Table of contents


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Focus

Focus on neural computation and theory

Advances in neuroscience research cannot progress without data collection. But we also require sophisticated ways of assembling and synthesizing these data into broader frameworks. Theoretical neuroscience, along with the requisite computational techniques, serves to ensure that our endeavors are more than large-scale stamp collecting. In this issue, Nature Neuroscience presents a series of reviews and perspectives that highlight the current thinking on topics that range from neural circuits and networks to cognitive estimation and psychiatric illness.

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Editorial

Focus on neural computation and theory

Focus on neural computation and theory p347

doi:10.1038/nn.4261

We present a special issue focusing on recent advances in computation- and theory-driven approaches to neuroscience that inform a host of biophysical and mechanistic models.


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Commentary

Focus on neural computation and theory

Conceptual and technical advances define a key moment for theoretical neuroscience pp348 - 349

Anne K Churchland & L F Abbott

doi:10.1038/nn.4255

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.


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Perspectives

Focus on neural computation and theory

Building functional networks of spiking model neurons pp350 - 355

L F Abbott, Brian DePasquale & Raoul-Martin Memmesheimer

doi:10.1038/nn.4241

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.


Focus on neural computation and theory

Using goal-driven deep learning models to understand sensory cortex pp356 - 365

Daniel L K Yamins & James J DiCarlo

doi:10.1038/nn.4244

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.


Focus on neural computation and theory

Confidence and certainty: distinct probabilistic quantities for different goals pp366 - 374

Alexandre Pouget, Jan Drugowitsch & Adam Kepecs

doi:10.1038/nn.4240

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.


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Reviews

Focus on neural computation and theory

Efficient codes and balanced networks pp375 - 382

Sophie Denève & Christian K Machens

doi:10.1038/nn.4243

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.


Focus on neural computation and theory

The mechanics of state-dependent neural correlations pp383 - 393

Brent Doiron, Ashok Litwin-Kumar, Robert Rosenbaum, Gabriel K Ocker & Krešimir Josić

doi:10.1038/nn.4242

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.


Focus on neural computation and theory

Computational principles of memory pp394 - 403

Rishidev Chaudhuri & Ila Fiete

doi:10.1038/nn.4237

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.


Focus on neural computation and theory

Computational psychiatry as a bridge from neuroscience to clinical applications pp404 - 413

Quentin J M Huys, Tiago V Maia & Michael J Frank

doi:10.1038/nn.4238

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.


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News and Views

Illuminating next-generation brain therapies pp414 - 416

Emily Ferenczi & Karl Deisseroth

doi:10.1038/nn.4232

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.


Targeting PTEN interactions for Alzheimer's disease pp416 - 418

Samuel Frere & Inna Slutsky

doi:10.1038/nn.4248

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.

See also: Article by Knafo et al.


Gaining on reward prediction errors pp418 - 419

Nathan F Parker & Ilana B Witten

doi:10.1038/nn.4246

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.

See also: Article by Eshel et al.


Schizophrenia and brain volume genetic covariation p419

P Alexander Arguello

doi:10.1038/nn0316-419

See also: Article by Franke et al.


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Articles

Genetic influences on schizophrenia and subcortical brain volumes: large-scale proof of concept pp420 - 431

Barbara Franke, Jason L Stein, Stephan Ripke, Verneri Anttila, Derrek P Hibar, Kimm J E van Hulzen, Alejandro Arias-Vasquez, Jordan W Smoller, Thomas E Nichols, Michael C Neale, Andrew M McIntosh, Phil Lee, Francis J McMahon, Andreas Meyer-Lindenberg, Manuel Mattheisen, Ole A Andreassen, Oliver Gruber, Perminder S Sachdev, Roberto Roiz-Santiañez, Andrew J Saykin, Stefan Ehrlich, Karen A Mather, Jessica A Turner, Emanuel Schwarz, Anbupalam Thalamuthu, Yin Yao, Yvonne Y W Ho, Nicholas G Martin, Margaret J Wright, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Enigma Consortium, Michael C O'Donovan, Paul M Thompson, Benjamin M Neale, Sarah E Medland & Patrick F Sullivan

doi:10.1038/nn.4228

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.

