A survey of neurophysiological differentiation across mouse visual brain areas and timescales
Authors: Gandhi Saurabh R. , Mayner William G. P. , Marshall William , Billeh Yazan N. , Bennett Corbett , Gale Samuel D , Mochizuki Chris , Siegle Joshua H. , Olsen Shawn , Tononi Giulio , Koch Christof , Arkhipov Anton 
Published: January 2022

Neurophysiological differentiation (ND), a metric that quantifies the number of distinct activity states that the brain or its part visits over a period of time, has been used as a correlate of meaningfulness or subjective perception of visual stimuli. ND has largely been studied in non-invasive human whole-brain recordings where spatial resolution is limited. However, it is likely that perception is supported by discrete populations of spiking neurons rather than the whole brain. Therefore, in this study, we use Neuropixels recordings from the mouse brain to characterize the ND metric within neural populations recorded at single-cell resolution in localized regions. Using the spiking activity of thousands of simultaneously recorded neurons spanning 6 visual cortical areas as well as the visual thalamus, we show that the ND of stimulus-evoked activity of the entire visual cortex is higher for naturalistic stimuli relative to artificial ones. This finding holds in most individual areas throughout the visual hierarchy as well. For animals performing an image change detection task, ND of the entire visual cortex (though not individual areas) is higher for successful detection compared to failed trials, consistent with the assumed perception of the stimulus. Analysis of spiking activity allows us to characterize the ND metric across a wide range of timescales from 10s of milliseconds to a few seconds. This analysis reveals that although ND of activity of single neurons is often maximized at an optimal timescale around 100 ms, the optimum shifts to under 5 ms for ND of neuronal ensembles. Finally, we find that the ND of activations in convolutional neural networks (CNNs) trained on an image classification task shows distinct trends relative to the mouse visual system: ND is often higher for less naturalistic stimuli and varies by orders of magnitude across the hierarchy, compared to modest variation in the mouse brain. Together, these results suggest that ND computed on cellular-level neural recordings can be a useful tool highlighting cell populations that may be involved in subjective perception.

Advances in our understanding on neural coding has revealed that information about visual stimuli is represented across several brain regions. However, availability of information does not imply that it is necessarily utilized by the brain, much less that it is subjectively perceived. Since percepts originate in neural activity, distinct percepts must be associated with distinct ‘states’ of neural activity, at least within the brain region that supports the percepts. Thus, one approach developed in this direction is to quantify the number of distinct ‘states’ that the activity of the brain goes through, called neurophysiological differentiation (ND). ND of the entire brain has been shown to reflect subjective reports of visual stimulus meaningfulness. But what specific subpopulations within the brain could be supporting conscious perception, and what is the correct timescale on which states should be quantified? In this study, we analyze ND of spiking neural activity in the mouse visual cortex recorded using Neuropixels probes, allowing us to characterize the ND metric across a wide range of timescales all the way down from 5 ms to a few seconds. It also allows us to understand the ND of neural activity of different ensembles of neurons, from individual thalamic or cortical ensembles to those spanning across multiple visual areas in the mouse brain.