Scholarlink

<p>Biophysical neuron models provide insights into cellular mechanisms underlying neural computations. A central challenge has been to identify parameters of detailed biophysical models such that they match physiological measurements or perform computational tasks. Here we describe a framework for simulating biophysical models in neuroscience—<span>Jaxley</span>—which addresses this challenge. By making use of automatic differentiation and GPU acceleration, <span>Jaxley</span> enables optimizing large-scale biophysical models with gradient descent. <span>Jaxley</span> can learn biophysical neuron models to match voltage or two-photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. <span>Jaxley</span> also makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. <span>Jaxley</span> improves the ability to build large-scale data- or task-constrained biophysical models, creating opportunities for investigating the mechanisms underlying neural computations across multiple scales.</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

Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月13日

查看全文
  • Michael Deistler,
  • Kyra L. Kadhim,
  • Matthijs Pals,
  • Jonas Beck,
  • Ziwei Huang,
  • Manuel Gloeckler,
  • Janne K. Lappalainen,
  • Cornelius Schröder,
  • Philipp Berens,
  • Pedro J. Gonçalves,
  • Jakob H. Macke

摘要

Biophysical neuron models provide insights into cellular mechanisms underlying neural computations. A central challenge has been to identify parameters of detailed biophysical models such that they match physiological measurements or perform computational tasks. Here we describe a framework for simulating biophysical models in neuroscience—Jaxley—which addresses this challenge. By making use of automatic differentiation and GPU acceleration, Jaxley enables optimizing large-scale biophysical models with gradient descent. Jaxley can learn biophysical neuron models to match voltage or two-photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. Jaxley also makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. Jaxley improves the ability to build large-scale data- or task-constrained biophysical models, creating opportunities for investigating the mechanisms underlying neural computations across multiple scales.