Canonical computations of cerebral cortex

https://doi.org/10.1016/j.conb.2016.01.008Get rights and content

Highlights

  • Cortical circuits specialize but may share fundamental properties and computations.

  • We suggest canonical computations by which cortical areas compute selectivity, gain.

  • Hierarchical arrangements of such local computations may allow invariant recognition.

  • Motor/frontal cortex may self-generate activity through enhanced excitation.

  • Lack of columnar organization in rodent cortex could reflect deeper differences.

The idea that there is a fundamental cortical circuit that performs canonical computations remains compelling though far from proven. Here we review evidence for two canonical operations within sensory cortical areas: a feedforward computation of selectivity; and a recurrent computation of gain in which, given sufficiently strong external input, perhaps from multiple sources, intracortical input largely, but not completely, cancels this external input. This operation leads to many characteristic cortical nonlinearities in integrating multiple stimuli. The cortical computation must combine such local processing with hierarchical processing across areas. We point to important changes in moving from sensory cortex to motor and frontal cortex and the possibility of substantial differences between cortex in rodents vs. species with columnar organization of selectivity.

Section snippets

A cortical computational unit?

The cerebral cortex performs a wide range of cognitive tasks in mammals  sensory, motor, and everything in between. Yet it processes these diverse tasks with what appears to be a remarkably uniform, primarily six-layer architecture, albeit with significant differences in details across species and cortical areas [1, 2, 3•, 4, 5, 6, 7, 8, 9, 10, 11•, 12, 13, 14]. Dense connectivity is generally restricted to horizontal extents (parallel to the layers) of a few hundred microns [e.g. 15]. This has

Canonical local computations

Here we review ideas for these two canonical computations within a sensory cortical area: computation of selectivity and computation of gain.

Challenges for understanding canonical cortical computations

The unknowns and challenges greatly outweigh our current understandings. Obviously we need to better understand computations within the local circuit, including those specific for cell type and/or layer; the hierarchical computations across cortical areas, including the role in the latter of feedback [e.g. 19, 124, 125, 126, 127, 128] as well as feedforward connections; and the role of structural variations between cortical areas and across species [e.g. 1, 2, 3•, 4, 5, 6, 7, 8, 9]. Additional

Conclusions

The rapid growth of cortical surface area in mammalian evolution suggests the development of a ‘canonical’ computational unit that, deployed in great numbers both within cortical areas and hierarchically across cortical areas, along with some specializations across species and areas, allowed the evolution of mammalian intelligence. We have outlined here some current ideas as to some elements of this computational unit. We can only hope that in coming decades our understandings will grow more

Conflict of interest

Nothing declared.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

Thanks to Roozbeh Kiani for helpful comments. Work and writing were supported by the Gatsby Charitable Foundation and R01-EY11001.

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