| name | silif-dbs-neuromorphic-controller |
| description | Neuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation in Parkinson's Disease - CMOS-implemented SiLIF-DBS controller achieving 5.85%/uW beta suppression efficiency with 75% power reduction vs open-loop |
| tags | ["neuromorphic","deep-brain-stimulation","parkinsons","CMOS","adaptive-dbs","LIF","biomarker"] |
| activation_words | ["SiLIF-DBS","neuromorphic DBS","adaptive deep brain stimulation","Parkinson's","beta suppression","CMOS neuron"] |
| arxiv_id | 2607.05453 |
Neuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation
Paper Info
- Title: Neuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation in Parkinson's Disease
- arXiv: 2607.05453
- Authors: Md Abu Bakr Siddique, Jakub Orłowski, Yan Zhang, Hongyu An
- Date: 2026-07-08
- Categories: cs.AR, cs.NE
- DOI: 10.1145/3822454.3822465
Core Contribution
First circuit-level realization of a neuromorphic adaptive deep brain stimulation (aDBS) controller using CMOS technology. The SiLIF-DBS (Silicon Leaky Integrate-and-Fire DBS) controller achieves:
- 75% power reduction vs open-loop stimulation
- 5.85%/μW suppression efficiency for pathological beta activity
- Closed-loop operation driven by STN-LFP biomarkers
System Architecture
SiLIF-DBS Controller
- Implemented in CMOS (metal-oxide-semiconductor) technology
- Uses Leaky Integrate-and-Fire (LIF) neuron model in silicon
- Processes beta-band subthalamic nucleus local field potentials (STN-LFPs)
- Control biomarker: average rectified value (Beta ARV)
Closed-Loop Validation Framework
- Embedded within Parkinsonian cortico-basal ganglia computational model
- Driven by beta-band STN-LFPs as physiological input
- Beta ARV extracted as real-time control signal
- Stimulation adjusted based on biomarker threshold crossings
Key Results
| Metric | SiLIF-DBS | Open-Loop DBS |
|---|
| Power consumption | 25% of baseline | 100% (baseline) |
| Beta suppression efficiency | 5.85%/μW | ~1.5%/μW |
| Pathological beta activity | Strongly suppressed | Moderately suppressed |
Methodology
Biomarker Extraction
- Record STN-LFPs from implanted electrode
- Bandpass filter for beta band (13-30 Hz)
- Compute average rectified value (ARV) as amplitude envelope
- Threshold crossing triggers stimulation adjustment
Neuromorphic Implementation
- LIF neuron dynamics in analog CMOS circuits
- Subthreshold operation for ultra-low power
- Event-driven stimulation delivery (only when biomarker exceeds threshold)
- Hardware-software co-design for implantable form factor
Clinical Significance
- Adaptive stimulation — tracks motor symptom fluctuations in real-time
- Energy efficiency — critical for implantable pulse generators (IPGs) with limited battery
- Reduced side effects — stimulation only when needed, minimizing tissue damage
- Scalable — CMOS fabrication enables mass production
Limitations
- Validated only in computational model, not in vivo
- Single biomarker (Beta ARV) may not capture all symptom dimensions
- CMOS implementation details (process node, area) not fully specified
- Long-term stability of silicon neuron characteristics not addressed
Related Skills
- [[neuromorphic-computing]]
- [[deep-brain-stimulation]]
- [[adaptive-control]]
- [[cmos-neural-circuits]]
- [[parkinsons-disease]]
Implementation Notes
The key innovation is moving aDBS from software algorithms to actual silicon circuits. Previous aDBS systems run algorithms on general-purpose processors consuming too much power for chronic implantation. The SiLIF approach uses the physics of silicon neurons (subthreshold MOSFET operation) to perform biomarker detection and stimulation decision-making at the circuit level, achieving orders-of-magnitude power savings.
