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Neuromodulators control neuronal dynamics through feature space reshaping
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The activity of neurons in the central nervous system changes drastically across different states like asleep or awake, attentive or drowsy, executing motor commands or resting. Neuromodulation supports these changes by affecting neuronal excitability. Much work has been devoted to understanding the effects of neuromodulators on various neurons. However, we still lack a cohesive picture of how neuromodulation affects neuronal dynamics. Here, we analyzed electrophysiological data from published papers and extracted features for seven types of neurons from five areas of the human and rodent central nervous system, under the effect of five neuromodulators. We studied neuromodulation dynamics using the widespread, simple, yet biologically accurate, Adaptive Exponential Integrate-and-Fire model. We have found that different neuromodulators remap the feature space of neurons into non-overlapping clusters. This work organizes our knowledge about neuromodulation in a compact format, useful for modelers and theoreticians alike.
Title: Neuromodulators control neuronal dynamics through feature space reshaping
Description:
The activity of neurons in the central nervous system changes drastically across different states like asleep or awake, attentive or drowsy, executing motor commands or resting.
Neuromodulation supports these changes by affecting neuronal excitability.
Much work has been devoted to understanding the effects of neuromodulators on various neurons.
However, we still lack a cohesive picture of how neuromodulation affects neuronal dynamics.
Here, we analyzed electrophysiological data from published papers and extracted features for seven types of neurons from five areas of the human and rodent central nervous system, under the effect of five neuromodulators.
We studied neuromodulation dynamics using the widespread, simple, yet biologically accurate, Adaptive Exponential Integrate-and-Fire model.
We have found that different neuromodulators remap the feature space of neurons into non-overlapping clusters.
This work organizes our knowledge about neuromodulation in a compact format, useful for modelers and theoreticians alike.
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