Stimulus feature-specific information flow along the columnar cortical microcircuit revealed by multivariate laminar spiking analysis
Most of the mammalian neocortex is comprised of a highly similar anatomical structure,
consisting of a granular cell layer between superficial and deep layers. Even so, different …
consisting of a granular cell layer between superficial and deep layers. Even so, different …
Circumstantial evidence and explanatory models for synapses in large-scale spike recordings
IH Stevenson - arXiv preprint arXiv:2304.09699, 2023 - arxiv.org
Whether, when, and how causal interactions between neurons can be meaningfully studied
from observations of neural activity alone are vital questions in neural data analysis. Here …
from observations of neural activity alone are vital questions in neural data analysis. Here …
Dynamic Parameter Estimation of Brain Mechanisms
PY Hsu - arXiv preprint arXiv:1909.11899, 2019 - arxiv.org
Demystifying effective connectivity among neuronal populations has become the trend to
understand the brain mechanisms of Parkinson's disease, schizophrenia, mild traumatic …
understand the brain mechanisms of Parkinson's disease, schizophrenia, mild traumatic …
Temporally Smoothed Wavelet Coherence for Multivariate Point-Processes and Neuron-Firing
AJ Gibberd, EAK Cohen - 2018 52nd Asilomar Conference on …, 2018 - ieeexplore.ieee.org
In neuroscience, it is of key importance to assess how neurons interact with each other as
evidenced via their firing patterns and rates. We here introduce a method of smoothing the …
evidenced via their firing patterns and rates. We here introduce a method of smoothing the …
A computationally efficient method for brain information-theoretic based causality detection using multichannel EEG
M Songhorzadeh, K Ansari-Asl… - 2015 22nd Iranian …, 2015 - ieeexplore.ieee.org
Information flow or causal interaction between neuronal populations of the brain is a critical
issue in describing the dynamics of such a complex network, which can be best described …
issue in describing the dynamics of such a complex network, which can be best described …
Algorithms and bounds for dynamic causal modeling of brain connectivity
SC Wu, AL Swindlehurst - IEEE transactions on signal …, 2013 - ieeexplore.ieee.org
Recent advances in neurophysiology have led to the development of complex dynamical
models that describe the connections and causal interactions between different regions of …
models that describe the connections and causal interactions between different regions of …
Dynamic organization of visual cortical networks revealed by machine learning applied to massive spiking datasets
Complex cognitive functions in a mammalian brain are distributed across many anatomically
and functionally distinct areas and rely on highly dynamic routing of neural activity across …
and functionally distinct areas and rely on highly dynamic routing of neural activity across …
Swift Two-sample Test on High-dimensional Neural Spiking Data
To understand how neural networks process information, it is important to investigate how
neural network dynamics varies with respect to different stimuli. One challenging task is to …
neural network dynamics varies with respect to different stimuli. One challenging task is to …
Uncovering the organization of neural circuits with generalized phase locking analysis
Despite the considerable progress of in vivo neural recording techniques, inferring the
biophysical mechanisms underlying large scale coordination of brain activity from neural …
biophysical mechanisms underlying large scale coordination of brain activity from neural …
Information theoretic measures of causal influences during transient neural events
K Shao, NK Logothetis, M Besserve - Frontiers in Network Physiology, 2023 - frontiersin.org
Introduction: Transient phenomena play a key role in coordinating brain activity at multiple
scales, however their underlying mechanisms remain largely unknown. A key challenge for …
scales, however their underlying mechanisms remain largely unknown. A key challenge for …