AIMC Topic: Neurosciences

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Finding the needle in a high-dimensional haystack: Canonical correlation analysis for neuroscientists.

NeuroImage
The 21st century marks the emergence of "big data" with a rapid increase in the availability of datasets with multiple measurements. In neuroscience, brain-imaging datasets are more commonly accompanied by dozens or hundreds of phenotypic subject des...

Before and beyond the Wilson-Cowan equations.

Journal of neurophysiology
The Wilson-Cowan equations represent a landmark in the history of computational neuroscience. Along with the insights Wilson and Cowan offered for neuroscience, they crystallized an approach to modeling neural dynamics and brain function. Although th...

Tensors and compositionality in neural systems.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Neither neurobiological nor process models of meaning composition specify the operator through which constituent parts are bound together into compositional structures. In this paper, we argue that a neurophysiological computation system cannot achie...

Hierarchical motor control in mammals and machines.

Nature communications
Advances in artificial intelligence are stimulating interest in neuroscience. However, most attention is given to discrete tasks with simple action spaces, such as board games and classic video games. Less discussed in neuroscience are parallel advan...

Deep learning improves automated rodent behavior recognition within a specific experimental setup.

Journal of neuroscience methods
Automated observation and analysis of rodent behavior is important to facilitate research progress in neuroscience and pharmacology. Available automated systems lack adaptivity and can benefit from advances in AI. In this work we compare a state-of-t...

Deep learning tools for the measurement of animal behavior in neuroscience.

Current opinion in neurobiology
Recent advances in computer vision have made accurate, fast and robust measurement of animal behavior a reality. In the past years powerful tools specifically designed to aid the measurement of behavior have come to fruition. Here we discuss how capt...

Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype.

Schizophrenia research
Despite extensive research and prodigious advances in neuroscience, our comprehension of the nature of schizophrenia remains rudimentary. Our failure to make progress is attributed to the extreme heterogeneity of this condition, enormous complexity o...

Utilizing supervised machine learning to identify microglia and astrocytes in situ: implications for large-scale image analysis and quantification.

Journal of neuroscience methods
BACKGROUND: The evaluation of histological tissue samples plays a crucial role in deciphering preclinical disease and injury mechanisms. High-resolution images can be obtained quickly however data acquisition are often bottlenecked by manual analysis...

An automatic behavior recognition system classifies animal behaviors using movements and their temporal context.

Journal of neuroscience methods
Animals can perform complex and purposeful behaviors by executing simpler movements in flexible sequences. It is particularly challenging to analyze behavior sequences when they are highly variable, as is the case in language production, certain type...