A computational exploration of complementary learning mechanisms in the primate ventral visual pathway

In order to develop transformation invariant representations of objects, the visual system must make use of constraints placed upon object transformation by the environment. For example, objects transform continuously from one point to another in both space and time. These two constraints have been exploited separately in order to develop translation and view invariance in a hierarchical multilayer model of the primate ventral visual pathway in the form of continuous transformation learning and temporal trace learning. We show for the first time that these two learning rules can work cooperatively in the model. Using these two learning rules together can support the development of invariance in cells and help maintain object selectivity when stimuli are presented over a large number of locations or when trained separately over a large number of viewing angles

Recurrence is required to capture the representational dynamics of the human visual system.

The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform parameter-matched feedforward models in terms of their ability to capture the multiregion cortical dynamics. Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections. These results establish that recurrent models are required to understand information processing in the human ventral stream

Enhancing Monte Carlo Tree Search for Retrosynthesis

Computer-Assisted Synthesis Programs are increasingly employed by organic chemists. Often, these tools combine neural networks for policy prediction with heuristic search algorithms. We propose two novel enhancements, which we call eUCT and dUCT, to the Monte Carlo tree search (MCTS) algorithm. The enhancements were deployed in AiZynthFinder and have been integrated into the open-source electronic lab notebook, AI4Green, available at https://ai4green.app. A memory-efficient stock file was used to reduce the computational carbon footprint. Both enhancements significantly reduced, by up to 50%, the computational clock-time to solve 1500 heavy (500–800 Da) molecules. The dUCT enhancement increased the number of routes found per molecule for the 1500 heavy molecules and a 50,000-molecule set from ChEMBL. eUCT and dUCT-v2 solved between 600 and 900 more molecules than the unenhanced MCTS algorithm across the 50,000 molecules. When limited to a 150 s time constraint, dUCT-v1 solved ∼5 million more routes to the 50,000 targets than the unenhanced algorithm

Recurrent Connections Aid Occluded Object Recognition by Discounting Occluders

Recurrent connections in the visual cortex are thought to aid object recognition when part of the stimulus is occluded. Here we investigate if and how recurrent connections in artificial neural networks similarly aid object recognition. We systematically test and compare architectures comprised of bottom-up (B), lateral (L) and top-down (T) connections. Performance is evaluated on a novel stereoscopic occluded object recognition dataset. The task consists of recognizing one target digit occluded by multiple occluder digits in a pseudo-3D environment. We find that recurrent models perform significantly better than their feedforward counterparts, which were matched in parametric complexity. Furthermore, we analyze how the network's representation of the stimuli evolves over time due to recurrent connections. We show that the recurrent connections tend to move the network's representation of an occluded digit towards its un-occluded version. Our results suggest that both the brain and artificial neural networks can exploit recurrent connectivity to aid occluded object recognition.Comment: 13 pages, 5 figures, accepted at the 28th International Conference on Artificial Neural Networks, published in Springer Lecture Notes in Computer Science vol 1172

Where Nothing Happened: The Experience of War Captivity and Levinas’s Concept of the ‘There Is’

This article takes as its subject matter the juridico-political space of the prisoner of war (POW) camp. It sets out to determine the nature of this space by looking at the experience of war captivity by Jewish members of the Western forces in World War II, focusing on the experience of Emmanuel Levinas, who spent 5 years in German war captivity. On the basis of a historical analysis of the conditions in which Levinas spent his time in captivity, it argues that the POW camp was a space of indifference that was determined by the legal exclusion of prisoners from both war and persecution. Held behind the stage of world events, prisoners were neither able to exercise their legal agency nor released from law into a realm of extra-legal violence. Through a close reading of Levinas’s early concept of the ‘there is’ [il y a], the article seeks to establish the impact on prisoners of prolonged confinement in such a space. It sets out how prisoners’ subjectivity dissolved in the absence of meaningful relations with others and identifies the POW camp as a space in which existence was reduced to indeterminate, impersonal being

On the causes of economic growth in Europe: why did agricultural labour productivity not converge between 1950 and 2005?

The objective of this study is to make a further contribution to the debate on the causes of economic growth in the European Continent. It explains why agricultural labour productivity differences did not converge between 1950 and 2005 in Europe. We propose an econometric model, one combining both proximate and fundamental causes of economic growth. The results show that the continuous exit of labour power from the sector, coupled with the increased use of productive factors originating in other sectors of the economy, caused the efficiency of agricultural workers to rise. However, we offer a complete explanation of the role played by institutions and geographical factors. Thus, we detect a direct and inverse relation between membership of the EU and the Communist bloc and the productivity of agricultural labour. In addition, strong support for agriculture affected productivity negatively

The role of war in deep transitions: exploring mechanisms, imprints and rules in sociotechnical systems

This paper explores in what ways the two world wars influenced the development of sociotechnical systems underpinning the culmination of the first deep transition. The role of war is an underexplored aspect in both the Techno-Economic Paradigms (TEP) approach and the Multi-level perspective (MLP) which form the two key conceptual building blocks of the Deep Transitions (DT) framework. Thus, we develop a conceptual approach tailored to this particular topic which integrates accounts of total war and mechanisms of war from historical studies and imprinting from organisational studies with the DT framework’s attention towards rules and meta-rules. We explore in what ways the three sociotechnical systems of energy, food, and transport were affected by the emergence of new demand pressures and logistical challenges during conditions of total war; how war impacted the directionality of sociotechnical systems; the extent to which new national and international policy capacities emerged during wartime in the energy, food, and transport systems; and the extent to which these systems were influenced by cooperation and shared sacrifice under wartime conditions. We then explore what lasting changes were influenced by the two wars in the energy, food, and transport systems across the transatlantic zone. This paper seeks to open up a hitherto neglected area in analysis on sociotechnical transitions and we discuss the importance of further research that is attentive towards entanglements of warfare and the military particularly in the field of sustainability transitions