[Eeglablist] Open online discussion: How Do Cable Theory and AMPA/GABA Balance Compare in Their Contributions to 1/f?

[Makoto Miyakoshi]

Hi Ching-Ming, Yevgeny, and Cedric,

Thank you for sharing your experience and results!

 *Structural Stability vs. Dynamic Change:* Since the subject's neuronal
   morphology (cable theory properties) cannot change by 60% within 3 hours,
   this massive shift provides strong evidence that *E/I balance
   (GABA-mediated inhibition)* is the primary driver of 1/f *dynamics*,
   even if cable theory sets the *baseline*.


Cable theory describes that when a neuron receives synaptic inputs at
synapses that are located further from the soma, it generates more low-pass
filtered post-synaptic potential. See a nice illustration from Rall et al.
(1967)
https://urldefense.com/v3/__https://github.com/sccn/OneOverF/discussions/8__;!!Mih3wA!DHtN8b_WLfq9LU0kcatC6qilNfGVpUWCoDhdGUr5gz4qcT4adsUlFefUMcDmaZVmsSJ6ai6Kq6mOCrDe05dArQOcMds$ 

Stephanie Jones' human neocortical neurosolver (HNN) has an explicit 2x2x2
models: cells at layer 2/3 and 5, distal and proximal inputs, and
excitatory (pyramidal neurons) and inhibitory (basket cells) neurons. Among
these parameters, 'distal input' is likely to be associated with 'more
low-pass filtered post-synaptic membrane potentials'.

Ching-Ming, thus the question is 'Can TMS (or any other intervention)
change the balance between distal and proximal inputs?' This is much
trickier than thinking whether or not neural morphology can change by 60%
in 3 hours.

We can think of synaptic inputs to apical dendrite in superficial layers as
a typical case of 'distal input'. If TMS (or any other intervention such as
hyperventilation) can increase relative amount of 'distal input', that
would shift 1/f to 'steeper' = 'as if GABA_A-R is dominant at the
measurement site'.

What can increase inputs to apical dendrite? I published a paper last year
that extralemniscal thalamic (EXLEM) projection goes to the superficial
layers (about 25%; See 'type 2' in this illustration
https://urldefense.com/v3/__https://github.com/sccn/OneOverF/discussions/17__;!!Mih3wA!DHtN8b_WLfq9LU0kcatC6qilNfGVpUWCoDhdGUr5gz4qcT4adsUlFefUMcDmaZVmsSJ6ai6Kq6mOCrDe05dAARwz5uE$ ). EXLEM is the same as
'non-specific thalamus' mentioned by Grey Walter, Robert Galambos etc..
Recently, Giandomenico Iannetti has been leading the revival of EXLEM.

Yevgeny, your reviewer will be angry and tell you you should never call a
study with n=167 preliminary. Thank you for sharing your results! It is
good to know that 1/f is not sensitive to hyperventilation.

Cedric, thanks for your clarification of 'why AMPA' question. I have not
investigated it in full depth, but I'm sure Brad Voytek and other pioneers
had some reason they picked it up specifically. I imagine AMPA
receptor-derived membrane potentials are dominant from scalp M/EEG's
perspective.

Makoto

On Tue, Apr 14, 2026 at 9:48 PM Cedric Cannard via eeglablist <
eeglablist at sccn.ucsd.edu> wrote:

