general-aspects-internal-noise-spiking-neural

name: general-aspects-internal-noise-spiking-neural description: "Internal noise analysis in Spiking Neural Networks. Covers noise sources (channel, synaptic, threshold), propagation mechanisms, and effects on SNN dynamics. Distinguishes additive vs multiplicative noise regimes and their impacts on computation. Activation: SNN, internal noise, spiking neural networks, noise analysis, neuromorphic"

Internal Noise Analysis in Spiking Neural Networks

Overview

A comprehensive analysis of internal noise in spiking neural networks, examining how noise originates from biological sources (channel noise, synaptic variability, threshold fluctuations), propagates through network layers, and affects computation. Distinguishes between additive and multiplicative noise regimes and provides frameworks for characterizing and quantifying noise in both biological and artificial spiking systems.

Source Paper

• Title: General aspects of internal noise in spiking neural networks
• Authors: I. D. Kolesnikov, D. A. Maksimov, V. M. Moskvitin, N. Semenova
• arXiv: 2604.13612v1
• Published: 2026-04-15
• Categories: N/A
• PDF: https://arxiv.org/pdf/2604.13612v1

Core Concepts

Noise Sources in SNNs

Internal noise arises from multiple biological mechanisms:

1. Channel noise: Stochastic opening/closing of ion channels (thermal fluctuations)
2. Synaptic noise: Probabilistic neurotransmitter release (vesicle stochasticity)
3. Threshold noise: Variability in spike threshold due to channel state
4. Background activity: Ongoing spontaneous network activity (synaptic bombardment)

Additive vs Multiplicative Noise

import numpy as np

class NoisyLIFNeuron:
    """
    Leaky Integrate-and-Fire neuron with configurable internal noise.
    """
    
    def __init__(self, tau_m=20e-3, v_rest=-65e-3, v_thresh=-50e-3,
                 noise_type='additive', noise_std=1e-3):
        self.tau_m = tau_m
        self.v_rest = v_rest
        self.v_thresh = v_thresh
        self.v_reset = v_rest
        self.noise_type = noise_type
        self.noise_std = noise_std
        self.v_membrane = v_rest
        
    def step(self, input_current, dt=1e-3):
        # LIF membrane dynamics
        dv = (-(self.v_membrane - self.v_rest) + input_current * 1e6) / self.tau_m * dt
        
        # Internal noise
        if self.noise_type == 'additive':
            # State-independent: dV = f(V)dt + sigma*dW
            noise = np.random.normal(0, self.noise_std)
        elif self.noise_type == 'multiplicative':
            # State-dependent: dV = f(V)dt + g(V)*dW
            noise = np.random.normal(0, self.noise_std * abs(self.v_membrane - self.v_rest))
        else:
            noise = 0
        
        self.v_membrane += dv + noise
        
        if self.v_membrane >= self.v_thresh:
            self.v_membrane = self.v_reset
            return True
        return False

Noise Characterization

def characterize_noise_regime(spike_times):
    """
    Determine noise regime from spike train statistics.
    
    - Additive noise: CV approx 1 (Poisson-like), Fano factor approx 1
    - Multiplicative noise: CV != 1, Fano factor != 1
    - Sub-Poisson: CV < 1 (regular firing)
    - Super-Poisson: CV > 1 (bursty firing)
    """
    isi = np.diff(spike_times)
    if len(isi) < 2:
        return {'CV': np.nan, 'fano_factor': np.nan}
    
    cv = np.std(isi) / np.mean(isi)
    
    # Fano factor from spike counts in windows
    window = 0.1
    counts = []
    for t in np.arange(spike_times[0], spike_times[-1] - window, window):
        counts.append(np.sum((spike_times >= t) & (spike_times < t + window)))
    
    fano = np.var(counts) / (np.mean(counts) + 1e-10)
    
    return {'CV': cv, 'fano_factor': fano}

Practical Applications

1. Neuromorphic hardware: Design noise-robust SNN chips
2. Stochastic computing: Leverage noise for probabilistic inference
3. Robustness testing: Validate SNN under realistic noise conditions
4. Neurological modeling: Excessive noise linked to disorders

Activation Keywords

• SNN internal noise
• spiking neural network noise
• additive multiplicative noise
• neuromorphic noise
• spike timing jitter
• stochastic SNN

Latest Research Updates

arXiv:2604.13612v1 (2026-04-15)

Title: General aspects of internal noise in spiking neural networks Authors: I. D. Kolesnikov, D. A. Maksimov, V. M. Moskvitin et al. Link: https://arxiv.org/abs/2604.13612v1