Pith Number

pith:4JDXVSDA

pith:2026:4JDXVSDAYKYPVE6AMWQAFVTIIW

not attested not anchored not stored refs resolved

FiTS: Interpretable Spiking Neurons via Frequency Selectivity and Temporal Shaping

Jongmin Choi, Joon Son Chung

Spiking neurons improve in simple feedforward networks when each one separately selects a target frequency and then adjusts its timing contribution through group delay.

arxiv:2605.13071 v1 · 2026-05-13 · cs.NE

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\pithnumber{4JDXVSDAYKYPVE6AMWQAFVTIIW}

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

FiTS consistently improves over a plain Leaky Integrate-and-Fire (LIF) baseline in simple feedforward SNNs without recurrence or network-level delays, while remaining competitive with strong temporal SNN baselines. The learned target frequencies and group-delay shifts provide interpretable neuron-level summaries.

C2weakest assumption

That factorizing temporal computation into an FS module (target frequency as maximizer of subthreshold magnitude response) and a TS module (group-delay modulation) will produce effective specialization and performance gains without requiring additional network mechanisms or post-hoc tuning.

C3one line summary

FiTS spiking neurons improve auditory task performance over LIF baselines by factorizing computation into frequency selectivity and group-delay-based temporal shaping, yielding interpretable per-neuron parameters.

References

45 extracted · 45 resolved · 1 Pith anchors

[1] A low power, fully event-based gesture recognition system 2017

[2] Advancing spatio-temporal processing in spiking neural networks through adaptation 2024

[3] Long short-term memory and learning-to-learn in networks of spiking neurons 2018

[4] Alexandre Bittar and Philip N. Garner. A surrogate gradient spiking baseline for speech command recognition.Frontiers in Neuroscience, V olume 16 - 2022, 2022 2022

[5] PMSN: A parallel multi-compartment spiking neuron for multi-scale temporal processing 2024

Receipt and verification

First computed	2026-05-18T03:08:58.864669Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e2477ac860c2b0fa93c065a002d66845a025504d20d6bb4c315ca0412c1c0d61

Aliases

arxiv: 2605.13071 · arxiv_version: 2605.13071v1 · doi: 10.48550/arxiv.2605.13071 · pith_short_12: 4JDXVSDAYKYP · pith_short_16: 4JDXVSDAYKYPVE6A · pith_short_8: 4JDXVSDA

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4JDXVSDAYKYPVE6AMWQAFVTIIW \
  | 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: e2477ac860c2b0fa93c065a002d66845a025504d20d6bb4c315ca0412c1c0d61

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4ca50fb213764521c0f76447be61a7216e4495c4b701d46fd3feff5a0562f707",
    "cross_cats_sorted": [],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.NE",
    "submitted_at": "2026-05-13T06:42:40Z",
    "title_canon_sha256": "2caec38af893ba856ab243fd56cbd5461a7c616eab878db7d6f3e6a5cb913382"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13071",
    "kind": "arxiv",
    "version": 1
  }
}