ven-circuit-snn-social-learning

name	ven-circuit-snn-social-learning
description	VENCircuit methodology — Von Economo neurons (VENs) as acquisition scaffolds in recurrent spiking neural networks. Combines computational modeling with clinical predictions for bvFTD and autism. Use when: studying VENs, social learning in SNNs, gradient pathway analysis, clinical prediction from computational models, developmental scaffolding in neural networks.

VENCircuit: VENs as Acquisition Scaffolds in Recurrent SNNs

Methodology from Keskin (2026) arXiv:2605.17399.

Core Finding

Von Economo neurons (VENs) function as acquisition scaffolds — their presence enables reliable convergence in recurrent SNNs (98% vs 70% convergence). VEN ablation causes stochastic learning failure (not mere speed reduction), providing a computational analogue of variable social skill acquisition in autism spectrum conditions.

Key Experimental Design

Network Architecture

• VEN-like projection neurons: K=40, 2% of total neurons
• Recurrent pyramidal circuit
• Trained on binary classification task
• 50 matched random initializations with/without VENs

Phase-Ablation Protocol

1. Train intact network
2. Ablate VENs at different training phases
3. Most disruptive: mid-training (epochs 5-25)
4. Reveals co-adaptive dependency in pyramidal circuit

Formal Account

VENs provide a direct gradient pathway immune to Jacobian instabilities affecting the recurrent circuit. This explains:

• Why VEN loss causes complete learning failure (not slowdown)
• Why mid-training ablation is most disruptive
• Why inference-time ablation causes variable collapse

Spectral Norm Analysis

All networks initialize near the critical gradient-flow boundary:

• σ₂ ≈ 0.078 uniformly across all seeds
• α ≈ 1.028 (spectral growth factor)
• This confirms the gradient advantage is architecturally ubiquitous, not seed-specific

Negative Result: STDP Does NOT Benefit from VENs

Appendix B shows reward-modulated STDP fails to leverage VEN advantage:

• Two-phase protocol: (1) backprop for 20 epochs, (2) freeze input/recurrent, train output via STDP
• No significant difference between VEN-intact and VEN-ablated conditions
• VEN gradient pathway benefit only manifests under surrogate-gradient BPTT
• Implication: VENs solve a credit-assignment problem specific to backprop-style gradients, not local plasticity rules

Clinical Predictions

Condition	VEN Status	Prediction
bvFTD	Selective VEN loss	Stochastic learning failure
ASC	Reduced VENs	Variable social skill acquisition
Organoid models	VEN manipulation	Falsifiable electrophysiology tests

Implementation Pattern

# VENCircuit training loop pattern with surrogate-gradient BPTT
# NOTE: VEN benefit requires surrogate-gradient BPTT; STDP does NOT leverage it
for epoch in range(epochs):
    # Forward pass through recurrent circuit
    hidden, spikes = recurrent_circuit(inputs, hidden)
    
    # VEN gradient pathway (bypasses recurrent Jacobian)
    if ven_enabled:
        ven_output = ven_layer(inputs, hidden)
        loss = compute_loss(ven_output, target)
    else:
        # Degraded gradient through unstable Jacobian
        loss = compute_loss(hidden, target)
    
    # Backprop with VEN-mediated stability (surrogate-gradient)
    loss.backward()

# ARCHITECTURAL SPEC:
# Pyramidal: N=2,000 LIF, τ=20ms, sparse recurrent (p=0.15)
# VEN-like: K=40 (2%), τ=10ms, feedforward-only input (fan-in=8)
# VENs have: (1) direct output projection, (2) faster time constant,
#            (3) feedforward-only connectivity (no recurrent input)

Reproducibility Notes

• 50 random seeds essential for statistical significance
• Fisher's exact test: OR=21.0, 95% CI [2.7, 167], p=8.7e-5
• Inference ablation: Wilcoxon p=0.022
• Performance drop range: minimal to catastrophic (0.989→0.620)

Activation Keywords

ven-circuit, von economo neurons, VEN, social learning SNN, acquisition scaffold, bvFTD computational model, autism computational model, gradient pathway SNN