Early experience with low-pass filtered images facilitates visual category learning in a neural network model

The Oomplet dataset toolkit as a flexible and extensible system for large-scale, multi-category image generation

The modern study of perceptual learning across humans, non-human animals, and artificial agents requires large-scale datasets with flexible, customizable, and controllable features for distinguishing between categories. To support this research, we developed the Oomplet Dataset Toolkit (ODT), an open-source, publicly available toolbox capable of generating 9.1 million unique visual stimuli across ten feature dimensions. Each stimulus is a cartoon-like humanoid character, termed an "Oomplet," designed to be an instance within clearly defined visual categories that are engaging and suitable for use with diverse groups, including children. Experiments show that adults can use four to five of the ten dimensions as single classification criteria in simple perceptual discrimination tasks, underscoring the toolkit's flexibility. With the ODT, researchers can dynamically generate large, novel stimulus sets to study perceptual learning across biological and artificial contexts.

Deep Learning-Driven Transformation: A Novel Approach for Mitigating Batch Effects in Diffusion MRI Beyond Traditional Harmonization

BACKGROUND

"Batch effect" in MR images, due to vendor-specific features, MR machine generations, and imaging parameters, challenges image quality and hinders deep learning (DL) model generalizability.

PURPOSE

We aim to develop a DL model using contrast adjustment and super-resolution to reduce diffusion-weighted images (DWIs) diversity across magnetic field strengths and imaging parameters.

STUDY TYPE

Retrospective.

SUBJECTS

The DL model was built using an open dataset from one individual. The MR machine identification model was trained and validated on a dataset of 1134 adults (54% females, 46% males), with 1050 subjects showing no DWI abnormalities and 84 with conditions like stroke and tumors. The 21,000 images were divided into 80% for training, 20% for validation, and 3500 for testing.

FIELD STRENGTH/SEQUENCE

Seven MR scanners from four manufacturers with 1.5 T and 3 T magnetic field strengths. DWIs were acquired using spin-echo sequences and high-resolution T2WIs using the T2-SPACE sequence.

ASSESSMENT

An experienced, board-certified radiologist evaluated the effectiveness of restoring high-resolution T2WI and harmonizing diverse DWI with metrics such as PSNR and SSIM, and the texture and frequency attributes were further analyzed using gray-level co-occurrence matrix and 1-dimensional power spectral density. The model's impact on machine-specific characteristics was gauged through the performance metrics of a ResNet-50 model. Comprehensive statistical tests were employed for statistical robustness, including McNemar's test and the Dice index.

RESULTS

Our DL protocol reduced DWI contrast and resolution variation. ResNet-50 model's accuracy decreased from 0.9443 to 0.5786, precision from 0.9442 to 0.6494, recall from 0.9443 to 0.5786, and F1 score from 0.9438 to 0.5587. The t-SNE visualization indicated more consistent image features across multiple MR devices. Autoencoder halved learning iterations; Dice coefficient >0.74 confirmed signal reproducibility in 84 lesions.

CONCLUSION

This study presents a DL strategy to mitigate batch effects in diffusion MR images, improving their quality and generalizability.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 1.

An edge-simplicity bias in the visual input to young infants

The development of sparse edge coding in the mammalian visual cortex depends on early visual experience. In humans, there are multiple indicators that the statistics of early visual experiences has unique properties that may support these developments. However, there are no direct measures of the edge statistics of infant daily-life experience. Using head-mounted cameras to capture egocentric images of young infants and adults in the home, we found infant images to have distinct edge statistics relative to adults. For infants, scenes with sparse edge patterns-few edges and few orientations-dominate. The findings implicate biased early input at the scale of daily life that is likely specific to the early months after birth and provide insights into the quality, amount, and timing of the visual experiences during the foundational developmental period for human vision.

Going after the bigger picture: Using high-capacity models to understand mind and brain

Deep neural networks (DNNs) provide a unique opportunity to move towards a generic modelling framework in psychology. The high representational capacity of these models combined with the possibility for further extensions has already allowed us to investigate the forest, namely the complex landscape of representations and processes that underlie human cognition, without forgetting about the trees, which include individual psychological phenomena.

A Unifying Principle for the Functional Organization of Visual Cortex

A key feature of many cortical systems is functional organization: the arrangement of neurons with specific functional properties in characteristic spatial patterns across the cortical surface. However, the principles underlying the emergence and utility of functional organization are poorly understood. Here we develop the Topographic Deep Artificial Neural Network (TDANN), the first unified model to accurately predict the functional organization of multiple cortical areas in the primate visual system. We analyze the key factors responsible for the TDANN's success and find that it strikes a balance between two specific objectives: achieving a task-general sensory representation that is self-supervised, and maximizing the smoothness of responses across the cortical sheet according to a metric that scales relative to cortical surface area. In turn, the representations learned by the TDANN are lower dimensional and more brain-like than those in models that lack a spatial smoothness constraint. Finally, we provide evidence that the TDANN's functional organization balances performance with inter-area connection length, and use the resulting models for a proof-of-principle optimization of cortical prosthetic design. Our results thus offer a unified principle for understanding functional organization and a novel view of the functional role of the visual system in particular.