Steady or changing? Long-term monitoring of neuronal population activity.

By Henry Lütcke, David J. Margolis, Fritjof Helmchen. 2013

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Natural continual learning: success is a journey, not (just) a destination (2021, background): “The stability of such single-neuron dynamics after learning has recently been a topic of much interest in biological circuits (Clopath et al., 2017; Lütcke et al., 2013; Rule et al., 2019).”
Automated long-term recording and analysis of neural activity in behaving animals (2016, methodology): “Such longitudinal recordings would allow us to follow the activity of single neurons over more trials, experimental conditions, and behavioral states, thus increasing the power with which inferences about neural function can be made (Lütcke et al., 2013).”
Mechanisms for Stable, Robust, and Adaptive Development of Orientation Maps in the Primary Visual Cortex (2013, methodology): “Using genetically encoded calcium indicators, it has also become possible to use calcium imaging for chronic recordings (Lütcke et al., 2013).”
Variance and invariance of neuronal long-term representations (2017, unknown): “In order to distinguish between these alternatives, the activity of the same individual cells and cell populations has to be followed over multiple time points (Figure 1) [16].”