Machine learning and artificial intelligence in neuroscience: A primer for researchers DOI Creative Commons

Fakhirah Badrulhisham,

Esther Pogatzki‐Zahn, Daniel Segelcke

et al.

Brain Behavior and Immunity, Journal Year: 2023, Volume and Issue: 115, P. 470 - 479

Published: Nov. 14, 2023

Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute to. Machine learning (ML) commonly understood as a set methods develop an AI. Both have seen recent boom in usage, both scientific and commercial fields. For community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, functional brain imaging or *omics approaches. here identify patterns could not been found using traditional statistic However, comes with serious limitations need be kept mind: their tendency optimise solutions input means it crucial importance externally validate any findings before considering them more than hypothesis. Their black-box nature implies decisions usually cannot understood, which renders use medical decision making problematic lead ethical issues. Here, present introduction curious field ML/AI. We explain principles well methodological advancements discuss risks what see future directions field. Finally, show practical examples neuroscience illustrate ML.

Language: Английский

A MYT1L syndrome mouse model recapitulates patient phenotypes and reveals altered brain development due to disrupted neuronal maturation DOI Creative Commons
Jiayang Chen,

Mary E. Lambo,

Xia Ge

et al.

Neuron, Journal Year: 2021, Volume and Issue: 109(23), P. 3775 - 3792.e14

Published: Oct. 5, 2021

Language: Английский

Citations

56
Neural systems that facilitate the representation of social rank DOI Creative Commons
Madeleine F. Dwortz, James P. Curley, Kay M. Tye

et al.

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2022, Volume and Issue: 377(1845)

Published: Jan. 10, 2022

Across species, animals organize into social dominance hierarchies that serve to decrease aggression and facilitate survival of the group. Neuroscientists have adopted several model organisms study in laboratory setting, including fish, reptiles, rodents primates. We review recent literature across species sheds light onto how brain represents rank guide socially appropriate behaviour within a hierarchy. First, we discuss responds status signals. Then, approach avoidance learning mechanisms propose could drive rank-appropriate behaviour. Lastly, memories individuals (social memory) this may support maintenance unique individual relationships This article is part theme issue ‘The centennial pecking order: current state future prospects for hierarchies’.

Language: Английский

Citations

51
Neural circuits regulating prosocial behaviors DOI Open Access
Jessica J. Walsh, Daniel J. Christoffel, Robert C. Malenka

et al.

Neuropsychopharmacology, Journal Year: 2022, Volume and Issue: 48(1), P. 79 - 89

Published: June 14, 2022

Language: Английский

Citations

44
BehaviorDEPOT is a simple, flexible tool for automated behavioral detection based on markerless pose tracking DOI Creative Commons
Christopher Gabriel, Zachary Zeidler, Benita Jin

et al.

eLife, Journal Year: 2022, Volume and Issue: 11

Published: Aug. 18, 2022

Quantitative descriptions of animal behavior are essential to study the neural substrates cognitive and emotional processes. Analyses naturalistic behaviors often performed by hand or with expensive, inflexible commercial software. Recently, machine learning methods for markerless pose estimation enabled automated tracking freely moving animals, including in labs limited coding expertise. However, classifying specific based on data requires additional computational analyses remains a significant challenge many groups. We developed BehaviorDEPOT (DEcoding POsitional Tracking), simple, flexible software program that can detect from video timeseries analyze results experimental assays. calculates kinematic postural statistics keypoint creates heuristics reliably behaviors. It no programming experience is applicable wide range designs. provide several hard-coded heuristics. Our freezing detection heuristic achieves above 90% accuracy videos mice rats, those wearing tethered head-mounts. also helps researchers develop their own incorporate them into software’s graphical interface. Behavioral stored framewise easy alignment data. demonstrate immediate utility flexibility using popular assays fear conditioning, decision-making T-maze, open field, elevated plus maze, novel object exploration.

Language: Английский

Citations

40
Machine learning and artificial intelligence in neuroscience: A primer for researchers DOI Creative Commons

Fakhirah Badrulhisham,

Esther Pogatzki‐Zahn, Daniel Segelcke

et al.

Brain Behavior and Immunity, Journal Year: 2023, Volume and Issue: 115, P. 470 - 479

Published: Nov. 14, 2023

Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute to. Machine learning (ML) commonly understood as a set methods develop an AI. Both have seen recent boom in usage, both scientific and commercial fields. For community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, functional brain imaging or *omics approaches. here identify patterns could not been found using traditional statistic However, comes with serious limitations need be kept mind: their tendency optimise solutions input means it crucial importance externally validate any findings before considering them more than hypothesis. Their black-box nature implies decisions usually cannot understood, which renders use medical decision making problematic lead ethical issues. Here, present introduction curious field ML/AI. We explain principles well methodological advancements discuss risks what see future directions field. Finally, show practical examples neuroscience illustrate ML.

Language: Английский

Citations

40