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Unsupervised Learning with Self-Organizing Spiking Neural Networks
TLDR
A hybridization of self-organized map properties with spiking neural networks that retain many of the features of SOMs is presented, and using the optimal choice of parameters, this approach produces improvements over state-of-art spiking Neural networks.
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
TLDR
It is argued that this package facilitates the use of spiking networks for large-scale machine learning problems and some simple examples by using BindsNET in practice are shown.
Decoding the Formation of New Semantics: MVPA Investigation of Rapid Neocortical Plasticity during Associative Encoding through Fast Mapping
TLDR
It is proposed that fast mapping promotes incidental rapid integration of new associations into existing neocortical semantic networks by activating related, nonoverlapping conceptual knowledge, while hippocampal involvement is less predictive of this kind of learning.
Lattice map spiking neural networks (LM-SNNs) for clustering and classifying image data
TLDR
Spiking neural networks with a lattice architecture are introduced in this work, combining several desirable properties of SNNs and self-organized maps, including a population-level confidence rating, and an n-gram inspired method.
Early diagnosis of Parkinson's disease via machine learning on speech data
TLDR
It is shown that machine learning tools can be used for the early diagnosis of Parkinson's disease from speech data and that while the training phase of machine learning process from one country can be reused in the other; different features dominate in each country; presumably because of languages differences.
The Liquid State Machine is not Robust to Problems in Its Components but Topological Constraints Can Restore Robustness
TLDR
It is shown that the LSM as normally defined cannot serve as a natural model for brain function, and specifying certain kinds of topological constraints, which have been claimed are reasonably plausible biologically, can restore robustness in this sense to LSMs.
Computational Diagnosis of Parkinson's Disease Directly from Natural Speech Using Machine Learning Techniques
TLDR
This work shows that early diagnosis of Parkinson's disease is possible solely from the voice signal, and conjecture that such systems can be extended to monitoring and classifying additional neurological diseases and speech pathologies.
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