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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. Expand
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. Expand
Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
TLDR
A method for learning image features with locally connected layers in SNNs using a spike-timing-dependent plasticity (STDP) rule, which has the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning. Expand
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. Expand
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. Expand
Topological constraints and robustness in liquid state machines
TLDR
It is shown that specifying certain kinds of topological constraints (such as ''small world assumption''), which have been claimed are reasonably plausible biologically, can restore robustness in this sense to LSMs. Expand
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. Expand
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. Expand
Closed Loop Experiment Manager (CLEM)—An Open and Inexpensive Solution for Multichannel Electrophysiological Recordings and Closed Loop Experiments
TLDR
This work describes the application, its architecture and facilities, and demonstrates, using networks of cortical neurons growing on multielectrode arrays (MEA), that despite its reliance on generic hardware, its performance is appropriate for flexible, closed-loop experimentation at the neuronal network level. Expand
Reinforcement learning with a network of spiking agents
TLDR
It is shown that a network of GLM spiking agents connected in a hierarchical fashion, where each spiking agent modulates its firing policy based on local information and a global prediction error, can learn complex action representations to solve reinforcement learning (RL) tasks. Expand
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