Midya Rivu, Pawar Ambarish S, Pattnaik Debi P, Mooshagian Eric, Borisov Pavel, Albright Thomas D, Snyder Lawrence H, Williams R Stanley, Yang J Joshua, Balanov Alexander G, Gepshtein Sergei, Savel'ev Sergey E
Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA, USA.
Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
Nat Commun. 2025 Aug 7;16(1):7289. doi: 10.1038/s41467-025-62151-9.
Rapid development of memristive elements emulating biological neurons creates new opportunities for brain-like computation at low energy consumption. A first step toward mimicking complex neural computations is the analysis of single neurons and their characteristics. Here we measure and model spiking activity in artificial neurons built using diffusive memristors. We compare activity of these artificial neurons with the spiking activity of biological neurons measured in sensory, pre-motor, and motor cortical areas of the monkey (male) brain. We find that artificial neurons can operate in diverse self-sustained and noise-induced spiking regimes that correspond to the activity of different types of cortical neurons with distinct functions. We demonstrate that artificial neurons can function as trans-functional devices (transneurons) that reconfigure their behaviour to attain instantaneous computational needs, each capable of emulating several biological neurons.
能够模拟生物神经元的忆阻器元件的快速发展为低能耗类脑计算创造了新机会。迈向模拟复杂神经计算的第一步是对单个神经元及其特征进行分析。在此，我们对使用扩散忆阻器构建的人工神经元中的尖峰活动进行测量和建模。我们将这些人工神经元的活动与在雄性猴子大脑的感觉、运动前和运动皮层区域测量的生物神经元的尖峰活动进行比较。我们发现人工神经元可以在多种自持和噪声诱导的尖峰模式下运行，这些模式对应于具有不同功能的不同类型皮层神经元的活动。我们证明人工神经元可以充当跨功能设备（跨神经元），重新配置其行为以满足即时计算需求，并每个都能够模拟多个生物神经元。
