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pith:5D334NPE

pith:2026:5D334NPEHHOZKOH34UGMEGNC66
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Predictive Coding Light+: learning to predict visual sequences with spike timing-dependent plasticity and synaptic delays

Antony W. N'dri, C\'eline Teuli\`ere, Jochen Triesch, Thomas Barbier

Spiking neural networks learn recurrent excitatory connections with delays to maintain recent past and predict future visual sequences.

arxiv:2605.12732 v1 · 2026-05-12 · q-bio.NC

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3 Author claim open · sign in to claim
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Claims

C1strongest claim

our work shows how spiking neural networks can learn recurrent excitatory connections with delays to maintain a record of the recent past and successfully predict the future.

C2weakest assumption

That spike timing-dependent plasticity applied to recurrent connections with delays is sufficient to learn and maintain the necessary short-term memory traces for accurate future prediction without additional mechanisms or labeled supervision.

C3one line summary

PCL+ spiking network learns recurrent connections with delays via STDP to retain recent visual inputs and predict future ones, reproducing cortical sequence learning and filling missing data in gesture recognition.

References

42 extracted · 42 resolved · 0 Pith anchors

[1] A low power, fully event-based ges- ture recognition system 2017
[2] Working models of working memory 2014
[3] Spike timing-based unsupervised learning of orienta- tion, disparity, and motion representations in a spiking neural network 2021
[4] PIX2NVS: Parame- terized conversion of pixel-domain video frames to neu- romorphic vision streams 2017
[5] Persistent ac- tivity in the prefrontal cortex during working memory 2003
Receipt and verification
First computed 2026-05-18T03:09:49.229738Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

e8f7be35e439dd9538fbe50cc219a2f78a0e99636dd17a7ab7470dda841d1499

Aliases

arxiv: 2605.12732 · arxiv_version: 2605.12732v1 · doi: 10.48550/arxiv.2605.12732 · pith_short_12: 5D334NPEHHOZ · pith_short_16: 5D334NPEHHOZKOH3 · pith_short_8: 5D334NPE
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/5D334NPEHHOZKOH34UGMEGNC66 \
  | 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: e8f7be35e439dd9538fbe50cc219a2f78a0e99636dd17a7ab7470dda841d1499
Canonical record JSON
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    "abstract_canon_sha256": "e06a5297389ee85fe2703016c423cffda78852c197728493a557f08538f484d9",
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    "primary_cat": "q-bio.NC",
    "submitted_at": "2026-05-12T20:34:25Z",
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