pith:5AKN3BQJ
Embodied Neurocomputation: A Framework for Interfacing Biological Neural Cultures with Scaled Task-Driven Validation
Biological neural networks outperform optimized silicon agents in navigation tasks through optimized interfacing parameters.
arxiv:2605.13315 v1 · 2026-05-13 · cs.ET · cs.LG · cs.NE · cs.SY · eess.SY · q-bio.NC
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\usepackage{pith}
\pithnumber{5AKN3BQJ6X7UZ53ZAHM7LQ62EU}
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Record completeness
Claims
These configurations achieved significantly higher task performances than optimized silicon-based DQN agents under the same interaction budget.
That the simulated grid-world with odor-style gradient sufficiently captures the relevant dynamics of real biological neural cultures for the optimization to translate outside the model.
A new framework optimizes biological neural network interfaces and identifies 12 configurations that outperform DQN agents in simulated navigation under equal interaction budgets.
References
Formal links
Receipt and verification
| First computed | 2026-05-18T02:44:48.789348Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
e814dd8609f5ff4cf77901d9f5c3da2537218fce6dfcdc2ca42fda27c5bd06a4
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/5AKN3BQJ6X7UZ53ZAHM7LQ62EU \
| 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: e814dd8609f5ff4cf77901d9f5c3da2537218fce6dfcdc2ca42fda27c5bd06a4
Canonical record JSON
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