Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational acceleration for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, rigorous simulation and consequent validation of brain-based experimental data is imperative. In this work, we investigate the potential of Intel's fifth generation neuromorphic chip - `Loihi', which is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain. The work is implemented in context of simulating the Leaky Integrate and Fire (LIF) models based on the mouse primary visual cortex matched to a rich data set of anatomical, physiological and behavioral constraints. Simulations on the classical hardware serve as the validation platform for the neuromorphic implementation. We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.

中文翻译：

---

在英特尔的神经形态硬件 Loihi 上映射和验证点神经元模型

神经形态硬件基于模拟大脑的自然生物结构。由于它的计算模型类似于标准的神经模型，它可以作为神经科学和人工智能领域研究项目的计算加速，包括生物医学应用。然而，为了开发新一代计算机芯片，必须对基于大脑的实验数据进行严格的模拟和随后的验证。在这项工作中，我们研究了英特尔第五代神经形态芯片“Loihi”的潜力，该芯片基于模拟大脑中神经元的尖峰神经网络 (SNN) 的新想法。这项工作是在模拟 Leaky Integrate and Fire (LIF) 模型的背景下实施的，该模型基于与解剖、生理和行为约束的丰富数据集相匹配的小鼠初级视觉皮层。经典硬件上的模拟用作神经形态实现的验证平台。我们发现 Loihi 非常有效地复制了经典模拟，并且随着网络变大，在时间和能量性能方面的扩展性都非常好。