Neural mechanisms of state-dependent sensory processing - Van Der Plas, TL (2023)
A key purpose of the central nervous system is to transform sensation into action. During this process, identical stimuli can cause variable behavioural responses because of changes in the condition -- or, state -- of the brain. This response variability is a common phenomenon, yet the underlying neural mechanisms of how sensory information differentially interacts with varying network states remain obscure. In this thesis, we consider three such mechanisms in depth: inhibition stabilisation, neural variability and predictive learning. First, we analyse all-optical experiments where we were able to simultaneously record and insert neural activity in somatosensory cortex in vivo. Our results show that population activity is stabilised by inhibition, thereby allowing faint sensory stimulation to be detected and propagated downstream. In mice trained to detect these stimuli, we further observe that the neural variability across neurons modulated their perceptual performance, whereas this effect did not occur during passive stimulation. Therefore, maximising the cortical signal-to-noise ratio of behaviourally relevant stimuli is critical to facilitate activity propagation and perception. Separately, in a study of artificial neural networks, we demonstrate that it is advantageous for networks to continuously predict upcoming sensory information, as this facilitates learning of more complex working memory tasks. This could explain recent experimental findings showing that cortical neurons engage in predictive learning even when reward is absent. In sum, this thesis explores how sensory information interacts with ongoing neural activity in both biological and artificial neural networks, and explains mechanisms that underpin the variability of our behavioural response.