Viktoria Zemliak

The paper shows spike synchrony reliably detects stimulus familiarity while firing rate detects stimulus class, demonstrating complementary coding mechanisms in neural computation.

This paper introduces a spiking neural network model that decodes familiarity from temporal and frequency features of spike trains, outperforming LSTM in continual familiarity detection.

The paper demonstrates that spike synchrony in V1 reflects the Gestalt structure of stimuli, suggesting it serves as a mechanism for estimating prior probability of incoming visual input.