title: The sparseness of mixed selectivity neurons controls the generalization-discrimination trade-off.
abstract: Intelligent behavior requires integrating several sources of information in a meaningful fashion-be it context with stimulus or shape with color and size. This requires the underlying neural mechanism to respond in a different manner to similar inputs (discrimination), while maintaining a consistent response for noisy variations of the same input (generalization). We show that neurons that mix information sources via random connectivity can form an easy to read representation of input combinations. Using analytical and numerical tools, we show that the coding level or sparseness of these neurons' activity controls a trade-off between generalization and discrimination, with the optimal level depending on the task at hand. In all realistic situations that we analyzed, the optimal fraction of inputs to which a neuron responds is close to 0.1. Finally, we predict a relation between a measurable property of the neural representation and task performance.
authors: Center for Theoretical Neuroscience, Department of Neuroscience, Columbia University Medical Center, New York, New York 10032, USA.
authors: Omri
authors: O
authors: Barak
authors: Mattia
authors: M
authors: Rigotti
authors: Stefano
authors: S
authors: Fusi
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D003118:Color Perception; D004192:Discrimination, Psychological / Q000502:physiology*; D005793:Generalization, Psychological / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009474:Neurons / Q000145:classification / Q000502:physiology*; D010775:Photic Stimulation; D012858:Size Perception