Neural correlates of sparse coding and dimensionality reduction
Fig 1
NSC promotes population codes that are both sparse and parts based.
(A) Hypothetical activity in a population of neurons during presentation of two different external stimuli (“contexts”). A sparse code is a trade-off between a local code (in which a context is represented by the activity of a single neuron, and different contexts are represented by different neurons) and a dense code (in which all neurons are active, and their combined activity is used to encode each context). Dense codes possess great memory capacity but suffer from cross talk among neurons, whereas local codes do not suffer from interference but also have no capacity for generalization (inspired by [8]). (B) In a holistic representation of faces, individual neurons in the population respond themselves to faces as a whole [11], whereas in a parts-based representation, individual neurons explicitly encode individual face components [12], such as the eyes, nose, and mouth (inspired by [13]). NSC, nonnegative sparse coding.