Pith Number

pith:C33XZ6OJ

pith:2026:C33XZ6OJ63VTF7YNJBZ5LXOZCL

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From Chaos to Synchrony in Recurrent Excitatory-Inhibitory Networks with Target-Specific Inhibition

Alessia Annibale, Carles Martorell, Miguel A. Mu\~noz, Rub\'en Calvo

Target-specific inhibition organizes recurrent excitatory-inhibitory networks into three distinct dynamical regimes of quiescence, asynchronous chaos, or persistent activity with either synchronous chaos or coherent oscillations.

arxiv:2605.14916 v1 · 2026-05-14 · cond-mat.dis-nn

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Claims

C1strongest claim

Target-specific inhibition organizes the phase diagram into three qualitative classes: inhibition-dominated or strictly balanced networks display only quiescent activity and asynchronous chaos; excitation-dominated networks display persistent activity together with either synchronous chaos with non-vanishing mean activity or coherent oscillations, depending on the stability-matrix eigenvalues. Crucially, coherent oscillations do not coexist with chaotic fluctuations around the periodic mean trajectory.

C2weakest assumption

That dynamical mean-field theory in the large-N limit accurately captures the macroscopic statistics and stability criteria for finite networks with the chosen target-specific inhibitory couplings and broken E-I balance.

C3one line summary

Target-specific inhibition in E-I recurrent networks creates three dynamical classes: quiescent or asynchronous chaos in balanced cases, and persistent activity with either synchronous chaos or coherent oscillations in excitation-dominated cases, where oscillations suppress chaos.

References

79 extracted · 79 resolved · 2 Pith anchors

[1] E. R. Kandel (Ed.), Principles of neural science, 4th Edition, McGraw-Hill, New York, NY , 2000 2000

[2] P. Dayan, L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neu- ral Systems, MIT Press, 2005 2005

[3] Kistler, Richard Naud, and Liam Paninski.Neuronal Dy- namics: From Single Neurons to Networks and Models of Cognition 2014 · doi:10.1017/cbo9781107447615

[4] E. M. Izhikevich, Dynamical systems in neuro- science: the geometry of excitability and burst- ing, Computational neuroscience, MIT Press, Cam- bridge, Mass, 2007 2007

[5] E Lindo, and M Pachitariu 2019 · doi:10.1038/s41586-019-1346-5

Receipt and verification

First computed	2026-05-17T23:38:55.709612Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

16f77cf9c9f6eb32ff0d4873d5ddd912fce9cf7df32c16433598d061c53962a5

Aliases

arxiv: 2605.14916 · arxiv_version: 2605.14916v1 · doi: 10.48550/arxiv.2605.14916 · pith_short_12: C33XZ6OJ63VT · pith_short_16: C33XZ6OJ63VTF7YN · pith_short_8: C33XZ6OJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/C33XZ6OJ63VTF7YNJBZ5LXOZCL \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 16f77cf9c9f6eb32ff0d4873d5ddd912fce9cf7df32c16433598d061c53962a5

Canonical record JSON

{
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    "abstract_canon_sha256": "3c92eaa8fe9cd4d93469abb84a225320b8b659cfab17589d5c9871cbb74a99ff",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cond-mat.dis-nn",
    "submitted_at": "2026-05-14T14:50:38Z",
    "title_canon_sha256": "47793c78accf21bfc73ed3b9ee7d77fbbb27d85a99a26767df06b047c22e7f65"
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  "source": {
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}