>
> Hi Makoto and everyone,
>
> I won't be able to attend unfortunately. One thing I am curious about is
> why we mainly focus on AMPA receptors as the excitatory model. I am no
> expert here, but from what I recall from undergrad classes, NMDA receptors
> are the dominant glutamate receptors in the human brain, so it feels like
> they should probably be included if we want to better understand the
> aperiodic background at the neuronal level?
>
> According to an AI-assisted exploration (to be fact-checked), AMPA
> receptors are simpler to model because they are fast and Ca2+-impermeable,
> with stereotypical decay kinetics that map cleanly onto cable theory. NMDA,
> on the other hand, has a voltage-dependent Mg2+ block that introduces
> nonlinearities, which is why it tends to appear in more sophisticated
> compartmental models combining Hodgkin-Huxley conductances with
> multi-compartment cable structure (
> https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/8684467/__;!!Mih3wA!FIzLKzvJ2yvgcESdkJKVn6WMFR279-nFGjfNcMr1mO_rEcMhkKcRqWkebEG4KRMHmf4jQgYzj18GHSUE8URMHPkFbA$
> ;
> https://urldefense.com/v3/__https://pmc.ncbi.nlm.nih.gov/articles/PMC10600871/__;!!Mih3wA!FIzLKzvJ2yvgcESdkJKVn6WMFR279-nFGjfNcMr1mO_rEcMhkKcRqWkebEG4KRMHmf4jQgYzj18GHSUE8UQ7a9S8hw$
> ).
> I believe Eugen and Makoto (and others) have already discussed on Github
> the possibility that one of the mechanisms underlying 1/f-ness is
> electrodiffusive, related to ionic diffusion in the extracellular medium
> and how voltage signals filter through brain tissue (
> https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/17025932/__;!!Mih3wA!FIzLKzvJ2yvgcESdkJKVn6WMFR279-nFGjfNcMr1mO_rEcMhkKcRqWkebEG4KRMHmf4jQgYzj18GHSUE8UQL2GLiYA$
> ;
> https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/19348744/__;!!Mih3wA!FIzLKzvJ2yvgcESdkJKVn6WMFR279-nFGjfNcMr1mO_rEcMhkKcRqWkebEG4KRMHmf4jQgYzj18GHSUE8URLWVnFGQ$
> ).
> Including NMDA feels relevant here because its slow, voltage-dependent
> conductance would extend the source signal spectrum, while electrodiffusion
> shapes it on the propagation side.
>
> Specifically:
> - Multi-timescale kinetics. NMDA decay constants (~50-500 ms for NR2B)
> combined with AMPA (~5 ms) and GABA-B (~200 ms) naturally produce
> power-law-like spectra over a wider frequency range via superposition of
> Lorentzian processes, making the 1/f approximation more robust (
> https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/15483395/__;!!Mih3wA!FIzLKzvJ2yvgcESdkJKVn6WMFR279-nFGjfNcMr1mO_rEcMhkKcRqWkebEG4KRMHmf4jQgYzj18GHSUE8UQnALGmEQ$
> ).
> - Distal dendritic enrichment. NMDA receptors are concentrated on apical
> dendrites where cable filtering is strongest, potentially producing
> location-specific spectral signatures.
> - Tonic NMDA activity. In high-conductance background states realistic in
> vivo, membrane fluctuations may regularly push voltage past the Mg2+
> unblock threshold, contributing a slowly fluctuating conductance noise
> critical for the low-frequency end of the 1/f spectrum.
> - Interaction with HCN (Ih) channels. Both are voltage-dependent and
> co-localized in distal apical dendrites (
> https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/25609619/__;!!Mih3wA!FIzLKzvJ2yvgcESdkJKVn6WMFR279-nFGjfNcMr1mO_rEcMhkKcRqWkebEG4KRMHmf4jQgYzj18GHSUE8USX3x02ZA$
> ), so their interaction would probably be unavoidable in any realistic
> model.
>
> Anyways, that's AI-heavy so to judge with caution, but it feels like there
> may be some useful food for thought in here.
>
>
> Cedric
>
>
>
> Sent with Proton Mail secure email.
>
> On Tuesday, April 14th, 2026 at 9:59 AM, Gin Estrella Cruz via eeglablist <
> eeglablist at sccn.ucsd.edu> wrote:
>
> > Dear all,
> >
> > Thank you both -- these are very helpful perspectives.
> >
> > I should say that I'm still quite new to 1/f analysis itself but very
> > interested in it because of my broader interest in multiscale brain
> > analysis, how synaptic /circuit-level mechanisms may relate to
> larger-scale
> > signals.
> >
> > I think the shift away from asking which mechanism is the single
> "correct"
