首页/数据集/Experimental data for "Long-term potentiation mechanism of biological postsynaptic activity in neuro-inspired halide perovskite memristors"

Experimental data for "Long-term potentiation mechanism of biological postsynaptic activity in neuro-inspired halide perovskite memristors"

Name: Experimental data for "Long-term potentiation mechanism of biological postsynaptic activity in neuro-inspired halide perovskite memristors"
Creator: Universitat Jaume I; Universidad Rey Juan Carlos; Astana IT University
Published: 2023-01-01T00:00:00+08:00

Harvard Dataverse2023-04-11 更新2026-04-09 收录

神经形态计算

钙钛矿忆阻器

数据链接：

https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/7JOSNP 数据链接链接失效反馈

官方服务：

资源简介：

Experimental visualization of synaptic potentiation operation in perovskite memristors.

应用场景：

提供机构：

Universitat Jaume I; Universidad Rey Juan Carlos; Astana IT University

创建时间：

2023-01-01

相关数据集

NeuroMorse

神经形态计算

时态特征识别

NeuroMorse数据集是由詹姆斯库克大学科学和工程学院等机构创建的，专为神经形态学习系统基准测试设计的时态结构化数据集。该数据集将英语中最常用的50个单词转换为莫尔斯电码的尖峰序列，尽管只使用两个输入尖峰通道，但通过时态模式编码了复杂信息。数据集在多个时间尺度上具有特征层次结构，能够测试神经形态算法分解输入模式成为空间和时间层次结构的能力。该数据集旨在解决神经形态计算中的时态特征识别问题。

arXiv2025-02-28 更新350

微电子所Mott基1T1R神经元电路与单层SNN数据

神经形态计算

脉冲神经网络硬件

利用NbOx神经元电路中晶体管栅极电压与神经元发射频率的线性关系，构建了一种实现ReLU激活函数的神经元，可以实现ANN向SNN的高效转化。

国家基础学科公共科学数据中心100

Replication Data for: Ionotronic Halide Perovskite Drift‐Diffusive Synapses for Low‐Power Neuromorphic Computation

新型材料研发

神经形态计算

This dataset includes the origin files and analysis of the individual figures published in the article titled "Ionotronic Halide Perovskite Drift‐Diffusive Synapses for Low‐Power Neuromorphic Computat

DataCite Commons2025-06-10 更新50

Data and code underlying the research of: SRIF-ADC for CIM accelerators

集成电路设计

神经形态计算

This targets neuromorphic and general-purpose arithmetic applications. A scalable and reliable integrate and fire circuit ADC (SRIF-ADC) design for CIM architectures is presented, suitable for stringe

Mendeley Data2024-06-25 更新130

Data and codes for producing results associated with the manuscript "Physical learning of power-efficient solutions"

神经形态计算

低功耗机器学习硬件

As the size and ubiquity of artificial intelligence and computational machine learning (ML) models grow, their energy consumption for training and use is rapidly becoming economically and environmenta

Figshare2023-12-29 更新70