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Learning Biophysical Models of Large-Scale Multineuronal Data to Enable Precise Neurostimulation
Paper Information
- Title: Learning Biophysical Models of Large-Scale Multineuronal Data to Enable Precise Neurostimulation
- Authors: Amrith Lotlikar, Ian Christopher Tanoh, Praful Vasireddy, Andrew Lanpouthakoun, Ramandeep Vilkhu
- arXiv: 2607.04063v1
- Date: 2026-07-05
- Categories: q-bio.NC
Core Problem
Multi-compartment Hodgkin-Huxley (HH) models provide principled neural dynamics prediction but require invasive intracellular recordings for parameter fitting. This limits scalability to large neural populations and prevents capturing cell-specific properties in circuits.
Key Innovation
Framework to infer HH biophysical parameters from extracellular MEA (Multi-Electrode Array) measurements using:
- Differentiable biophysical simulation
- Simulation-based inference
- Designed features of extracellular signals
Methodology
Differentiable Biophysical Simulation
Extracellular MEA Data
↓
Feature Extraction (waveform shape, spike timing, etc.)
↓
Differentiable HH Model Simulation
↓
Gradient-Based Parameter Inference
↓
Predicted Biophysical Parameters
Key Components
-
Feature Engineering: Extract informative features from extracellular recordings:
- Extracellular waveform shapes
- Spike timing patterns
- Population activity features
-
Differentiable Simulation: Make HH model differentiable to enable gradient-based optimization:
- Backpropagation through biophysical equations
- Efficient parameter updates
- Scalable to large neuron populations
-
Simulation-Based Inference: Use simulated data to train inference models:
- Generate training data from known parameters
- Learn mapping: features → biophysical parameters
- Generalize to real experimental data
Applications
Predicting Neurostimulation Responses
Central translational neuroengineering goal: predict neural spiking responses to electrical stimulation
Use cases:
- Optimize stimulation parameters for therapeutic effect
- Minimize side effects by predicting off-target activation
- Personalize deep brain stimulation (DBS) protocols
- Design closed-loop stimulation systems
Large-Scale Circuit Modeling
- Fit HH parameters for hundreds of neurons simultaneously
- Capture cell-type specific properties
- Build biologically realistic circuit models
- Enable in-silico testing of interventions
Technical Advantages
| Traditional Approach | This Framework |
|---|---|
| Intracellular recordings (invasive) | Extracellular MEA (scalable) |
| Single-cell fitting | Population-scale inference |
| Manual parameter tuning | Automated gradient-based optimization |
| Limited to simple models | Full multi-compartment HH models |
Implementation Notes
- Simulation framework: Differentiable HH model
- Inference method: Simulation-based inference with gradient optimization
- Data source: High-density MEA recordings
- Output: Biophysical parameters (conductances, time constants, morphology)
Validation
- Predict spiking responses to electrical stimulation
- Match experimental data from MEA recordings
- Generalize across neurons and conditions
Related Work
- Hodgkin-Huxley models (classic biophysics)
- Neural mass models (population-level)
- Differentiable programming in neuroscience
- Simulation-based inference (SBI)
- Brain stimulation optimization
Activation Triggers
- Hodgkin-Huxley, HH model, biophysical modeling
- neurostimulation, DBS, brain stimulation
- differentiable simulation, simulation-based inference
- MEA, multi-electrode array, extracellular recordings
- parameter inference, neural parameter estimation
- computational neuroscience, biophysical parameters