https://www.selleckchem.com/ALK.html With the advent of deep convolutional neural networks, machines now rival humans in terms of object categorization. The neural networks solve categorization with a hierarchical organization that shares a striking resemblance to their biological counterpart, leading to their status as a standard model of object recognition in biological vision. Despite training on thousands of images of object categories, however, machine-learning networks are poorer generalizers, often fooled by adversarial images with very simple image manipulations that humans easily distinguish as a false image. Humans, on the other hand, can generalize object classes from very few samples. Here we provide a dataset of novel object classifications in humans. We gathered thousands of crowd-sourced human responses to novel objects embedded either with 1 or 16 context sample(s). Human decisions and stimuli together have the potential to be re-used (1) as a tool to better understand the nature of the gap in category learning from few samples between human and machine, and (2) as a benchmark of generalization across machine learning networks. © 2020 The Author(s).Birds have often been recognised as the first informants of climatic change in our environment. Bird species recognition has assumed great significance not just for checking the survival of birds but also as an early warning signal of the declining health of earth and its climate. Earlier researchers have established recognition of bird species based on sounds from repository available online which were region-specific. In this article, we have presented the spike-based bird species recognition model, which deals with the process of identifying the bird species based on their vocalization or call. The dataset comprises of 14 bird species vocalizations. These recordings have been taken in their natural environment. The calls were recorded using a digital recorder and a unidirectional microphone at Central P