Representational capacity of face coding in monkeys

Abstract

We examine the distributed nature of the neural code for faces represented by the firing of visual neurons in the superior temporal sulcus of monkeys. Bath information theory and neural decoding techniques are applied to determine how the capacity to represent faces depends on the number of coding neurons. Using a combination of experimental data and Monte Carlo simulations, we show that the information grows linearly and the capacity to encode stimuli grows exponentially with the number of neurons. By decoding firing rates, we determine that the responses of the 14 recorded neurons can distinguish between 20 face stimuli with approximately 80% accuracy. In general, we find that N neurons of this type can encode approximately 3(2(0.4N)) different faces with 56% discrimination accuracy. These results indicate that the neural code for faces is highly distributed and capable of accurately representing large numbers of stimuli.