---
id: "9df551f7-211a-430c-95ea-05796220760e"
canonical_url: "https://rigorloop.com/claims/9df551f7-211a-430c-95ea-05796220760e"
json_url: "https://rigorloop.com/api/v1/claims/9df551f7-211a-430c-95ea-05796220760e"
title: "Evaluate thermodynamic amplification in a critical neural-network model"
status: "open"
field: "Computational neuroscience / statistical physics"
review_type: "paper_review"
authorship: "human_ai_assisted"
expert_reward_amount: "250.00"
currency: "USD"
review_window_days: 14
published_at: "2026-08-02T21:16:34.280Z"
updated_at: "2026-08-02T21:16:34.281Z"
requested_deliverables:
  - "Written review memo"
  - "Prior-art map"
  - "Code or reproduction package"
public_materials:
  - label: "CRCNS HC-3 dataset and documentation"
    url: "https://crcns.org/data-sets/hc/hc-3/about-hc-3"
---

# Evaluate thermodynamic amplification in a critical neural\-network model

> This project proposes that a small perturbation applied to highly connected nodes near a neural network’s critical point can produce a much larger system\-wide response\. Small\-world Ising\-network simulations report substantial changes in network magnetization after biasing approximately 10% of the highest\-degree nodes\. The model’s avalanche behavior was compared with derived neural\-avalanche measurements from six selected sessions in the CRCNS HC\-3 rat hippocampal dataset\. This Research Bounty seeks an independent technical assessment of whether the reported amplification represents a meaningful mechanism beyond the expected sensitivity of a critical network, how strongly the computational evidence supports that interpretation, and what experiment would most decisively test it\.

## Research Bounty details

- **Status:** open
- **Field:** Computational neuroscience / statistical physics
- **Review type:** paper\_review
- **Authorship:** human\_ai\_assisted
- **Expert reward:** USD 250.00
- **Review window:** 14 days

## Requested deliverables

- Written review memo
- Prior\-art map
- Code or reproduction package

## Public materials

- CRCNS HC\-3 dataset and documentation: <https://crcns.org/data-sets/hc/hc-3/about-hc-3>

Canonical HTML: <https://rigorloop.com/claims/9df551f7-211a-430c-95ea-05796220760e>