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Large Scale Image Segmentation with Structured Loss Based Deep Learning for Connectome Reconstruction.

IEEE transactions on pattern analysis and machine intelligence
We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability. Our method consists of a 3D U-Net, t...

Patterns of thought: Population variation in the associations between large-scale network organisation and self-reported experiences at rest.

NeuroImage
Contemporary cognitive neuroscience recognises unconstrained processing varies across individuals, describing variation in meaningful attributes, such as intelligence. It may also have links to patterns of on-going experience. This study examined whe...

How Much Does Movement and Location Encoding Impact Prefrontal Cortex Activity? An Algorithmic Decoding Approach in Freely Moving Rats.

eNeuro
Specialized brain structures encode spatial locations and movements, yet there is growing evidence that this information is also represented in the rodent medial prefrontal cortex (mPFC). Disambiguating such information from the encoding of other typ...

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

eNeuro
Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is...

A unified hierarchical oscillatory network model of head direction cells, spatially periodic cells, and place cells.

The European journal of neuroscience
Spatial cells in the hippocampal complex play a pivotal role in the navigation of an animal. Exact neural principles behind these spatial cell responses have not been completely unraveled yet. Here we present two models for spatial cells, namely the ...

A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection.

International journal of neural systems
Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of t...

Scale-freeness or partial synchronization in neural mass phase oscillator networks: Pick one of two?

NeuroImage
Modeling and interpreting (partial) synchronous neural activity can be a challenge. We illustrate this by deriving the phase dynamics of two seminal neural mass models: the Wilson-Cowan firing rate model and the voltage-based Freeman model. We establ...

Resonance with subthreshold oscillatory drive organizes activity and optimizes learning in neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations' spiking activity an...

Electroencephalographic derived network differences in Lewy body dementia compared to Alzheimer's disease patients.

Scientific reports
Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) require differential management despite presenting with symptomatic overlap. Currently, there is a need of inexpensive DLB biomarkers which can be fulfilled by electroencephalography (EEG)....

Dynamic functional network connectivity discriminates mild traumatic brain injury through machine learning.

NeuroImage. Clinical
Mild traumatic brain injury (mTBI) can result in symptoms that affect a person's cognitive and social abilities. Improvements in diagnostic methodologies are necessary given that current clinical techniques have limited accuracy and are solely based ...