Sweeney, Y. | Search bioRxiv

bioRxiv2018

Diverse homeostatic responses to visual deprivation by uncovering recurrent subnetworks

Multiple homeostatic plasticity mechanisms are thought to be critical for the prevention of excessively high or aberrantly low neural activity in the adult cortex. In L2/3 of adult mouse visual cortex the interplay between disinhibition and local functional interactions may support homeostatic recovery following visual deprivation. Despite blanket disinhibition only a subset of L2/3 excitatory neurons are observed to exhibit homeostatic recovery. Recovering neurons tend to be correlated with each other, forming functional networks prior to deprivation. How homeostatic recovery occurs in this way is therefore unclear, particularly in conditions of global disinhibition.\n\nHere, we employ a computational modelling approach to investigate the origin of diverse homeostatic responses in the cortex. This model finds network size to be a critical determinant of the diverse homeostatic activity profiles observed following visual deprivation, as neurons which belong to larger networks exhibit a stronger homeostatic response. Our simulations provide mechanistic insights into the emergence of diverse homeostatic responses, and predict that neurons with a high proportion of enduring functional associations will exhibit the strongest homeostatic recovery. We test and confirm these predictions experimentally.

neuroscience↗

bioRxiv2018

Population coupling predicts the plasticity of stimulus responses in cortical circuits

Long-term imaging of sensory cortex reveals a diverse range of stimulus response stability: some neurons retain stimulus responses that are stable over days whereas other neurons have highly plastic stimulus responses. Using a recurrent network model, we explore whether this observation could be due to an underlying diversity in the synaptic plasticity of neurons. We find that, in a network with diverse learning rates, neurons with fast rates are more coupled to population activity than neurons with slow rates. This phenomenon, which we call a plasticity-coupling link, surprisingly predicts that neurons with high population coupling exhibit more long-term stimulus response variability than neurons with low population coupling. We substantiate this prediction using recordings from the Allen Brain Observatory which track the orientation preferences of 15,000 neurons in mouse visual cortex. In agreement with our model, a neurons population coupling is correlated with the plasticity of its orientation preference. Finally, we show that high population coupling helps plastic neurons alter their stimulus preference during a simple perceptual learning task, but hinders the ability of stable neurons to provide an instructive signal for learning. This suggests a particular functional architecture: a stable backbone of stimulus representation formed by neurons with slow synaptic plasticity and low population coupling, on top of which lies a flexible substrate of neurons with fast synaptic plasticity and high population coupling.

neuroscience↗