Face familiarity detection with complex synapses.

iScience

Zuckerman Institute, Columbia University, New York, NY 10027, USA.

Published: January 2023

AI Article Synopsis

  • Synaptic plasticity incorporates various biochemical processes that enhance memory capacity, especially when dealing with imprecise variables.
  • The study demonstrates that complex synapses allow for the storage and recognition of many faces, showing a linear relationship with synapse count and a quadratic one with neuron count.
  • Complex synapses outperform simpler ones in memory tasks, suggesting their potential for practical applications like detecting face familiarity.

Article Abstract

Synaptic plasticity is a complex phenomenon involving multiple biochemical processes that operate on different timescales. Complexity can greatly increase memory capacity when the variables characterizing the synaptic dynamics have limited precision, as shown in simple memory retrieval problems involving random patterns. Here we turn to a real-world problem, face familiarity detection, 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9829748PMC
http://dx.doi.org/10.1016/j.isci.2022.105856DOI Listing

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