AIMC Topic: Spatial Learning

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Semantic-spatial feature-fused cortical surface parcellation: a scale-unified spatial learning network with boundary contrastive loss.

Medical & biological engineering & computing Nov 16, 2024
The cortical surface parcellation provides prior guidance for studying mental disorders and human cognition. Graph neural networks (GNNs) have gained popularity in this task to preserve its spatial structure. However, previous GNNs struggled to effec...
Neural Networks, Computer Algorithms Humans Semantics Cerebral Cortex Spatial Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Unbiased analysis of spatial learning strategies in a modified Barnes maze using convolutional neural networks.

Scientific reports Jul 10, 2024
Assessment of spatial learning abilities is central to behavioral neuroscience and a useful tool for animal model validation and drug development. However, biases introduced by the apparatus, environment, or experimentalist represent a critical chall...
Neural Networks, Computer Spatial Learning Disease Models, Animal Mice, Inbred C57BL Behavior, Animal Maze Learning Alzheimer Disease Male Mice Female Animals
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Quaternion Cross-Modality Spatial Learning for Multi-Modal Medical Image Segmentation.

IEEE journal of biomedical and health informatics Mar 6, 2024
Recently, the Deep Neural Networks (DNNs) have had a large impact on imaging process including medical image segmentation, and the real-valued convolution of DNN has been extensively utilized in multi-modal medical image segmentation to accurately se...
Image Processing, Computer-Assisted Spatial Learning Humans Neural Networks, Computer
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Compositional diversity in visual concept learning.

Cognition Jan 14, 2024
Humans leverage compositionality to efficiently learn new concepts, understanding how familiar parts can combine together to form novel objects. In contrast, popular computer vision models struggle to make the same types of inferences, requiring more...
Generalization, Psychological Spatial Learning Bayes Theorem Concept Formation Humans Neural Networks, Computer
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How well do rudimentary plasticity rules predict adult visual object learning?

PLoS computational biology Dec 11, 2023
A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images. One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains t...
Primates Spatial Learning Visual Perception Pattern Recognition, Visual Animals Models, Neurological Brain Humans Neural Networks, Computer Adult
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Early experience with low-pass filtered images facilitates visual category learning in a neural network model.

PloS one Jan 6, 2023
Humans are born with very low contrast sensitivity, meaning that inputs to the infant visual system are both blurry and low contrast. Is this solely a byproduct of maturational processes or is there a functional advantage for beginning life with poor...
Humans Spatial Learning Infant Neural Networks, Computer Time Infant, Newborn
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Contrastive language and vision learning of general fashion concepts.

Scientific reports Nov 8, 2022
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from gen...
Natural Language Processing Generalization, Psychological Spatial Learning Language
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London taxi drivers: A review of neurocognitive studies and an exploration of how they build their cognitive map of London.

Hippocampus Dec 16, 2021
Licensed London taxi drivers have been found to show changes in the gray matter density of their hippocampus over the course of training and decades of navigation in London (UK). This has been linked to their learning and using of the "Knowledge of L...
Humans Spatial Learning Artificial Intelligence Magnetic Resonance Imaging Cognition London
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Seeing through disguise: Getting to know you with a deep convolutional neural network.

Cognition Feb 13, 2021
People use disguise to look unlike themselves (evasion) or to look like someone else (impersonation). Evasion disguise challenges human ability to see an identity across variable images; Impersonation challenges human ability to tell people apart. Pe...
Neural Networks, Computer Facial Recognition Humans Recognition, Psychology Spatial Learning
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EO-MTRNN: evolutionary optimization of hyperparameters for a neuro-inspired computational model of spatiotemporal learning.

Biological cybernetics Mar 17, 2020
For spatiotemporal learning with neural networks, hyperparameters are often set manually by a human expert. This is especially the case with multiple timescale networks that require a careful setting of the values of timescales in order to learn spat...
Spatial Learning Models, Neurological Psychomotor Performance Humans Databases, Factual Time Perception Neural Networks, Computer Robotics Time Factors
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