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

IF 3.2 3区 医学 Q2 NEUROSCIENCES Frontiers in Neuroscience Pub Date : 2024-11-12 eCollection Date: 2024-01-01 DOI:10.3389/fnins.2024.1457774
Romain Beaubois, Jérémy Cheslet, Yoshiho Ikeuchi, Pascal Branchereau, Timothee Levi
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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.

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SoC FPGA 上的实时多室霍奇金-赫胥黎神经元仿真。
近年来,在计算平台技术显著进步的推动下,神经科学领域对揭示大脑功能复杂性的先进计算模型和模拟越来越感兴趣。多室模型能捕捉神经回路的详细形态和功能特性,是该领域的一大进步,与单室模型相比,它能提供更多的生物连贯性。这些模型是探索感觉处理、学习范式、适应行为和神经系统疾病的神经基础的基石。然而,这些模型的高复杂性为其实时实施带来了挑战,而实时实施对于探索神经系统疾病的替代疗法(如依赖于生物混合相互作用的电疗法)至关重要。在这里,我们在 SoC FPGA 上为多室霍奇金-赫胥黎神经元提供了一个易于访问、用户友好的实时仿真器。我们的系统可实现多室神经元的实时仿真,同时强调成本效益、灵活性和易用性。我们展示了利用一种在当前文献中仍未得到充分反映的技术实现这一特定应用的情况。我们预计,我们的系统将为多区室计算提供另一种架构,从而为增强计算平台做出贡献。此外,它还通过一个嵌入式实时平台,以类似于生物网络的时间尺度运行,为开发基于神经形态的神经义肢,用于生物电治疗迈出了一步。
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来源期刊
Frontiers in Neuroscience
Frontiers in Neuroscience NEUROSCIENCES-
CiteScore
6.20
自引率
4.70%
发文量
2070
审稿时长
14 weeks
期刊介绍: Neural Technology is devoted to the convergence between neurobiology and quantum-, nano- and micro-sciences. In our vision, this interdisciplinary approach should go beyond the technological development of sophisticated methods and should contribute in generating a genuine change in our discipline.
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