Visual image familiarity learning at multiple timescales in the primate inferotemporal cortex

Abstract

Humans and other primates can rapidly detect familiar objects and distinguish them from never-before-seen novel objects. We have an astonishing capacity to remember the details of visual scenes even after a single, fleeting experience. This ability is thought to rely in part on experience-dependent changes in the primate inferotemporal cortex (IT). Single neurons in IT encode visual familiarity by discriminating between novel and familiar stimuli, with stronger neural activity on average for novel images. However, key open questions are to understand how neural encoding in IT changes as images progress from novel to highly familiar, and what learning rules and computations can account for learning-dependent changes in IT activity. Here, we investigate the timescales over which novel stimuli become familiar by recording in IT as initially novel images become increasingly familiar both within and across days. We identified salient and persistent memory-related signals in IT that spanned multiple timescales of minutes, hours, and days. Average neural activity progressively decreased with familiarity as firing rates were strongest for novel, weaker for intermediately familiar, and weakest for highly familiar images. Neural signatures of familiarity learning were slow to develop as response reductions to initially-novel images emerged gradually over multiple days (or hundreds of views) of visual experience. In addition to slow changes that emerged across sessions, neural responses to novel images showed rapid decreases with familiarity within single sessions. To gain insight into the mechanisms underlying changes of visual responses with familiarity, we use computational modeling to investigate which plasticity rules are consistent with these changes. Learning rules inferred from the neural data revealed a strong diversity with many neurons following a negative plasticity rule as they exhibited synaptic depression over the course of learning across multiple days. A recurrent network model with two plasticity time constants - a slow time constant for long timescales and a fast time constant for short timescales - captured key dynamic features accompanying the transition from novel to familiar, including a gradual decrease in firing rates over multiple sessions, and a rapid decrease in firing rates within single sessions. Our findings suggest that distinct and complementary plasticity rules operating at different timescales may underlie the inferotemporal code for visual familiarity.