Pith Number

pith:RYXCHBIF

pith:2026:RYXCHBIF3GERRV4RPXDR3ZLMAM

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Response time of lateral predictive coding and benefits of modular structures

Guanghui Cai, Hai-Jun Zhou, Weikang Wang, Zhen-Ye Huang

Optimal lateral predictive coding networks can minimize response time to near the theoretical lower bound while keeping predictive error and signal robustness unchanged, and modular structures with fewer connections perform equivalently to全

arxiv:2604.20524 v1 · 2026-04-22 · q-bio.NC · cond-mat.dis-nn · cs.NE

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Claims

C1strongest claim

We find that the characteristic response time of the LPC system can be minimized to closely approaching the lower-bound value without compromising the mean predictive error (energetic cost) and the information robustness of signal transmission. We further demonstrate that optimal LPC networks taking a modular structural organization with extensively reduced number of lateral interactions are equally excellent as all-to-all completely connected networks, in terms of feature detection performance, response time, energetic cost and information robustness.

C2weakest assumption

That adjustments to the recurrent dynamical system can reduce response time to near the lower bound while leaving mean predictive error and information robustness unchanged, and that modular reductions in lateral interactions preserve all performance metrics equivalently to full connectivity.

C3one line summary

Optimal LPC networks achieve near-minimal response times without trade-offs in energetic cost or robustness, and modular structures with reduced lateral connections match all-to-all networks in performance.

References

31 extracted · 31 resolved · 0 Pith anchors

[1] Retrieval capabilities of hierarchical networks: From Dyson to Hopfield 2015 · doi:10.1103/physrevlett.114.028103

[2] Predictive coding is a consequence of energy efficiency in recurrent neural networks 2022 · doi:10.1016/j.patter.2022.100639

[3] doi:10.1016/j.neunet.2012.06.003 2012 · doi:10.1016/j.neunet.2012.06.003

[4] Canonical microcircuits for predictive coding 2012 · doi:10.1016/j.neuron.2012.10.038

[5] Bell and Terrence J 1995 · doi:10.1162/neco.1995.7.6.1129

Receipt and verification

First computed	2026-07-08T01:19:13.233870Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8e2e238505d98918d7917dc71de56c033d87c494aa3ddff53d7ba5ddda9f040d

Aliases

arxiv: 2604.20524 · arxiv_version: 2604.20524v1 · doi: 10.48550/arxiv.2604.20524 · pith_short_12: RYXCHBIF3GER · pith_short_16: RYXCHBIF3GERRV4R · pith_short_8: RYXCHBIF

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/RYXCHBIF3GERRV4RPXDR3ZLMAM \
  | 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: 8e2e238505d98918d7917dc71de56c033d87c494aa3ddff53d7ba5ddda9f040d

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "9c5f3f85c3bc7825d396ae3939aaba22a9c342185cbf756caaebe78e370ccdbc",
    "cross_cats_sorted": [
      "cond-mat.dis-nn",
      "cs.NE"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "q-bio.NC",
    "submitted_at": "2026-04-22T13:02:33Z",
    "title_canon_sha256": "818e7dee5cd19b12e11fb87d269a8940ed22015d2db5cade556c00a4c1d4e052"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.20524",
    "kind": "arxiv",
    "version": 1
  }
}