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基于片上系统现场可编程门阵列的实时多房室霍奇金-赫胥黎神经元仿真

Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA.

作者信息

Beaubois Romain, Cheslet Jérémy, Ikeuchi Yoshiho, Branchereau Pascal, Levi Timothee

机构信息

IMS, UMR5218, CNRS, University of Bordeaux, Talence, France.

Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.

出版信息

Front Neurosci. 2024 Nov 12;18:1457774. doi: 10.3389/fnins.2024.1457774. eCollection 2024.

Abstract

Advanced computational models and simulations to unravel the complexities of brain function have known a growing interest in recent years in the field of neurosciences, driven by significant technological progress in computing platforms. Multicompartment models, which capture the detailed morphological and functional properties of neural circuits, represent a significant advancement in this area providing more biological coherence than single compartment modeling. These models serve as a cornerstone for exploring the neural basis of sensory processing, learning paradigms, adaptive behaviors, and neurological disorders. Yet, the high complexity of these models presents a challenge for their real-time implementation, which is essential for exploring alternative therapies for neurological disorders such as electroceutics that rely on biohybrid interaction. Here, we present an accessible, user-friendly, and real-time emulator for multicompartment Hodgkin-Huxley neurons on SoC FPGA. Our system enables real-time emulation of multicompartment neurons while emphasizing cost-efficiency, flexibility, and ease of use. We showcase an implementation utilizing a technology that remains underrepresented in the current literature for this specific application. We anticipate that our system will contribute to the enhancement of computation platforms by presenting an alternative architecture for multicompartment computation. Additionally, it constitutes a step toward developing neuromorphic-based neuroprostheses for bioelectrical therapeutics through an embedded real-time platform running at a similar timescale to biological networks.

摘要

近年来,在计算平台取得重大技术进步的推动下,用于揭示脑功能复杂性的先进计算模型和模拟在神经科学领域受到越来越多的关注。多房室模型能够捕捉神经回路的详细形态和功能特性,代表了该领域的一项重大进展,比单房室建模具有更高的生物学连贯性。这些模型是探索感觉处理、学习范式、适应性行为和神经疾病神经基础的基石。然而,这些模型的高度复杂性给其实时实现带来了挑战,而实时实现对于探索神经疾病的替代疗法(如依赖生物杂交相互作用的电休克疗法)至关重要。在此,我们展示了一种用于片上系统现场可编程门阵列(SoC FPGA)上多房室霍奇金-赫胥黎神经元的易于使用的实时模拟器。我们的系统能够实时模拟多房室神经元,同时强调成本效益、灵活性和易用性。我们展示了一种利用目前该特定应用文献中较少提及的技术的实现方式。我们预计,我们的系统将通过提供一种用于多房室计算的替代架构,为增强计算平台做出贡献。此外,它朝着通过一个以与生物网络相似的时间尺度运行的嵌入式实时平台开发基于神经形态的生物电治疗神经假体迈出了一步。

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