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Table 1_Review of deep learning models with Spiking Neural Networks for modeling and analysis of multimodal neuroimaging data.xlsx
Medical imaging has become an essential tool for identifying and treating neurological conditions. Traditional deep learning (DL) models have made tremendous advances in neuroimaging analysis; however
NIAID Data Ecosystem100
n-back dataset comparing optimized and unoptimized semantic pointer representations
Raw n-back simulation data used in the paper "Optimizing Semantic Pointer Representations for Symbol-like Processing in Spiking Neural Networks".
Figshare2016-01-05 更新70
Application spike sparsity and AHaH node count.
The applications and benchmarks presented in this paper to demonstrate various machine learning tasks using AHaH plasticity require different AHaH node configurations depending on the type of data bei
NIAID Data Ecosystem40
Statistics of the large-scale AI network.
Reference (ref.) simulated with NEST, distorted (dist.) and compensated (comp.) with the ESS. Statistics of the large-scale AI network.
NIAID Data Ecosystem80
CogSci2013: Experiment 2 Optimization
Results from 200 runs (plus 7 control runs) of Experiment 2 from "Simultaneous unsupervised and supervised learning of cognitive functions in biologically plausible spiking neural networks". These run
Figshare2016-01-18 更新50
