silif-dbs-neuromorphic-controller

name	silif-dbs-neuromorphic-controller
description	Neuromorphic silicon neuron controller (SiLIF-DBS) for adaptive deep brain stimulation in Parkinson's Disease — CMOS-implemented closed-loop aDBS achieving 75% power reduction with beta-band biomarker tracking.
version	1.0.0
author	Hermes Agent
license	MIT
metadata	{"hermes":{"tags":["neuromorphic","deep-brain-stimulation","parkinsons","adaptive-control","silicon-neuron","closed-loop","biomarker","low-power"]},"source_paper":"Neuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation in Parkinson's Disease","arxiv_id":"2607.05453","authors":"Md Abu Bakr Siddique, Jakub Orłowski, Yan Zhang, Hongyu An","published":"2026-07-05","categories":["cs.AR","cs.NE"]}

SiLIF-DBS: Neuromorphic Silicon Neuron Controller for Adaptive DBS

Overview

This skill describes the Silicon Leaky Integrate-and-Fire Deep Brain Stimulation (SiLIF-DBS) controller — a neuromorphic CMOS circuit implementation of adaptive deep brain stimulation (aDBS) for Parkinson's Disease. The system uses beta-band local field potentials as biomarkers to deliver physiologically-informed stimulation, achieving 75% power reduction compared to open-loop stimulation.

Source Paper

arXiv:2607.05453 — "Neuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation in Parkinson's Disease"

• Authors: Md Abu Bakr Siddique, Jakub Orłowski, Yan Zhang, Hongyu An
• Published: July 5, 2026
• Categories: cs.AR, cs.NE

Core Methodology

1. Clinical Background

• Parkinson's Disease (PD): Characterized by pathological beta-band (13-30 Hz) oscillations in the subthalamic nucleus (STN)
• Standard DBS: Continuous open-loop stimulation — wastes power, causes side effects
• Adaptive DBS (aDBS): Stimulates only when pathological biomarkers detected — more efficient, fewer side effects

2. SiLIF-DBS Architecture

The controller implements a silicon neuron (LIF model) that processes STN-LFP signals and generates stimulation pulses:

STN-LFP Signal → Beta Bandpass Filter → Beta ARV (Control Biomarker)
                                               ↓
                                        SiLIF Controller
                                        (CMOS LIF Neuron)
                                               ↓
                                    Adaptive Stimulation Pulses

Key Components:

• Beta ARV (Average Rectified Value): Control biomarker extracted from STN-LFP
• Silicon LIF Neuron: CMOS implementation of leaky integrate-and-fire dynamics
• Closed-Loop Feedback: Stimulation intensity adapts based on beta power level

3. Performance Metrics

• Power consumption: Only 25% of open-loop stimulation power
• Suppression efficiency: 5.85%/μW — high efficiency per unit power
• Beta suppression: Strong pathological beta activity suppression

4. Validation Framework

System-level evaluation uses a Parkinsonian cortico-basal ganglia computational model:

class ParkinsonianNetwork:
    """Simplified cortico-basal ganglia model for aDBS validation."""
    
    def __init__(self):
        self.stn_activity = self.generate_beta_oscillations()
        self.gpi_output = self.compute_gpi_output()
    
    def apply_stimulation(self, stimulation_params):
        """Apply DBS and measure beta suppression."""
        self.stn_activity = self.simulate_stimulation(stimulation_params)
        beta_power = self.compute_beta_power()
        return beta_power
    
    def evaluate_adbs(self, controller):
        """Closed-loop validation of SiLIF-DBS controller."""
        suppression_efficiency = []
        power_consumed = []
        
        for t in range(self.simulation_duration):
            beta_arv = self.extract_beta_arv()
            stimulation = controller.compute_stimulation(beta_arv)
            beta_power = self.apply_stimulation(stimulation)
            
            suppression_efficiency.append(beta_power)
            power_consumed.append(stimulation.energy)
        
        return {
            'suppression_efficiency': np.mean(suppression_efficiency),
            'total_power': np.sum(power_consumed),
            'power_vs_openloop': np.sum(power_consumed) / self.openloop_power
        }

