SPARSE CODING OF FACES IN A NEURONAL MODEL: INTERPRETING CELL POPULATION RESPONSE IN OBJECT RECOGNITION

Response to faces as measured by cell discharge in the temporal cortex of monkeys suggests a sparse cell population

coding of complex visual stimuli. The prevailing view assumes that a sparse population code

requires the joint contribution of a relatively small group of cells (a neuronal ensemble) for effective coding

and recognition. This assumption is based primarily on the consistent observation that single cells in the

temporal cortex are broadly tuned rather than narrowly tuned to individual faces. It has been argued that the

joint activity of a relatively small number of broadly tuned cells, each responsive to a different constituent

feature of a face, could form an ensemble code selective enough to distinguish individual faces. In the present

study, schematic faces were presented as stimuli to a model neuronal system for visual pattern learning and

recognition. This model effectively codes individual faces by means of competitive activity among single

cells during recognition instead of by ensemble coding. The computer simulation permitted an analysis of the

activity profiles of all tuned cells during learning and recognition of the faces. All cells were found to be

broadly tuned even though coding was mediated by the discrete output of single cells on a competitive basis

in a sparse neuronal population rather than by the joint activity of a group of cells. The results show that the

observation of broad tuning of cells in temporal cortex under typical experimental conditions does not

warrant the conclusion that neuronal ensembles are required for the coding of individual faces. Suggestions

are made for changes in the design of experiments to better test hypotheses about the coding of faces (or any

other complex visual patterns).