Perspectives

Neuroscience can explain cognition by considering single neurons and their connections (a ‘Sherringtonian’ view) or by considering neural spaces constructed by populations of neurons (a ‘Hopfieldian’ view). In this Perspective, Barack and Krakauer argue that the Hopfieldian view has the conceptual resources to explain cognition more fully the Sherringtonian view.

In this Perspective, Koban, Gianaros, Kober and Wager describe neural systems that construct models of the ‘self-in-context’. Such models endow events with personal meaning and enable predictive control over behaviour and peripheral physiology — with implications for health and disease.

The role of the default mode network (DMN) is unclear. In this Perspective, Yeshurun, Nguyen and Hasson review evidence that the DMN integrates extrinsic inputs with intrinsic information over long timescales, enabling it to represent meaning in a way that can be shared between individuals.

Deep neural networks may offer theories of perception, cognition and action for biological brains. Here, Saxe, Nelli and Summerfield offer a road map of how neuroscientists can use deep networks to model and understand biological brains.

Mounting evidence suggests that the gut microbiome impacts brain function, and mechanistic connections between specific microbial by-products and the brain have begun to emerge. In this Perspective, Mazmanian and colleagues discuss the assortment of microbial molecules currently thought to mediate these gut–brain connections.

Major compelling questions about the functional role of the locus coeruleus nucleus that had been difficult to answer, given its remote location and diminutive size, have now become accessible via new neuroscience tools. In this Perspective, 14 investigators provide a historical context for recent discoveries and outline new vistas for investigation.

Reinforcement learning has been suggested to come in two flavours: model-free and model-based. In this Perspective, Collins and Cockburn explain why viewing reinforcement learning through this dichotomous lens is not always accurate or helpful, and suggest paths forward.

Although inputs and outputs that carry social signals are anatomically restricted to distinct subnuclear regions of the amygdala, social behaviours are not. This fact may be explained by the operation of multidimensional processing in parallel with subcircuits of genetically identical neurons that serve specialized and functionally dissociable functions.

In this Perspective, Hanno Würbel and colleagues argue that a disregard for incorporating biological variation in study design is an important cause of poor reproducibility in animal research. They put the case for the use of systematic heterogenization of study samples and conditions in studies to improve reproducibility.

The backpropagation of error (backprop) algorithm is frequently used to train deep neural networks in machine learning, but it has not been viewed as being implemented by the brain. In this Perspective, however, Lillicrap and colleagues argue that the key principles underlying backprop may indeed have a role in brain function.

Prior experience is incorporated into the brain’s predictive models of the world, enabling the accurate interpretation of and responses to new sensory information. In this Perspective, Teufel and Fletcher make the case for an important distinction between two forms of prediction that may advance our understanding of brain function.

Certain biological properties vary across different areas of the cerebral cortex. In this Perspective, Xiao-Jing Wang proposes that macroscopic gradients in some properties align with functional hierarchy and can lead to qualitative differences in function.

Dopamine signals are implicated in not only reporting reward prediction errors but also various probabilistic computations. In this Opinion article, Gershman and Uchida propose that these different roles for dopamine can be placed within a common reinforcement learning framework.

A major challenge in neuroscience is the definition of neuronal types. Here, Paul and Huang give an overview of efforts to classify GABAergic cell types, and propose a framework in which cell types are transcriptionally defined communication elements with characteristic input–output properties.

Successful learning and decision-making require estimates of expected uncertainty and unexpected uncertainty. Soltani and Izquierdo define these concepts, describe proposed models of how they may be computed and discuss their neural substrates.

Studies that examine brain activity during real-time social interactions may advance our understanding of human social behaviour. Redcay and Schilbach describe progress in ‘second-person’ neuroscience and discuss the insights into the brain mechanisms of social behaviour that have been gained.

In this Opinion article, Martijn van den Heuvel and Olaf Sporns examine alterations in structural and functional brain connectivity across brain disorders. They propose a common landscape for such alterations that is based on principles of network organization.

How is the processing of auditory information by the cortex organized? Scott and colleagues describe differences in the connectivity and properties of the rostral and caudal auditory cortex and propose links to the functional specializations of the rostral and caudal auditory streams.

In this Opinion, Yonelinas et al. propose that the hippocampus binds together item-related and content-related information to form memories. They compare the evidence for this contextual binding theory with that for another theory of memory, standard systems consolidation theory.

Environmental enrichment is a classical experimental paradigm for the study of the interaction between genes and the environment. In this Opinion, Kempermann discusses how this paradigm can be further developed in order to capture the essence of interindividual differences in brain function.