ArticleFrontiers in systems neuroscience2020
Capturing Multiple Timescales of Adaptation to Second-Order Statistics With Generalized Linear Models: Gain Scaling and Fractional Differentiation.
Article in Frontiers in systems neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Multiple Distinct Timescales of Rapid Sensory Adaption in the Thalamocortical Circuit.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- MULTIPLE DISTINCT TIMESCALES OF RAPID SENSORY ADAPATION IN THE THALAMOCORTICAL CIRCUIT.bioRxiv : the preprint server for biology · 2024Article
- Dynamic Nonlinear Spatial Integrations on Encoding Contrasting Stimuli of Tectal Neurons.Animals : an open access journal from MDPI · 2024Article
- An advanced approach for the electrical responses of discrete fractional-order biophysical neural network models and their dynamical responses.Scientific reports · 2023Article
- Diverse electrical responses in a network of fractional-order conductance-based excitable Morris-Lecar systems.Scientific reports · 2023Article
- Visual Responses to Moving and Flashed Stimuli of Neurons in Domestic Pigeon (Animals : an open access journal from MDPI · 2022Article
- Efficient spline regression for neural spiking data.PloS one · 2021Article
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2 authors.
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Abstract
Single neurons can dynamically change the gain of their spiking responses to take into account shifts in stimulus variance. Moreover, gain adaptation can occur across multiple timescales. Here, we examine the ability of a simple statistical model of spike trains, the generalized linear model (GLM), to account for these adaptive effects. The GLM describes spiking as a Poisson process whose rate depends on a linear combination of the stimulus and recent spike history. The GLM successfully replicates gain scaling observed in Hodgkin-Huxley simulations of cortical neurons that occurs when the ratio of spike-generating potassium and sodium conductances approaches one. Gain scaling in the GLM depends on the length and shape of the spike history filter. Additionally, the GLM captures adaptation that occurs over multiple timescales as a fractional derivative of the stimulus envelope, which has been observed in neurons that include long timescale afterhyperpolarization conductances. Fractional differentiation in GLMs requires long spike history that span several seconds. Together, these results demonstrate that the GLM provides a tractable statistical approach for examining single-neuron adaptive computations in response to changes in stimulus variance.
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