Stoliar Pablo, Schneegans Olivier, Rozenberg Marcelo J
National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.
Université Paris-Saclay, Sorbonne Université, CentraleSupélec, CNRS, Laboratoire de Génie Électrique et Électronique de Paris, Gif-sur-Yvette, France.
Front Neurosci. 2021 Feb 25;15:635098. doi: 10.3389/fnins.2021.635098. eCollection 2021.
We demonstrate that recently introduced ultra-compact neurons (UCN) with a minimal number of components can be interconnected to implement a functional spiking neural network. For concreteness we focus on the Jeffress model, which is a classic neuro-computational model proposed in the 40's to explain the sound directionality detection by animals and humans. In addition, we introduce a , whose architecture is inspired by the Hodgkin-Huxley axon delay-line and where the UCNs implement the nodes of Ranvier. We then interconnect two of those neurons to an output layer of UCNs, which detect coincidences between spikes propagating down the long-axons. This functional spiking neural neuron circuit with biological relevance is built from identical UCN blocks, which are simple enough to be made with electronic components. Our work realizes a new, accessible and affordable , where neuroscientists can construct arbitrary mid-size spiking neuronal networks in a -block like fashion that work in continuous time. This should enable them to address in a novel experimental manner fundamental questions about the nature of the neural code and to test predictions from mathematical models and algorithms of basic neurobiology research. The present work aims at opening a new experimental field of basic research in Spiking Neural Networks to a potentially large community, which is at the crossroads of neurobiology, dynamical systems, theoretical neuroscience, condensed matter physics, neuromorphic engineering, artificial intelligence, and complex systems.
我们证明，最近引入的具有最少组件数量的超紧凑型神经元（UCN）可以相互连接以实现功能性脉冲神经网络。为了具体说明，我们重点关注杰弗里斯模型，它是40年代提出的经典神经计算模型，用于解释动物和人类的声音方向性检测。此外，我们引入了一种[此处原文缺失具体内容]，其架构受霍奇金 - 赫胥黎轴突延迟线启发，其中UCN实现郎飞结的节点。然后，我们将其中两个神经元与UCN的输出层互连，该输出层检测沿长轴突向下传播的尖峰之间的巧合。这种具有生物学相关性的功能性脉冲神经神经元电路由相同的UCN模块构建而成，这些模块足够简单，可以用电子元件制造。我们的工作实现了一种新的、可访问且经济实惠的[此处原文缺失具体内容]，神经科学家可以以类似于[此处原文缺失具体内容]模块的方式构建任意中等规模的脉冲神经元网络，这些网络在连续时间内工作。这应该使他们能够以新颖的实验方式解决关于神经编码本质的基本问题，并测试基础神经生物学研究的数学模型和算法的预测。目前的工作旨在为一个潜在的大型社区开辟一个脉冲神经网络基础研究的新实验领域，该社区处于神经生物学、动力系统、理论神经科学、凝聚态物理、神经形态工程、人工智能和复杂系统的交叉点。
