title: Linear readout of object manifolds.
abstract: 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.
authors.affiliation: Program in Applied Physics, School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA.|Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA.
authors.forename: SueYeon
authors.initials: S
authors.lastname: Chung
authors.affiliation: Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
authors.forename: Daniel D
authors.initials: DD
authors.lastname: Lee
authors.affiliation: Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138, USA.|Racah Institute of Physics, Hebrew University, Jerusalem 91904, Israel.|Edmond and Lily Safra Center for Brain Sciences, Hebrew University, Jerusalem 91904, Israel.
authors.forename: Haim
authors.initials: H
authors.lastname: Sompolinsky
mesh_terms: D008959:Models, Neurological; D016571:Neural Networks, Computer