Implementation Steps

Step 1: Beta ARV Extraction

def extract_beta_arv(signal, fs=1000, beta_range=(13, 30)):
    """Extract Beta Average Rectified Value from LFP signal."""
    from scipy.signal import butter, filtfilt
    
    # Bandpass filter for beta range
    b, a = butter(4, [beta_range[0]/(fs/2), beta_range[1]/(fs/2)], btype='band')
    beta_filtered = filtfilt(b, a, signal)
    
    # Average rectified value
    arv = np.mean(np.abs(beta_filtered))
    return arv

Step 2: Silicon LIF Neuron Model

class SiliconLIFNeuron:
    """CMOS-compatible LIF neuron model for aDBS control."""
    
    def __init__(self, tau_mem=20e-3, v_thresh=1.0, v_reset=0.0, dt=1e-3):
        self.tau_mem = tau_mem  # Membrane time constant
        self.v_thresh = v_thresh  # Firing threshold
        self.v_reset = v_reset  # Reset potential
        self.dt = dt
        self.membrane_potential = 0.0
    
    def step(self, input_current):
        """One integration step of LIF dynamics."""
        dv = (-self.membrane_potential + input_current) * self.dt / self.tau_mem
        self.membrane_potential += dv
        
        if self.membrane_potential >= self.v_thresh:
            self.membrane_potential = self.v_reset
            return 1.0  # Spike
        return 0.0  # No spike
    
    def compute_stimulation(self, beta_arv):
        """Map beta ARV to stimulation intensity."""
        # Normalize beta ARV to input current
        input_current = beta_arv * .gain
        spike = .step(input_current)
        
        
         spike:
             .pulse_amplitude
         

Step 3: Closed-Loop Integration

def closed_loop_adbs(network, controller, duration_ms=1000):
    """Run closed-loop adaptive DBS simulation."""
    results = {'beta_power': [], 'stimulation': [], 'power': []}
    
    for t in range(int(duration_ms)):
        # Extract biomarker
        beta_arv = network.extract_beta_arv()
        
        # Controller computes stimulation
        stim = controller.compute_stimulation(beta_arv)
        
        # Apply stimulation and measure effect
        beta_power = network.apply_stimulation(stim)
        power = controller.compute_power(stim)
        
        results['beta_power'].append(beta_power)
        results['stimulation'].append(stim)
        results['power'].append(power)
    
    return results

Key Findings

1. 75% power reduction: SiLIF-DBS consumes only 25% of open-loop stimulation power
2. High suppression efficiency: 5.85%/μW — significant improvement over existing aDBS methods
3. CMOS implementation: Hardware-realizable design suitable for implantable devices
4. Closed-loop validation: Tested in Parkinsonian cortico-basal ganglia computational model

Activation Triggers

Trigger words: adaptive deep brain stimulation, SiLIF, silicon neuron, Parkinson's disease, aDBS, beta oscillation, neuromorphic controller, closed-loop stimulation, STN-LFP, low-power implantable

Use when:

• Designing adaptive deep brain stimulation systems
• Implementing neuromorphic controllers for neurological disorders
• Building low-power implantable medical devices
• Modeling Parkinson's disease cortico-basal ganglia circuits
• Developing closed-loop neurostimulation algorithms

Pitfalls

1. Biomarker selection: Beta ARV is specific to PD — different disorders require different biomarkers
2. Computational model limitations: Simplified cortico-basal ganglia model may not capture all clinical dynamics
3. CMOS constraints: Hardware implementation must consider power, area, and process variations
4. Validation gap: Computational validation must be followed by in-vivo testing for clinical translation

References

• Siddique, M. A. B., Orłowski, J., Zhang, Y., & An, H. (2026). Neuromorphic Silicon Neuron Controller for Adaptive Deep Brain Stimulation in Parkinson's Disease. arXiv:2607.05453
• Standard DBS clinical literature
• LIF neuron modeling references