Khuvis, S. | Search bioRxiv

bioRxiv2018

Face-selective units in human ventral temporal cortex reactivate during free recall

Properties of face-responsive individual neurons in the human ventral temporal cortex (VTC) have yet to be studied, and their role in conscious perception remains unknown. To explore this, we implanted microelectrodes into the VTCs of eight human subjects undergoing invasive epilepsy monitoring. Most (26 of 33) category-selective units showed specificity for face stimuli, with a range of response profiles. Different face exemplars evoked consistent and discriminable responses in the population of units sampled. During a free recall task, face-selective units selectively reactivated in the absence of visual stimulation during the 2-second window prior to face recall events. Furthermore, the identity of the recalled face could be predicted by comparing activity preceding recall events to activity evoked by visual stimulation.

neuroscience↗

bioRxiv2018

Deep Convolutional modeling of human face selective columns reveals their role in pictorial face representation

Despite the massive accumulation of systems neuroscience findings, their functional meaning remains tentative, largely due to the absence of realistically performing models. The discovery that deep convolutional networks achieve human performance in realistic tasks offers fresh opportunities for such modeling. Here we show that the face-space topography of face-selective columns recorded intra-cranially in 32 patients significantly matches that of a DCNN having human-level face recognition capabilities. Three modeling aspects converge in pointing to a role of human face areas in pictorial rather than person identification: First, the match was confined to intermediate layers of the DCNN. Second, identity preserving image manipulations abolished the brain to DCNN correlation. Third, DCNN neurons matching face-column tuning displayed view-point selective receptive fields. Our results point to a \"convergent evolution\" of pattern similarities in biological and artificial face perception. They demonstrate DCNNs as a powerful modeling approach for deciphering the function of human cortical networks.

neuroscience↗