Linear readout of object manifolds

Sueyeon Chung, Daniel D. Lee, Haim Sompolinsky

Research output: Contribution to journalArticlepeer-review


Objects are represented in sensory systems by continuous manifolds due to sensitivity of neuronal responses to changes in physical features such as location, orientation, and intensity. What makes certain sensory representations better suited for invariant decoding of objects by downstream networks? We present a theory that characterizes the ability of a linear readout network, the perceptron, to classify objects from variable neural responses. We show how the readout perceptron capacity depends on the dimensionality, size, and shape of the object manifolds in its input neural representation.

Original languageEnglish (US)
Article number060301
JournalPhysical Review E
Issue number6
StatePublished - Jun 30 2016

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics


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