Focus on neural computation and theory

Content

• Focus on neural computation and theory

  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.

  Editorial23 Feb 2016 Nature Neuroscience
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  Conceptual and technical advances define a key moment for theoretical neuroscience

  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.

  • Anne K Churchland
  • L F Abbott

  Commentary23 Feb 2016 Nature Neuroscience
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  Building functional networks of spiking model neurons

  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.

  • L F Abbott
  • Brian DePasquale
  • Raoul-Martin Memmesheimer

  Perspective23 Feb 2016 Nature Neuroscience
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  Using goal-driven deep learning models to understand sensory cortex

  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.

  • Daniel L K Yamins
  • James J DiCarlo

  Perspective23 Feb 2016 Nature Neuroscience
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  Confidence and certainty: distinct probabilistic quantities for different goals

  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.

  • Alexandre Pouget
  • Jan Drugowitsch
  • Adam Kepecs

  Perspective23 Feb 2016 Nature Neuroscience
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  Efficient codes and balanced networks

  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.

  • Sophie Denève
  • Christian K Machens

  Review Article23 Feb 2016 Nature Neuroscience
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  The mechanics of state-dependent neural correlations

  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.

  • Brent Doiron
  • Ashok Litwin-Kumar
  • Krešimir Josić

  Review Article23 Feb 2016 Nature Neuroscience
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  Computational principles of memory

  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.

  • Rishidev Chaudhuri
  • Ila Fiete

  Review Article23 Feb 2016 Nature Neuroscience
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  Computational psychiatry as a bridge from neuroscience to clinical applications

  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.

  • Quentin J M Huys
  • Tiago V Maia
  • Michael J Frank

  Review Article23 Feb 2016 Nature Neuroscience