论文标题
使用拓扑电荷动力学的基于抗铁磁铁的神经形态
Antiferromagnet-Based Neuromorphics Using Dynamics of Topological Charges
论文作者
论文摘要
我们建议使用一维抗铁磁铁中的拓扑绕组纹理，提出了神经形态计算的基于自旋的硬件实现。鉴于拓扑电荷的保存以及磁绕组的自然时空相互转换，强调了这种网络的一致性。我们讨论了神经元泄漏的整合和传火行为的实现以及突触的峰值依赖性可塑性。我们的提案开辟了基于抗铁磁绝缘子的全旋转神经形态平台的可能性。
We propose a spintronics-based hardware implementation of neuromorphic computing, specifically, the spiking neural network, using topological winding textures in one-dimensional antiferromagnets. The consistency of such a network is emphasized in light of the conservation of topological charges, and the natural spatiotemporal interconversions of magnetic winding. We discuss the realization of the leaky integrate-and-fire behavior of neurons and the spike-timing-dependent plasticity of synapses. Our proposal opens the possibility for an all-spin neuromorphic platform based on antiferromagnetic insulators.
