REPRESENTATIONAL CAPACITY OF FACE CODING IN MONKEYS

We examine the distributed nature of the neural code for faces represe nted by the firing of visual neurons in the superior temporal sulcus o f monkeys. Both information theory and neural decoding techniques are applied to determine how the capacity to represent faces depends on th e number of coding neurons. Using a combination of experimental data a nd Monte Carte simulations, we show that the information grows linearl y and the capacity to encode stimuli grows exponentially with the numb er of neurons. By decoding firing rates, we determine that the respons es of the 14 recorded neurons can distinguish between 20 face stimuli with approximately 80% accuracy. In general, we find that IV neurons o f this type can encode approximately 3(2(0.4N)) different faces with 5 0% discrimination accuracy. These results indicate that the neural cod e for faces is highly distributed and capable of accurately representi ng large numbers of stimul.