> > one toward asking whether different mechanisms may matter differently
> > depending on recording scale, brain state, or experimental context is
> quite
> > useful in this discussion.
> >
> > Experimental ideas: These make me wonder whether a good near-term step
> > might be to start with existing datasets that compare scales or states,
> > e.g. scalp EEG vs more local recordings, or wake/sleep/anesthesia
> > transitions, just to see which effects seem large enough to pursue more
> > directly.
> >
> > Longitudinal rTMS: Very interesting result. A large exponential shift
> over
> > only a few hours does seem to suggest that at least part of the 1/f
> > structure is dynamically changing vs being explained only by fixed
> > structural properties. At the same time, could such a shift still reflect
> > several factors at once, e.g. inhibition, arousal, oscillatory changes,
> or
> > broader network reorganization, instead of one mechanism alone?
> >
> > Would it make sense to think of this in two stages: (1) use available
> > longitudinal or cross-scale data to see where the strongest effects seem
> to
> > be, and then (2) build a more focused modeling or experimental comparison
> > around those candidate mechanisms?
> >
> > Warm regards,
> >
> > GIn
> >
> > On Mon, Apr 13, 2026 at 9:11 PM Ching-Ming Lee via eeglablist <
> > eeglablist at sccn.ucsd.edu> wrote:
> >
> > > Hello Makoto and the 1/f community,
> > >
> > > Inspired by this discussion, I would like to share some empirical data
> that
> > > might help quantify the relative contributions of cable theory versus
> E/I
> > > balance.
> > >
> > > As a physicist working on materials science and EEG, I conducted an
> N-of-1
> > > longitudinal study on a 65-year-old subject (a senior physicist). We
> > > delivered four consecutive sessions of alpha-tuned rTMS (totaling 8,600
> > > pulses) within 3.5 hours and monitored the state evolution.
> > >
> > > *Key Findings:*
> > >
> > >    1.
> > >
> > >    *Dramatic Exponent Shift:* We observed the 1/f exponent (FOOOF)
> drifting
> > >    from ~1.2 (baseline) up to ~1.8 at Stage 4.
> > >    2.
> > >
> > >    *Structural Stability vs. Dynamic Change:* Since the subject's
> neuronal
> > >    morphology (cable theory properties) cannot change by 60% within 3
> > > hours,
> > >    this massive shift provides strong evidence that *E/I balance
> > >    (GABA-mediated inhibition)* is the primary driver of 1/f *dynamics*,
> > >    even if cable theory sets the *baseline*.
> > >    3.
> > >
> > >    *The "Collapse" Threshold:* Our WPLI network analysis shows that
> while
> > >    the "Small-World" topology optimizes initially, it undergoes a
> *"Network
> > >    Collapse"* beyond a certain exponent threshold (Stage 5), where
> global
> > >    efficiency drops and local clustering disintegrates.
> > >
> > > *Implications for the Discussion:* This data supports the idea that
> > > 1/f-ness is not just a passive physical property but a dynamic
> biomarker of
> > > functional boundaries. It also challenges the clinical "more is better"
> > > approach in TMS therapy, suggesting that we can push the brain into an
> > > over-inhibited disordered phase.
> > >
> > > These findings are part of a manuscript currently in preparation.
> > >
> > > I look forward to discussing how we might use such longitudinal
> > > "perturbation" data to weigh the factors Makoto and DeepSeek mentioned.
> > >
> > > Best regards,
> > >
> > > Ching-Ming Lee
> > >
> > > Graduate School of Materials Science
> > >
> > > National Yunlin University of Science and Technology
> > >
> > > Евгений Машеров via eeglablist <eeglablist at sccn.ucsd.edu> 於
> 2026年4月13日週一
> > > 上午9:38寫道：
> > >
> > > > This is wonderful.
> > > > I tried to devise an experiment that would allow me to choose
> between the
> > > > hypotheses. Due to my laziness, I asked DeepSeek about it, but he
> > > suggested
> > > > what was probably a wonderful plan, but it required methods
> unavailable
> > > to
> > > > me (perhaps I could interest colleagues from another institute, but
> the
> > > > chances are slim).
> > > > Here's his advice:
> > > > "Excellent question. This moves the discussion from theory to
