6 · 2019: 3 · 2018: 5 · 2017: 6 · 2016: 9 · 2015: 7 · 2014: 9 · 2013: 10 · 2012: 7
- FWCI
- 0.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Theoretical Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Eye Institute (EY 02158, R01EY002158)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ion channel regulation and function
- Keywords
- neocortical neurons, ionic currents, sodium current, potassium currents, transient current, Kerr nonlinearity, spike shape, firing rate, burst firing, dynamical regimes, synaptic coupling, neural network model, short-term memory
- MeSH terms
- Animals, Computer Simulation, Humans, Ion Channels, Memory, Short-Term, Models, Neurological, Neocortex
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