Topic: self-averaging

A vast variety of sensory cues are represented as distinct patterns of neural responses in the brain. To retrieve memories associated with sensory cues, one would assume that the representations of sensory signals should be stable over time. However, recent development of population-level recording techniques has revealed that these representations can drift over time $^{1}$. To investigate the mechanisms and consequences of representational drift, we formulate a random feedforward network model $^{2,3}$ that incorporates stochastic fluctuations in membrane potential and synaptic strength $^{4