AIMC Topic: Electrophysiological Phenomena

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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
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
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
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
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
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
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...

Modeling the motor cortex: Optimality, recurrent neural networks, and spatial dynamics.

Neuroscience research
Specialization of motor function in the frontal lobe was first discovered in the seminal experiments by Fritsch and Hitzig and subsequently by Ferrier in the 19th century. It is, however, ironical that the functional and computational role of the mot...

Context-dependent coding and gain control in the auditory system of crickets.

The European journal of neuroscience
Sensory systems process stimuli that greatly vary in intensity and complexity. To maintain efficient information transmission, neural systems need to adjust their properties to these different sensory contexts, yielding adaptive or stimulus-dependent...

A neural network that finds a naturalistic solution for the production of muscle activity.

Nature neuroscience
It remains an open question how neural responses in motor cortex relate to movement. We explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands. To formalize this hypothesis, we tr...

Granger causality-based synaptic weights estimation for analyzing neuronal networks.

Journal of computational neuroscience
Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC ...