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<p>Understanding complex biological processes often requires modeling both deterministic logic and stochastic variability. In this work, we incorporate stochasticity into spiking neural network (SNN)-based Boolean networks through probabilistic inputs and variability in connectivity patterns. We present an approach to simulate synchronous Boolean networks with SNNs, representing Boolean variables as neuron populations, and logical functions by spiking subcircuits. The representation of genes with a set of neurons instead of a single one, enables new methods for incorporating stochasticity into the network by defining the pattern of the neuron connections between sets. In a case study, we implemented a Boolean model of hematopoietic stem cell regulation and analyze the network transitions between attractor states under different input scenarios, especially stochastic variations in external inputs and neuron connection patterns.</p>

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Stochasticity in spiking-neural-network based Boolean networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

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  • Caya L. O. Hotstegs,
  • Johann M. Kraus,
  • Joachim Ankerhold,
  • Hans A. Kestler

摘要

Understanding complex biological processes often requires modeling both deterministic logic and stochastic variability. In this work, we incorporate stochasticity into spiking neural network (SNN)-based Boolean networks through probabilistic inputs and variability in connectivity patterns. We present an approach to simulate synchronous Boolean networks with SNNs, representing Boolean variables as neuron populations, and logical functions by spiking subcircuits. The representation of genes with a set of neurons instead of a single one, enables new methods for incorporating stochasticity into the network by defining the pattern of the neuron connections between sets. In a case study, we implemented a Boolean model of hematopoietic stem cell regulation and analyze the network transitions between attractor states under different input scenarios, especially stochastic variations in external inputs and neuron connection patterns.