Inferring Synaptic Excitation/Inhibition Balance from Field Potentials

By Richard Gao, Erik J. Peterson, Bradley Voytek. bioRxiv, 2016

936 citations recorded by the source corpus.

Neural circuits sit in a dynamic balance between excitation (E) and inhibition (I). Fluctuations in this E:I balance have been shown to influence neural computation, working memory, and information processing. While more drastic shifts and aberrant E:I patterns are implicated in numerous neurological and psychiatric disorders, current methods for measuring E:I dynamics require invasive procedures that are difficult to perform in behaving animals, and nearly impossible in humans. This has limited the ability to examine the full impact that E:I shifts have in neural computation and disease. In this study, we develop a computational model to show that E:I ratio can be estimated from the power law exponent (slope) of the electrophysiological power spectrum, and validate this relationship using previously published datasets from two species (rat local field potential and macaque electrocorticography). This simple method--one that can be applied retrospectively to existing data--removes a major hurdle in understanding a currently difficult to measure, yet fundamental, aspect of neural computation.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0a2c3202a9c4c4f7709eeed324ca53e12ad4af72

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Complex oscillatory waves emerging from cortical organoids model early human brain network development (2019, background): “Periodic oscillatory activity is often defined as a ‘‘bump’’ over the characteristic 1/f background in the power spectral density (PSD) of extracellular signals above and beyond the aperiodic 1/f signal (Buzsáki et al., 2013; Gao et al., 2017).”
Parameterizing neural power spectra into periodic and aperiodic components (2020, methodology): “Given that the aperiodic exponent partially reflects excitation/inhibition balance 19 , and is systematically altered in aging 18 , we hypothesized that the aperiodic component would predict working memory performance.”
The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine (2019, neutral): “For instance, due to the problem of 631 fitting a power-law where oscillations are present, the empirical studies in [35] select a range of 632 frequencies between 30 and 70 Hz.”
Aperiodic neural timescales in prefrontal cortex dilate with increased task abstraction (2025, unknown): “We thus deferred to fitting a model without knees, and instead used the PSD-Exponent as a proxy (24,50).”
The development of theta and alpha neural oscillations from ages 3 to 24 years (2021, background): “for this hypothesis comes from propofol-induced anesthesia of macaque monkeys, where propofol, acting on GABAA inhibitory activity, increased the “I” in the E:I balance, causing the aperiodic signal to steepen (Gao et al., 2017).”
Characterization of aperiodic and theta activity in preterm infants using EEG: Insights into cerebral maturation and inter-individual variability (2026, unknown): “…frequencies, the aperiodic exponent has been linked to the balance between synaptic excitation (E) and inhibition (I) within cortical circuits (Donoghue et al., 2020; Gao et al., 2017), with higher exponent associated with lower E/I ratio related to increased inhibitory activity (Gao et al., 2017).”
Aperiodic Dynamics of Cell Assemblies Recruited for L1 and L2 Processing of French Wh-Dependencies Highlight a Temporo-Parietal Engagement in Syntax (2026, unknown): “The exponent x has been argued to reflect the balance of postsynaptic and transmembrane activity for excitatory and inhibitory circuits [56–59], with a steeper slope indicating greater inhibitory signal [60].”
Non-Neural Sources Systematically Impact Aperiodic EEG Activity (2026, unknown): “While the offset has been linked to neuronal population spiking rates 3 , variations in the exponent are proposed to reflect differences in neural noise 4,5 , self-organized criticality 6 – 8 , or the most prominently 9 , the balance between inhibitory and excitatory synaptic activity 10,11 .”
Relating ADHD to the aperiodic component of the EEG signal in both males and females across development (2026, unknown): “In their seminal paper using computational and animal models, Gao et al. (2017) linked the aperiodic exponent to the ratio of excitation and inhibition in neuronal signaling.”
Revolutionizing Pediatric Neurophysiology With Magnetoencephalography (2026, unknown): “Measurement of the exponent (slope) of the resting-state power spectrum is hypothesized to provide a non-invasive measure of the neural-circuit excitatory:inhibitory (E:I) balance (Gao et al. 2017).”
Resting-state periodic and aperiodic brain oscillations from birth to preschool years: Aperiodic maturity predicts developmental course (2026, unknown): “…associated with an age-related increase in the neural-circuit E:I ratio, with changes to the neural-circuit E:I due to less neural noise in older than younger children, given an age-related increase in inhibitory GABAergic and an age-related decrease in glutamatergic signaling (Gao et al., 2017).”
Confirmation of a Useful Dark‐Room Resting‐State Procedure: Periodic and Aperiodic MEG Activity in Children (2026, unknown): “Measurement of the exponent (slope) of the RS power spectrum is hypothesized to provide a non-invasive measure of the neural-circuit excitatory:inhibitory (E:I) balance (Gao et al. 2017).”
Aperiodic parameters of the fMRI power spectrum associate with preterm birth and neonatal age (2026, unknown): “I) balance 54 , undergoing changes from the very early stages of development onwards 55 .”
Age and gender-related neurophysiological changes in sleep and wake states during childhood (2026, unknown): “…component was found to directly relate to global levels of neuronal activity (offset of the component) (Miller et al., 2014) and to synaptic and network properties, in particular excitation/inhibition balance across neuronal circuits (slope of the component) (Brake et al., 2024; Gao et al., 2017).”
Resting state aperiodic and periodic EEG activity in preschool-aged autistic children: differences from neurotypical peers and links to language skills (2026, unknown): “Given the growing hypothesis that autism may be related to E/I imbalance [34, 38], we hypothesized that autistic children would have a lower, flattened aperiodic slope, indicating increased excitation over inhibition, compared to neurotypical peers.”