Volume 19 Issue 3, March 2016

Theoretical approaches have long shaped neuroscience, but current needs for theory are elevated and prospects for advancement are bright. Advances in measuring and manipulating neurons demand new models and analyses to guide interpretation. Advances in theoretical neuroscience offer new insights into how signals evolve across areas and new approaches for connecting population activity with behavior. These advances point to a global understanding of brain function based on a hybrid of diverse approaches.

The networks used by computer scientists and by modelers in neuroscience frequently consider unit activities as continuous. Neurons, however, com­municate primarily through discontinuous spiking. This Perspective offers a unifying view of the current methods for transferring our ability to construct functional networks from continuous to more realistic spiking network models.

Recent computational neuroscience developments have used deep neural networks to model neural responses in higher visual areas. This Perspective describes key algorithmic underpinnings in computer vision and artificial intelligence that have contributed to this progress and outlines how deep networks could drive future improvements in understanding sensory cortical processing.

The authors use recent probabilistic theories of neural computation to argue that confidence and certainty are not identical concepts. They propose precise mathematical definitions for both of these concepts and discuss putative neural representations.

Despite representing a minority of cortical cells, inhibitory neurons deeply shape cortical responses. Inhibitory currents closely track excitatory currents, opening only brief windows of opportunity for a neuron to fire. This explains the variability of cortical spike trains, but may also, paradoxically, render a spiking network maximally efficient and precise.

The state of the nervous system shifts constantly. Most studies focus on how state determines the average neural response, with little attention to the trial-to-trial fluctuations of brain activity. We review recent theoretical advances in modeling the physiological mechanisms responsible for state-dependent modulations in the correlated fluctuations of neuronal populations.

What are the challenges associated with storing information over time in the brain? Here the authors explore the computational principles by which biological memory might be built. They develop a high-level view of shared problems and themes in short- and long-term memory and highlight questions for future research.

The complexity of problems and data in psychiatry requires powerful computational approaches. Computational psychiatry is an emerging field encompassing mechanistic theory-driven models and theoretically agnostic data-driven analyses that use machine-learning techniques. Clinical applications will benefit from relating theoretically meaningful process variables to complex psychiatric outcomes through data-driven techniques.

A clinical trial inspired and guided by optogenetics experiments in rodents reports the outcome of targeted transcranial magnetic stimulation in patients suffering from cocaine addiction.

Depression of AMPA receptor–mediated synaptic currents and impairment of long-term potentiation, triggered by amyloid-β, are the hallmarks of Alzheimer's pathophysiology. These dysfunctions are now linked to upregulated PDZ domain–dependent PTEN translocation to spines, contributing to cognitive deficits in model mice.

In this issue of Nature Neuroscience, Eshel et al. characterize the homogeneity with which individual dopamine neurons encode reward prediction error, a teaching signal that is thought to be crucial for associative learning.

The authors defined a roadmap for investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders. Their proof-of-concept study using the largest available common variant data sets for schizophrenia and volumes of several (mainly subcortical) brain structures did not find evidence of genetic overlap.

The loss of nerve cells in the brain is the main event causing life-long deficits and neurological problems after stroke. Weilinger et al. show that NMDA receptors cause nerve cell death during stroke in an unexpected way. Although they require ligand binding and recruitment of downstream pannexin channels, NMDA receptors do not use the receptor's ion channel.

In this study, the authors show that PTEN alters synaptic function after PDZ-dependent recruitment into spines induced by amyloid-β. This mechanism is crucial for pathogenesis, as preventing PTEN-PDZ interactions renders neurons resistant to amyloid-β and rescues cognitive function in Alzheimer's disease models. This suggests that PTEN is a critical effector of the synaptic pathology associated with Alzheimer's disease.

This study shows that learning-induced plasticity of local parvalbumin (PV) basket cells is specifically required for long-term, but not short to intermediate-term, memory consolidation in mice. PV plasticity depends on local D1/5 dopamine receptor signaling 12–14 h after acquisition for its continuance, ensuring enhanced sharp-wave ripple densities and memory consolidation.

Unlike artificial sweeteners, sugar promotes ingestive behavior via both gustatory and post-ingestive pathways. Tellez et al. find that separate basal ganglia circuits mediate the hedonic and nutritional actions of sugar. They demonstrate that sugar recruits a dedicated striatofugal pathway that acts to prioritize calorie-seeking over taste quality.

The role of subcortical acetylcholine in decision-making under uncertainty is ill-defined. By combining genetic tools, computational modeling and a new multi-armed bandit task for mice, the authors show that nicotinic acetylcholine receptors expressed in the ventral tegmental area drive the motivation to seek reward uncertainty.

Dopamine neurons in the ventral tegmental area are thought to signal reward prediction error. The authors show that these neurons respond with striking homogeneity during classical conditioning. All dopamine neurons appear to calculate reward prediction error similarly, enabling robust and consistent broadcasting of this signal throughout the brain.

Wallach et al. use closed-loop artificial whisking in anesthetized rats to show that vibrissal mechanoreceptors extract phase information from on-going whisker kinematics in a frequency- and amplitude-invariant manner. Brainstem paralemniscal neurons preserve this phase information while filtering out information about whisker offset; lemniscal neurons preserve both types of information.

Gene-regulatory elements are drivers of evolutionary divergence, yet where these are located and which are evolutionarily relevant is unclear. In this work, large-scale epigenomic analysis of human, rhesus and chimpanzee brain tissue allowed the identification of human-specific gene-regulatory changes that contributed to the emergence of the human brain.

Heterogeneity within distinct cell populations resident in the central nervous system is increasingly recognized as important for functional diversity, plasticity and sensitivity to neurological disease. The authors demonstrate genome-wide diversity of microglia dependent on brain localization in the young adult and show that aging of microglia occurs in a regionally variable manner.

Recurrent, reciprocal genomic disorders due to non-allelic homologous recombination (NAHR) are a major cause of human disease. The authors developed a CRISPR/Cas9 genome engineering method that directly targets segmental duplications and efficiently mimics the NAHR-mediated mechanism of microdeletion and microduplication that occurs in vivo using 16p11.2 and 15q13.3 as proof-of-principle models.