> > > experimental
> > > > testing.
> > > >
> > > > The main problem is that in real EEG/LFP, all four mechanisms act
> > > > simultaneously. The task is not to choose "the one true one" but to
> > > > estimate the relative contribution of each in a specific context
> (type of
> > > > activity, brain region, state, species, age).
> > > >
> > > > Below is one "clean" experiment (or type of analysis) for testing the
> > > > primacy of each hypothesis. The gold standard is to compare model
> > > > predictions with data, manipulating only one parameter at a time.
> > > >
> > > > 1. Testing hypothesis 2.1 (spatial averaging / source size)
> > > > What experiment: Simultaneous scalp EEG + intracortical LFP
> (high-density
> > > > linear array) in the same animal/human (intraoperatively).
> > > >
> > > > Logic:
> > > >
> > > > LFP registers local sources with a radius of ~0.5–2 mm — here, the
> mutual
> > > > cancellation between spatially separated generators is minimal.
> > > >
> > > > Scalp EEG is the result of averaging over areas of several cm².
> > > >
> > > > Prediction: If spatial averaging is the main contributor to the 1/f
> > > slope:
> > > >
> > > > In LFP, the spectral slope will be significantly flatter (closer to
> > > > 1/f⁰…¹) compared to EEG.
> > > >
> > > > The difference in slope (EEG minus LFP) will be positive and
> correlate
> > > > with the size of the coherent region, estimated via cross-coherence
> on
> > > LFP.
> > > >
> > > > Control: If there is no difference, then the 1/f on the scalp is
> mainly
> > > > shaped by local membrane/synaptic mechanisms (2.2, 2.3).
> > > >
> > > > 2. Testing hypothesis 2.2 (cable theory / dendritic filtering)
> > > > What experiment: Patch-clamp from the soma + simultaneous activation
> of
> > > > distal and proximal dendritic synapses (optogenetics or focal
> glutamate
> > > > stimulation) in a single neuron in slice / in vivo.
> > > >
> > > > Logic:
> > > >
> > > > Stimulate proximal synapses (near the soma) — record EPSP at the
> soma.
> > > >
> > > > Stimulate distal synapses (on the same neuron) — record EPSP at the
> soma.
> > > >
> > > > Compare the spectra of these two response types.
> > > >
> > > > Prediction: If cable filtering is significant:
> > > >
> > > > EPSPs from distal inputs will have a steeper 1/f roll-off (less
> > > > high-frequency power) compared to proximal ones.
> > > >
> > > > The peak amplitude (20–50 Hz) will be significantly reduced.
> > > >
> > > > Key experiment: Do the same in the presence of separate AMPA and
> GABA-A
> > > > blockers to eliminate their kinetics. The remaining difference is
> purely
> > > > passive filtering.
> > > >
> > > > Quantitative check: Compare with a cable model (Rall, NEURON) —
> match the
> > > > spectral slope at different distances.
> > > >
> > > > 3. Testing hypothesis 2.3 (AMPA vs GABA-A kinetics)
> > > > What experiment: Dynamic pharmacological manipulation (and
> optogenetics
> > > > with different kinetics) at the same LFP site.
> > > >
> > > > Logic:
> > > >
> > > > Block GABA-A (bicuculline) — get predominance of fast AMPA-EPSPs.
> > > >
> > > > Block AMPA (CNQX) — leave slow GABA-A-IPSPs.
> > > >
> > > > Then restore the original balance.
> > > >
> > > > Compare the 1/f slope (2–100 Hz range).
> > > >
> > > > Prediction (per Gao & Donoghue 2016):
> > > >
> > > > AMPA-dominant → flatter spectrum (weak 1/f dependence).
> > > >
> > > > GABA-A-dominant → steeper spectrum (strong 1/f dependence).
> > > >
> > > > Critical control: Simultaneously monitor spatial coherence (to rule
> out
> > > > mechanism 2.1) and vary stimulation distance from soma (to rule out
> 2.2).
> > > >
> > > > If effect exists — receptor kinetics make an independent
> contribution.
> > > > If no effect — the 1/f slope is not primarily determined by kinetics
> > > > (unlikely but possible).
> > > >
> > > > 4. Testing hypothesis 2.4 (slow ion concentration / open-loop
> current)
> > > > What experiment: Long-duration (>10–20 min) recording of LFP +
> > > > ion-selective microelectrodes (K⁺, Ca²⁺) in cortex without external
> > > > stimulation (spontaneous activity).
> > > >
