The encoding of complex visual stimuli by a canonical model of the primary visual cortex: Temporal population code for face recognition on the iCub robot

The connectivity of the cerebral cortex is characterized by dense local and sparse long-range connectivity. It has been proposed that this connection topology provides a rapid and robust transformation of spatial stimulus information into a temporal population code (TPC). TPC is a canonical model of cortical computation whose topological requirements are independent of the properties of the input stimuli and, therefore, can be generalized to the processing requirements of all cortical areas. Here we propose a real time implementation of TPC for classifying faces, a complex natural stimuli that mammals are constantly confronted with. The model consists of a network comprising a primary visual cortex V1 network of laterally connected integrate-and-fire neurons implemented in the humanoid robot platform iCub. The experiment was performed using human faces presented to the robot under different angles and position of light incidence. We show that the TPC-based model can recognize faces with a correct ratio of 97% without any face-specific strategy. Additionally, the speed of encoding is coherent with the mammalian visual system suggesting that the representation of natural static visual stimulus is generated based on the combined temporal dynamics of multiple neuron populations. Our results provides that, without any input dependent wiring, TPC can be efficiently used for encoding local features in a high complexity task such as face recognition.