See also: News and Views by Arguello


Metabotropic NMDA receptor signaling couples Src family kinases to pannexin-1 during excitotoxicity pp432 - 442

Nicholas L Weilinger, Alexander W Lohman, Brooke D Rakai, Evelyn M M Ma, Jennifer Bialecki, Valentyna Maslieieva, Travis Rilea, Mischa V Bandet, Nathan T Ikuta, Lucas Scott, Michael A Colicos, G Campbell Teskey, Ian R Winship & Roger J Thompson

doi:10.1038/nn.4236

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.


PTEN recruitment controls synaptic and cognitive function in Alzheimer's models pp443 - 453

Shira Knafo, Cristina Sánchez-Puelles, Ernest Palomer, Igotz Delgado, Jonathan E Draffin, Janire Mingo, Tina Wahle, Kanwardeep Kaleka, Liping Mou, Inmaculada Pereda-Perez, Edvin Klosi, Erik B Faber, Heidi M Chapman, Laura Lozano-Montes, Ana Ortega-Molina, Lara Ordóñez-Gutiérrez, Francisco Wandosell, Jose Viña, Carlos G Dotti, Randy A Hall, Rafael Pulido, Nashaat Z Gerges, Andrew M Chan, Mark R Spaller, Manuel Serrano, César Venero & José A Esteban

doi:10.1038/nn.4225

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.

See also: News and Views by Frere & Slutsky


PV plasticity sustained through D1/5 dopamine signaling required for long-term memory consolidation pp454 - 464

Smitha Karunakaran, Ananya Chowdhury, Flavio Donato, Charles Quairiaux, Christoph M Michel & Pico Caroni

doi:10.1038/nn.4231

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.


Separate circuitries encode the hedonic and nutritional values of sugar pp465 - 470

Luis A Tellez, Wenfei Han, Xiaobing Zhang, Tatiana L Ferreira, Isaac O Perez, Sara J Shammah-Lagnado, Anthony N van den Pol & Ivan E de Araujo

doi:10.1038/nn.4224

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.


Nicotinic receptors in the ventral tegmental area promote uncertainty-seeking pp471 - 478

Jérémie Naudé, Stefania Tolu, Malou Dongelmans, Nicolas Torquet, Sébastien Valverde, Guillaume Rodriguez, Stéphanie Pons, Uwe Maskos, Alexandre Mourot, Fabio Marti & Philippe Faure

doi:10.1038/nn.4223

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 share common response function for reward prediction error pp479 - 486

Neir Eshel, Ju Tian, Michael Bukwich & Naoshige Uchida

doi:10.1038/nn.4239

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.

See also: News and Views by Parker & Witten


On-going computation of whisking phase by mechanoreceptors pp487 - 493

Avner Wallach, Knarik Bagdasarian & Ehud Ahissar

doi:10.1038/nn.4221

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.


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Resources

Epigenomic annotation of gene regulatory alterations during evolution of the primate brain pp494 - 503

Marit W Vermunt, Sander C Tan, Bas Castelijns, Geert Geeven, Peter Reinink, Ewart de Bruijn, Ivanela Kondova, Stephan Persengiev, Netherlands Brain Bank, Ronald Bontrop, Edwin Cuppen, Wouter de Laat & Menno P Creyghton

doi:10.1038/nn.4229

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.


Microglial brain region−dependent diversity and selective regional sensitivities to aging pp504 - 516

Kathleen Grabert, Tom Michoel, Michail H Karavolos, Sara Clohisey, J Kenneth Baillie, Mark P Stevens, Tom C Freeman, Kim M Summers & Barry W McColl

doi:10.1038/nn.4222

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.


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Technical Report

Engineering microdeletions and microduplications by targeting segmental duplications with CRISPR pp517 - 522

Derek J C Tai, Ashok Ragavendran, Poornima Manavalan, Alexei Stortchevoi, Catarina M Seabra, Serkan Erdin, Ryan L Collins, Ian Blumenthal, Xiaoli Chen, Yiping Shen, Mustafa Sahin, Chengsheng Zhang, Charles Lee, James F Gusella & Michael E Talkowski

doi:10.1038/nn.4235

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.


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