The brains of mammals are very efficient learning machines. Many aspects of mammalian learning are yet to be incorporated into machine learning algorithms. For instance, vision is typically considered to be a spatial problem in which a learning system needs to be trained with labeled examples of ...
Each dataset comprises two sets of labels obtained from two different observers, with the first observer considered as the ground truth (GT). The DRIVE dataset is a commonly used dataset for retinal vessel segmentation, consisting of a total of 40 labeled retinal vessel images, each with a ...
Deep neural network models of sensory systems are often proposed to learn representational transformations with invariances like those in the brain. To reveal these invariances, we generated ‘model metamers’, stimuli whose activations within a model stage are matched to those of a natural stimulus....
In practice, many LLMs use a combination of both unsupervised and supervised learning. The model might first undergo unsupervised pre-training on large text datasets to learn general language patterns, followed by supervised fine-tuning on task-specific labeled data. Advantages of Large Language Mode...
However, MANF does not penetrate the blood–brain barrier. So far, MANF therapy has been administered by intracranial delivery, which is highly invasive and thus not a realistic approach for therapeutic use. Intranasal delivery of several other proteins is neuroprotective in rat and mouse transient...
In supervised and semi-supervised learning, this training data must be thoughtfully labeled by data scientists to optimize results. Given proper feature extraction, supervised learning requires a lower quantity of training data overall than unsupervised learning. ...
At present, approximately 4000 odorants have been labeled with their corresponding odor. The smells of odorants have been labeled with odor descriptors (ODs), such as ‘sweet,’‘fruity,’ and ‘green.’ These data introduce the possibility of using data-driven approaches in molecular structure-...
The activity of midbrain dopamine neurons, as reflected in levels of extracellular dopamine concentration and the fMRI BOLD signals in their target areas, is hypothesized to represent a reward prediction error [1] or, alternatively, confidence in a prediction of a desired outcome [2]. The firing...
A. et al. Unitopatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading. In 2021 IEEE Int. Conf. Image Processing (ICIP) (eds alZahir, S., Labeau, F. & Mock, K.) 76–80 (IEEE, 2021). Brancati, N. et al. BRACS: a dataset for ...
In connectomics, the study of the network structure of connected neurons, great advances are being made on two different scales: that of macro- and meso-scale connectomics, studying the connectivity between populations of neurons, and that of micro-scale