Note on the coefficient of variations of neuronal spike trains
Sign Up to like & getrecommendations! Published in 2017 at "Biological Cybernetics"
DOI: 10.1007/s00422-017-0717-y
Abstract: It is known that many neurons in the brain show spike trains with a coefficient of variation (CV) of the interspike times of approximately 1, thus resembling the properties of Poisson spike trains. Computational studies… read more here.
Keywords: variations neuronal; coefficient variations; spike trains; note coefficient ... See more keywords
Modeling multiscale causal interactions between spiking and field potential signals during behavior
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Neural Engineering"
DOI: 10.1088/1741-2552/ac4e1c
Abstract: Objective. Brain recordings exhibit dynamics at multiple spatiotemporal scales, which are measured with spike trains and larger-scale field potential signals. To study neural processes, it is important to identify and model causal interactions not only… read more here.
Keywords: potential signals; causality; spike trains; field potential ... See more keywords
Reconstructing networks of pulse-coupled oscillators from spike trains.
Sign Up to like & getrecommendations! Published in 2017 at "Physical Review E"
DOI: 10.1103/physreve.96.012209
Abstract: We present an approach for reconstructing networks of pulse-coupled neuronlike oscillators from passive observation of pulse trains of all nodes. It is assumed that units are described by their phase response curves and that their… read more here.
Keywords: coupled oscillators; spike trains; networks pulse; reconstructing networks ... See more keywords
A Highly Effective and Robust Membrane Potential-Driven Supervised Learning Method for Spiking Neurons
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2018.2833077
Abstract: Spiking neurons are becoming increasingly popular owing to their biological plausibility and promising computational properties. Unlike traditional rate-based neural models, spiking neurons encode information in the temporal patterns of the transmitted spike trains, which makes… read more here.
Keywords: spike trains; driven; method; learning method ... See more keywords
Sparse Large-Scale Nonlinear Dynamical Modeling of Human Hippocampus for Memory Prostheses
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"
DOI: 10.1109/tnsre.2016.2604423
Abstract: In order to build hippocampal prostheses for restoring memory functions, we build sparse multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from hippocampal CA3 and CA1 regions of epileptic… read more here.
Keywords: memory; spike trains; human hippocampus; memory prostheses ... See more keywords
Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns
Sign Up to like & getrecommendations! Published in 2024 at "Frontiers in Computational Neuroscience"
DOI: 10.3389/fncom.2024.1363514
Abstract: In this study, we focus on training recurrent spiking neural networks to generate spatiotemporal patterns in the form of closed two-dimensional trajectories. Spike trains in the trained networks are examined in terms of their dissimilarity… read more here.
Keywords: recurrent spiking; spiking neural; spatiotemporal patterns; spike trains ... See more keywords