AIMC Topic: Electrophysiological Phenomena
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Unsupervised identification of states from voltage recordings of neural networks.
Journal of neuroscience methods
Feb 23, 2019
BACKGROUND: Modern techniques for multi-neuronal recording produce large amounts of data. There is no automatic procedure for the identification of states in recurrent voltage patterns.
A coarse-graining framework for spiking neuronal networks: from strongly-coupled conductance-based integrate-and-fire neurons to augmented systems of ODEs.
Journal of computational neuroscience
Feb 16, 2019
Homogeneously structured, fluctuation-driven networks of spiking neurons can exhibit a wide variety of dynamical behaviors, ranging from homogeneity to synchrony. We extend our partitioned-ensemble average (PEA) formalism proposed in Zhang et al. (Jo...
Network structure and input integration in competing firing rate models for decision-making.
Journal of computational neuroscience
Jan 19, 2019
Making a decision among numerous alternatives is a pervasive and central undertaking encountered by mammals in natural settings. While decision making for two-option tasks has been studied extensively both experimentally and theoretically, characteri...
Electrophysiological Muscle Classification Using Multiple Instance Learning and Unsupervised Time and Spectral Domain Analysis.
IEEE transactions on bio-medical engineering
Feb 5, 2018
OBJECTIVE: Electrophysiological muscle classification (EMC) is a crucial step in the diagnosis of neuromuscular disorders. Existing quantitative techniques are not sufficiently robust and accurate to be reliably clinically used. Here, EMC is modeled ...
Stochastic spike synchronization in a small-world neural network with spike-timing-dependent plasticity.
Neural networks : the official journal of the International Neural Network Society
Oct 12, 2017
We consider the Watts-Strogatz small-world network (SWN) consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). ...
A robot for high yield electrophysiology and morphology of single neurons in vivo.
Nature communications
Jun 1, 2017
Single-cell characterization and perturbation of neurons provides knowledge critical to addressing fundamental neuroscience questions including the structure-function relationship and neuronal cell-type classification. Here we report a robot for effi...
Vowel decoding from single-trial speech-evoked electrophysiological responses: A feature-based machine learning approach.
Brain and behavior
Apr 26, 2017
INTRODUCTION: Scalp-recorded electrophysiological responses to complex, periodic auditory signals reflect phase-locked activity from neural ensembles within the auditory system. These responses, referred to as frequency-following responses (FFRs), ha...
The Sense of Agency as Tracking Control.
PloS one
Oct 14, 2016
Does sense of agency (SoA) arise merely from action-outcome associations, or does an additional real-time process track each step along the chain? Tracking control predicts that deviant intermediate steps between action and outcome should reduce SoA....
Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery.
Movement disorders : official journal of the Movement Disorder Society
Oct 6, 2016
BACKGROUND: Microelectrode recordings along preplanned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson's disease. Usually, the demarcation of the S...
Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals.
Physical review. E
Apr 29, 2016
This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian ei...
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