pith:5D334NPE
Predictive Coding Light+: learning to predict visual sequences with spike timing-dependent plasticity and synaptic delays
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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\usepackage{pith}
\pithnumber{5D334NPEHHOZKOH34UGMEGNC66}
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Record completeness
Claims
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.
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.
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
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
· · · · ·Agent API
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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"primary_cat": "q-bio.NC",
"submitted_at": "2026-05-12T20:34:25Z",
"title_canon_sha256": "a5f7b5f594506491b3cbd6219f9f2c9cec0e6255dfe2f888ba5018159a54ac37"
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