ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2005
History-dependent multiple-time-scale dynamics in a single-neuron model.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed, 82 citations in OpenAlex.
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- Pulse frequency dependency of photobiomodulation on the bioenergetic functions of human dental pulp stem cells.Scientific reports · 2017Article
- Dynamical Timescale Explains Marginal Stability in Excitability Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Neural oscillations as a signature of efficient coding in the presence of synaptic delays.eLife · 2016Article
- How can we study reasoning in the brain?Frontiers in human neuroscience · 2015Article
- Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model.PLoS computational biology · 2014Article
- Temporal whitening by power-law adaptation in neocortical neurons.Nature neuroscience · 2013Article
- Distinct effects of brief and prolonged adaptation on orientation tuning in primary visual cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2013Article
- Time scales in cognitive neuroscience.Frontiers in physiology · 2013Article
- Is the brain's inertia for motor movements different for acceleration and deceleration?PloS one · 2013Article
- Conserved N-terminal negative charges support optimally efficient N-type inactivation of Kv1 channels.PloS one · 2013Article
- Dopamine modulation of Ih improves temporal fidelity of spike propagation in an unmyelinated axon.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2012Article
- Beyond faithful conduction: short-term dynamics, neuromodulation, and long-term regulation of spike propagation in the axon.Progress in neurobiology · 2011Review
- Estimating Neuronal Information: Logarithmic Binning of Neuronal Inter-Spike Intervals.Entropy (Basel, Switzerland) · 2011Article
- Exploring the fundamental dynamics of error-based motor learning using a stationary predictive-saccade task.PloS one · 2011Article
- History-dependent excitability as a single-cell substrate of transient memory for information discrimination.PloS one · 2010Article
- Dynamics of excitability over extended timescales in cultured cortical neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2010Article
- Near scale-free dynamics in neural population activity of waking/sleeping rats revealed by multiscale analysis.PloS one · 2010Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
History-dependent characteristic time scales in dynamics have been observed at several levels of organization in neural systems. Such dynamics can provide powerful means for computation and memory. At the level of the single neuron, several microscopic mechanisms, including ion channel kinetics, can support multiple-time-scale dynamics. How the temporally complex channel kinetics gives rise to dynamical properties of the neuron is not well understood. Here, we construct a model that captures some features of the connection between these two levels of organization. The model neuron exhibits history-dependent multiple-time-scale dynamics in several effects: first, after stimulation, the recovery time scale is related to the stimulation duration by a power-law scaling; second, temporal patterns of neural activity in response to ongoing stimulation are modulated over time; finally, the characteristic time scale for adaptation after a step change in stimulus depends on the duration of the preceding stimulus. All these effects have been observed experimentally and are not explained by current single-neuron models. The model neuron here presented is composed of an ensemble of ion channels that can wander in a large pool of degenerate inactive states and thus exhibits multiple-time-scale dynamics at the molecular level. Channel inactivation rate depends on recent neural activity, which in turn depends through modulations of the neural response function on the fraction of active channels. This construction produces a model that robustly exhibits nonexponential history-dependent dynamics, in qualitative agreement with experimental results.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.