The first preprint from the lab!

Using in vivo intracellular recordings, closed-loop manipulations of neuronal activity, and analysis of published two-photon imaging data in layer 2/3 of V1, we find causal roles for layer 2/3 spiking and neuromodulatory input from the locus coeruleus in driving a reorganization of visuomotor integration to enhance predictive cancellation.

I discuss how recent findings indicate that the locus coeruleus (LC) responds to various prediction errors, supporting models where the LC signals major failures in prediction. I illustrate how this function is useful in adjusting the learning rate of cortical predictions, helping to balance the stability and flexibility of internal models. Additionally, I show how this perspective aligns with other established functions of the LC and how LC circuitry can in principle compute such signals.

The neuromodulatory locus coeruleus (LC) responds to unexpected stimuli, potentially reflecting prediction errors. Using two-photon calcium imaging in the cortex of mice running in virtual reality, we discovered that the LC signals unsigned visuomotor prediction errors in the cortex. Optogenetic stimulation revealed that transient LC activity can gate cortical sensorimotor plasticity within minutes, mimicking the plasticity usually observed over days of visuomotor experience.

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We review the evidence that visuomotor mismatch responses in layer 2/3 of V1 arise from prediction error computation. We demonstrate that the locomotion-induced gain increase in V1 visual responses does not explain mismatch responses, as mismatch response size heavily depends on recent visuomotor coupling experience. The results support the existence of a dynamic visuomotor prediction error computation in V1 based on visual motion predictability.

Whole cell intracellular recording in vivo is especially challenging in moving animals, in part due to the scarcity of detailed protocols. Rebecca created this protocol to provide essential practical information for anyone with a whole cell recording rig to successfully master the method and obtain these valuable recordings from head fixed animals engaged in locomotion or tasks.

Prediction error computation involves a neuron computing the difference between predicted and actual sensory input. This study uses whole recordings in the primary visual cortex of mice running in a virtual reality system, revealing that layer 2/3 neurons exhibit membrane potential dynamics indicative of comparing visual flow with predictions based on locomotion speed, while deeper layers do not show this computation.