Scholarlink

<p>Synaptic plasticity, defined as the ability of connections between neurons to strengthen or weaken over time, is essential for learning, yet unregulated plasticity can lead to instability. Inspired by biological feedback mechanisms, we propose a framework that stabilizes synaptic changes in artificial neural networks through top-down signals derived from the network’s own output. The model, termed intrinsic Top-Down Stabilization (iTDS), augments traditional supervised learning by introducing a slower, top-down signal that tracks the network’s output over time and modulates synaptic updates. Instead of directly influencing sensory inputs, these top-down signals guide synaptic plasticity by gradually aligning with ongoing network activity. We demonstrate that this simple mechanism improves training efficiency, enhances generalization, and increases resilience to noise perturbations. The approach is evaluated across recurrent, feedforward, and reservoir networks on 16 synthetic and real-world tasks, including temporal signal encoding, synthetic data categorization, handwritten character recognition, and image classification. Our results clarify how network activity influences the alignment of top-down signals with supervised learning, providing testable predictions for how feedback projections can stabilize learning in biological networks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Intrinsic stabilization of synaptic plasticity improves learning and robustness in artificial neural networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月19日

查看全文
  • Artem Pilzak,
  • Bobby Pennington,
  • Jean-Philippe Thivierge

摘要

Synaptic plasticity, defined as the ability of connections between neurons to strengthen or weaken over time, is essential for learning, yet unregulated plasticity can lead to instability. Inspired by biological feedback mechanisms, we propose a framework that stabilizes synaptic changes in artificial neural networks through top-down signals derived from the network’s own output. The model, termed intrinsic Top-Down Stabilization (iTDS), augments traditional supervised learning by introducing a slower, top-down signal that tracks the network’s output over time and modulates synaptic updates. Instead of directly influencing sensory inputs, these top-down signals guide synaptic plasticity by gradually aligning with ongoing network activity. We demonstrate that this simple mechanism improves training efficiency, enhances generalization, and increases resilience to noise perturbations. The approach is evaluated across recurrent, feedforward, and reservoir networks on 16 synthetic and real-world tasks, including temporal signal encoding, synthetic data categorization, handwritten character recognition, and image classification. Our results clarify how network activity influences the alignment of top-down signals with supervised learning, providing testable predictions for how feedback projections can stabilize learning in biological networks.