Imagine your brain as a mystical sequence-creating machine, linking memories and behaviors together like a well-choreographed dance. But how does the brain actually do this? Scientists have proposed various models, but validating them in a living brain is tricky business. However, a recent model for learning in the visual cortex offers a promising solution – using optogenetics to manipulate inhibitory interneurons. These special cells can be easily targeted in vivo with light-based genetic tools. By experimenting with these cells, researchers found that altering their activity during memory formation and recall caused distinct errors in timing. This suggests that inhibitory interneurons play a crucial role in storing and retrieving temporal information in our brains. These findings help validate the existing model and pave the way for further exploration into the intricate workings of memory formation. To dive deeper into the fascinating world of optogenetic manipulation and its impact on learning, check out the research article!

The brain uses temporal information to link discrete events into memory structures supporting recognition, prediction, and a wide variety of complex behaviors. It is still an open question how experience-dependent synaptic plasticity creates memories including temporal and ordinal information. Various models have been proposed to explain how this could work, but these are often difficult to validate in a living brain. A recent model developed to explain sequence learning in the visual cortex encodes intervals in recurrent excitatory synapses and uses a learned offset between excitation and inhibition to generate precisely timed “messenger” cells that signal the end of an instance of time. This mechanism suggests that the recall of stored temporal intervals should be particularly sensitive to the activity of inhibitory interneurons that can be easily targeted in vivo with standard optogenetic tools. In this work we examined how simulated optogenetic manipulations of inhibitory cells modifies temporal learning and recall based on these mechanisms. We show that disinhibition and excess inhibition during learning or testing cause characteristic errors in recalled timing that could be used to validate the model in vivo using either physiological or behavioral measurements.

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