Face familiarity detection with complex synapses

Synaptic plasticity is a complex phenomenon involving multiple biochemical processes that operate on different timescales. Complexity can greatly increase limited precision, as shown in simple memory retrieval problems involving tion, and we show that synaptic complexity can be harnessed to store in memory a large number of faces that can be recognized at a later time. The number of recognizable faces grows almost linearly with the number of synapses and quadratically with the number of neurons. Complex synapses outperform simple ones characterized by a single variable, even when the total number of dynamical variables is matched. Complex and simple synapses have distinct signatures that are testable in experiments. Our results indicate that a system with complex synapses can be used in real-world tasks such as face familiarity detection.

Automated summary

Synaptic plasticity is a complex phenomenon involving multiple biochemical processes that operate on different timescales. Complexity can greatly increase limited precision, as shown in simple memory retrieval problems. Our study demonstrates that complex synapses can be utilized to store and recognize a large number of faces. The number of recognizable faces increases linearly with the number of synapses and quadratically with the number of neurons. Complex synapses outperform simple ones, even when the total number of dynamical variables is matched. These findings have potential implications for real-world tasks such as face familiarity detection.