From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction

Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photographs, drawings, and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images, such as drawings, seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network

The neuroconnectionist research programme

rtificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call ‘neuroconnectionism’. ANNs have been not only lauded as the current best models of information processing in the brain but also criticized for failing to account for basic cognitive functions. In this Perspective article, we propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism for brain science. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of a scientific research programme is often not directly falsifiable but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a general research programme centred around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses and deriving novel understanding. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain

Seed production and seed quality of the dune building grass Panicum racemosum Spreng

Seed production, pollination requirement, seed characteristics related to quality and the relationship between number and mass of seeds were examined for Panicum racemosum in three successional populations in southern Brazilian coastal dunes. The seed production was generally low and declined further between the frontal dunes and the backdunes, dropping from 4.05 seeds per panicle in the former to 1.8 seeds in the latter. However fertility (% fertile florets) did not differ among the three habitats. Plants cross-pollinated in a glasshouse showed an increase in seed production to 41.4 seeds compared to no seed production in self-pollinated plants. Caryopses varied in mass from 3.2 to 12.2 mg with a mean of 7.98 mg. A strong negative correlation was found between mean individual seed mass and the total number of seeds per panicle in a natural population. However, this relationship did not persist in seeds produced by cultivated plants in the glasshouse. The causes of low seed production appear to be mainly pollen self-incompatibility and additionally competition for nutrients between sexual reproduction and allocation to clonal growth. Under conditions of nutrient shortage, Panicum racemosum probably allocates resources more to clonal growth and to fewer, but well-endowed seeds. This would permit emergence from deeper burial sand, faster growth and greater survival of seedlings