dmosopt-neural-dynamical-systems

name: dmosopt-neural-dynamical-systems description: "DMOSOPT: Joint surrogate learning framework for multi-objective optimization of neural dynamical systems. Learns objectives, constraints, and sensitivities simultaneously. Activation: multi-objective optimization, neural dynamics, surrogate model, parameter optimization"

DMOSOPT: Multi-Objective Optimization for Neural Dynamical Systems

A scalable optimization framework using jointly learned surrogate models to capture the interplay between objectives, constraints, and parameter sensitivities in biophysical neural simulations.

Metadata

• Source: arXiv:2603.20984
• Authors: [Paper authors]
• Published: 2026-03
• Categories: cs.LG

Core Methodology

Key Innovation

DMOSOPT addresses the challenge of optimizing high-dimensional neural system parameters under numerous constraints by learning a unified surrogate model that simultaneously approximates objectives, feasibility boundaries, and parameter sensitivities, providing gradient signals where traditional methods fail.

Technical Framework

Joint Surrogate Learning

1. Objective landscape approximation: Smooth surrogate for objective functions
2. Feasibility boundary learning: Constraint satisfaction prediction
3. Sensitivity estimation: Per-parameter gradient information
4. Unified gradient: Steers search toward improved objectives AND constraint satisfaction

Optimization Strategy

• Binary feasible/infeasible partition: Handles hard constraints
• Targeted exploration: Uses sensitivity estimates for intelligent parameter updates
• Supercomputing scale: Validated on large-scale neural circuit models
• Fewer evaluations: Achieves optimization with substantially fewer problem evaluations

Application Scope

Neural System Optimization

• Single-cell dynamics parameter fitting
• Population-level network activity tuning
• Multi-scale neural circuit modeling
• Biophysical model calibration

General Applicability

While demonstrated in neuroscience, applicable to any constrained multi-objective optimization in scientific and engineering domains.

Implementation Guide

Prerequisites

• Simulation framework for target dynamical system
• Multi-objective optimization library
• Surrogate modeling capabilities (Gaussian Processes, Neural Networks)

Workflow

1. Define objectives: Quantify what to optimize
2. Specify constraints: Hard and soft constraints
3. Initial sampling: Generate training data for surrogate
4. Joint learning: Train unified surrogate model
5. Guided search: Use unified gradients and sensitivities
6. Iterate: Update surrogate and continue optimization

Key Considerations

• High-dimensional parameter spaces require sufficient initial sampling
• Constraint boundary learning critical for feasibility
• Sensitivity estimates enable efficient exploration
• Surrogate accuracy affects final solution quality

Applications

• Neural model parameter fitting
• Brain network dynamics optimization
• Biophysical simulation calibration
• Control policy optimization
• Engineering design under constraints

Pitfalls

• Surrogate accuracy depends on training data quality
• High-dimensional spaces may require many initial evaluations
• Constraint learning can be challenging for complex feasibility regions
• Computational cost of surrogate training at scale

Related Skills

• learning-neuron-dynamics-deep-snn
• neural-dynamics-universal-translator
• snn-learning-survey
• optimization-neural-networks