数据链接：
官方服务：
资源简介：
The trained neural network models
应用场景：
创建时间：
2025-10-28
相关数据集
具有安全感知能力的电网潮流神经网络模型训练数据集
常规的强化学习需要从与环境的交互数据中学习，然而与真实电网交互会带来安全问题。此外，与输电网相比，配电网模型精度不足。使用配电网模型为强化学习构建仿真环境往往难以取得可靠效果。设计概率集成神经网络，从数据中发现电网的响应特性知识。为强化学习提供具有安全感知能力的仿真交互环境。该数据集为训练电网潮流神经网络模型所用的数据集，由电网模型仿真生成。
国家基础学科公共科学数据中心180
Cell-C neuron model parameters and their values.
* These parameters are not constrained by the experimental data. Their values are simply assumed as typical values. The leak conductance, and random variation in the excitatory synaptic inputs were la
NIAID Data Ecosystem90
Main Figures
Data and code to recreate main figures in Identifying Properties of Pattern Completion Neurons in a Computational Model of the Visual Cortex
DataCite Commons2023-04-03 更新70
Whole cell patch-clamp recordings of cerebellar stellate cells
This dataset provides a characterization of the electrophysiological properties of the cerebellar stellate cells and investigation of their functions. Stellate cells are inhibitory interneurons that r
DataCite Commons2021-07-20 更新150
Supplementary Text S1 - A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences
This document describes the response of the ABC model to switching oddball sequences, and block, sequential and random tone configurations. It also describes the response of the ABCD model to sequence
NIAID Data Ecosystem70
