sbtg-neural-circuit-inference - SKILL.md Agent Skill

name: sbtg-neural-circuit-inference version: v1.0.0 last_updated: 2026-05-05 description: "Score-Block Time Graphs (SBTG) methodology for inferring lag-specific directed neural circuit interactions from population activity data. Uses denoising score models and cross-block score products to recover the Jacobian of transition maps under nonlinear dynamics. Based on arXiv:2605.02852 (Kinger et al., 2026)." category: ai_collection

Score-Block Time Graphs (SBTG) — Neural Circuit Inference

Description

SBTG is a method for inferring directed, lag-specific neural circuit interactions from sampled neural population activity under varying stimuli, without assuming a parametric form for the underlying dynamics. It leverages denoising score models to estimate joint-window scores over consecutive brain state snapshots and converts these into calibrated, directed edge tests via cross-block score products.

Paper: Kinger, S., Bertram, J., Dyballa, L. et al. "Inferring Active Neural Circuits Using Diffusion Scores." arXiv:2605.02852 (2026).

Activation Keywords

sbtg
score-block time graphs
neural circuit inference
diffusion score neural
directed connectivity neural
lag-specific interactions
神经回路推断
扩散分数
brain state transition

Core Methodology

Key Insight

Cross-block score products recover the Jacobian of the transition map between brain states under nonlinear dynamics:

Score products → directed edge weights
Jacobian → lag-specific interaction strengths
Multi-block windows → conditioning on intermediate time points to avoid omitted-lag bias

Workflow

Step 1: Data Preparation

Input: Time series of neural population activity (e.g., calcium imaging, electrophysiology)
Format: N neurons × T time points matrix
Preprocess: Normalize, detrend, optionally smooth

Step 2: Train Denoising Score Model

Train a denoising diffusion model on the neural state space
The score model learns: s(x) = ∇_x log p(x)
Use noise-conditioned score matching (NCSM) or denoising score matching (DSM)

Step 3: Estimate Joint-Window Scores

Define time windows of consecutive activity snapshots
Estimate scores over joint windows: s(x_t, x_{t+1}, ..., x_{t+k})
This captures multi-time-point dependencies

Step 4: Cross-Block Score Products

Compute cross-block score products between time windows
Product = E[s(x_{t→t+1}) · s(x_{t+1→t+2})^T]
This recovers the Jacobian J = ∂x_{t+1}/∂x_t

Step 5: Multi-Block Window Conditioning

Use minimal multi-block windows to separate lag-specific effects
Condition on intermediate time points to avoid pairwise analysis bias
This isolates direct vs. indirect interactions

Step 6: Directed Edge Tests

Convert Jacobian estimates to calibrated, directed edge tests
Apply statistical significance testing
Build directed connectivity graph

Step 7: Validation

Compare with known connectomes (e.g., electron microscopy)
Check cell-type-specific temporal organization
Verify neuromodulatory profiles against known receptor kinetics

Implementation Notes

Score Model Architecture

Use a neural network (MLP or transformer) for the score model
Input: noisy neural state vector
Output: score vector (gradient of log density)
Train with denoising score matching objective

Cross-Block Product Computation

# Pseudocode for cross-block score product
def cross_block_score_product(score_model, states, lag=1):
    """Compute cross-block score product for lag-specific interactions."""
    scores_t = score_model(states[:-lag])
    scores_tplus = score_model(states[lag:])
    # Cross-product recovers Jacobian
    jacobian_estimate = np.mean(scores_t[:, None] * scores_tplus[None, :], axis=0)
    return jacobian_estimate

Advantages Over Existing Methods

Non-parametric: No assumption about functional form of dynamics
Lag-specific: Separates direct from indirect interactions
Calibrated: Statistical tests for edge significance
Validated: Successfully applied to C. elegans whole-brain calcium imaging

Applications

Whole-brain circuit mapping (C. elegans, zebrafish, mouse)
Cell-type-specific temporal organization analysis
Neuromodulatory pathway identification
Dynamic functional connectivity estimation
AI for science: turning population recordings into testable hypotheses

Resources

Paper: https://arxiv.org/abs/2605.02852
PDF: https://arxiv.org/pdf/2605.02852
Related: denoising score matching, neural ODEs, dynamic causal modeling

Related Skills

odebrain-continuous-eeg-graph (continuous-time neural dynamics)
hermes-brain-connectivity (brain connectivity analysis toolkit)
neural-dynamics-universal-translator (neural dynamics alignment)
time-varying-brain-connectivity (dynamic brain networks)