Abstract
Some neurons have stimulus responses that are stable over days, whereas other neurons have highly plastic stimulus responses. Using a recurrent network model, we explore whether this could be due to an underlying diversity in their synaptic plasticity. 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 plasticity-coupling link 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, finding that a neuron's population coupling is correlated with the plasticity of its orientation preference. Simulations of a simple perceptual learning task suggest a particular functional architecture: a stable 'backbone' of stimulus representation formed by neurons with low population coupling, on top of which lies a flexible substrate of neurons with high population coupling.
Highlights
The brain encodes information about the external world via its neural activity
Diverse population coupling emerges in cortical networks with diverse learning rates As the plasticity-coupling link is robustly observed in a fully-connected small network with simple stimulus responses, we investigate i) whether the plasticity-coupling link is present in larger networks which more accurately represent the synaptic connectivity and stimulus response properties observed in mouse visual cortex, and ii) whether the diverse population coupling observed in sensory cortex emerges by introducing diverse learning rates (Okun et al, 2015)
We have studied the impact of diverse learning rates in a recurrent network model of visual cortex
Summary
The brain encodes information about the external world via its neural activity. One aspect of such encoding is that neurons in sensory cortex often have a preferred stimulus which evokes a stronger response than other stimuli. Advances in neural imaging techniques allow us to interrogate such changes by tracking stimulus responses of hundreds of neurons over many days in vivo (Andermann, 2010; Mank et al, 2008) These recordings reveal a substantial, and puzzling, variability in the long-term stability of responses in sensory cortex: some neurons retain highly stable preferences to specific stimuli, whereas the stimulus preference of other neurons change from day to day (Ranson, 2017; Clopath et al, 2017; Poort et al, 2015; Lutcke et al, 2013; Rule et al, 2019; Rose et al, 2016). It is possible to induce stimulus response plasticity through perturbations such as sensory deprivation (Rose et al, 2016), or to increase task-related stimulus response stability through rewarded learning (Poort et al, 2015)
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