• Publications
  • Influence
BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python
The development of spiking neural network simulation software is a critical component enabling the modeling of neural systems and the development of biologically inspired algorithms. ExistingExpand
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Locally Connected Spiking Neural Networks for Unsupervised Feature Learning
In recent years, spiking neural networks (SNNs) have demonstrated great success in completing various machine learning tasks. We introduce a method for learning image features with locally connectedExpand
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Decoding the Formation of New Semantics: MVPA Investigation of Rapid Neocortical Plasticity during Associative Encoding through Fast Mapping
Neocortical structures typically only support slow acquisition of declarative memory; however, learning through fast mapping may facilitate rapid learning-induced cortical plasticity andExpand
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Unsupervised Learning with Self-Organizing Spiking Neural Networks
We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are trained in anExpand
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Early diagnosis of Parkinson's disease via machine learning on speech data
Using two distinct data sets (from the USA and Germany) of healthy controls and patients with early or mild stages of Parkinson's disease, we show that machine learning tools can be used for theExpand
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Topological constraints and robustness in liquid state machines
The Liquid State Machine (LSM) is a method of computing with temporal neurons, which can be used amongst other things for classifying intrinsically temporal data directly unlike standard artificialExpand
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Closed Loop Experiment Manager (CLEM)—An Open and Inexpensive Solution for Multichannel Electrophysiological Recordings and Closed Loop Experiments
There is growing need for multichannel electrophysiological systems that record from and interact with neuronal systems in near real-time. Such systems are needed, for example, for closed loop,Expand
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Reinforcement learning with a network of spiking agents
Neuroscientific theory suggests that dopaminergic neurons broadcast global reward prediction errors to large areas of the brain influencing the synaptic plasticity of the neurons in those regions. WeExpand
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Computational Diagnosis of Parkinson's Disease Directly from Natural Speech Using Machine Learning Techniques
The human voice signal carries much information in addition to direct linguistic semantic information. This information can be perceived by computational systems. In this work, we show that earlyExpand
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Stability and Topology in Reservoir Computing
Recently Jaeger and others have put forth the paradigm of "reservoir computing" as a way of computing with highly recurrent neural networks. This reservoir is a collection of neurons randomlyExpand
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