> > > > Logic:
> > > >
> > > > Standard microelectrodes record high-frequency LFP (0.5–200 Hz).
> > > >
> > > > Ion-selective electrodes give extracellular K⁺ concentration with
> ~0.1–1
> > > > Hz temporal resolution.
> > > >
> > > > Prediction: If slow ionic shifts affect 1/f:
> > > >
> > > > The spectral slope in the 0.05–1 Hz range will correlate with K⁺
> > > > concentration.
> > > >
> > > > When the "closed-loop" current is disrupted (e.g., via Na⁺-K⁺-ATPase
> > > > blockade with ouabain, or during hypoxia), the 1/f slope will first
> > > change,
> > > > then return with a time constant of tens of seconds to minutes.
> > > >
> > > > Key test: Look at 40 Hz ASSR in two groups (red vs blue, as in your
> > > > example) — if the difference in 1/f is at frequencies <<1 Hz but not
> at
> > > >10
> > > > Hz, that supports 2.4. If the difference is at high frequencies, that
> > > > points to 2.1–2.3.
> > > >
> > > > Summary: which single experiment is the most powerful for
> disentangling
> > > > these?
> > > > There isn't one. But there is a combinatorial protocol that could
> yield a
> > > > weighted contribution:
> > > >
> > > > Step    Manipulation    Tests
> > > > 1       Scalp EEG vs intracortical LFP  2.1
> > > > 2       Proximal vs distal responses (optogenetics)     2.2
> > > > 3       Pharmacology (CNQX + bicuculline) at same LFP site      2.3
> > > > 4       Long-term recording + ion-selective electrodes + ouabain
> > > 2.4
> > > > If after step 2 (distance control) the pharmacological effect
> remains —
> > > > 2.3 is real.
> > > > If after step 1 (accounting for source size) the 1/f almost
> disappears —
> > > > 2.1 is the main contributor.
> > > > If slope changes appear only in the very low-frequency range (<<1
> Hz) —
> > > > that's 2.4.
> > > >
> > > > This is a complex experiment, but it is already being partially
> performed
> > > > in neurophysiology using optogenetics and high-density probe arrays."
> > > >
> > > > In the meantime, I'll try to come up with a way to differentiate
> between
> > > > the two using existing EEG recordings of people in different states.
> I'm
> > > > not sure it will work, but maybe it will be informative.
> > > >
> > > > Eugen Masherov
> > > >
> > > >
> > > >
> > > > > Hello 1/f people,
> > > > >
> > > > > Gin Estrella Cruz and I will meet online to discuss whether it is
> > > > possible
> > > > > to quantitatively compare the contributions of cable theory and
> > > AMPA/GABA
> > > > > (i.e., E/I) balance to EEG's 1/f power distribution. At this
> point, I
> > > do
> > > > > not know whether such a comparison is feasible. Anyone is welcome
> to
> > > join
> > > > > if the timing works, I'd love to hear your opinions and advice.
> Since
> > > > there
> > > > > is a 12-hour time difference between us, the meeting time is a bit
> > > > awkward.
> > > > >
> > > > > *Time*: Apr 15, 2026 08:00 AM Eastern Time (US and Canada, EDT)
> > > > > *Place*:
> > > > >
> > > >
> > >
> https://urldefense.com/v3/__https://ucsd.zoom.us/j/3026035468?pwd=bQg61iUIe0AHfDQ5QSipOSEXi4FzCs.1&omn=97531862557__;!!Mih3wA!FZmTaWWKaDH-9ElW5sUmquTyS4E1TAy3KcNOWuur9efjaocNW7F7lNBCzhmS1d1FL9tA6L-gHGo2EbexRI-rf5m6dcE$
> > > > > *Meeting ID*: 302 603 5468
> > > > > *Password*: 1overF
> > > > >
> > > > > To provide a bit of background, since the publication of the
> original
> > > > FOOOF
> > > > > paper, the idea that a flatter or steeper 1/f slope in the EEG
> power
> > > > > spectral density reflects excitatory/inhibitory states appears to
> have
> > > > been
> > > > > overgeneralized. However, the 1/f-like behavior of EEG is an
> intrinsic
> > > > > property predicted by cable theory. If there are two contributors
> (at
> > > > > least) to 1/f-ness, they should be compared quantitatively to
> determine
> > > > > which contributes more.
> > > > >
> > > > > Makoto
> > > > > _______________________________________________
> > > > > To unsubscribe, send an empty email to
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> > >
> > >
> > >
> > > --
> > > 李景明
> > > _______________________________________________
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