Spontaneous behaviors drive multidimensional, brainwide activity

Neuron activity across the brain How is it that groups of neurons dispersed through the brain interact to generate complex behaviors? Three papers in this issue present brain-scale studies of neuronal activity and dynamics (see the Perspective by Huk and Hart). Allen et al. found that in thirsty mice, there is widespread neural activity related to stimuli that elicit licking and drinking. Individual neurons encoded task-specific responses, but every brain area contained neurons with different types of response. Optogenetic stimulation of thirst-sensing neurons in one area of the brain reinstated drinking and neuronal activity across the brain that previously signaled thirst. Gründemann et al. investigated the activity of mouse basal amygdala neurons in relation to behavior during different tasks. Two ensembles of neurons showed orthogonal activity during exploratory and nonexploratory behaviors, possibly reflecting different levels of anxiety experienced in these areas. Stringer et al. analyzed spontaneous neuronal firing, finding that neurons in the primary visual cortex encoded both visual information and motor activity related to facial movements. The variability of neuronal responses to visual stimuli in the primary visual area is mainly related to arousal and reflects the encoding of latent behavioral states. Science, this issue p. eaav3932, p. eaav8736, p. eaav7893; see also p. 236 Neurons in the primary visual cortex encode both visual information and motor activity. INTRODUCTION In the absence of sensory inputs, the brain produces structured patterns of activity, which can be as large as or larger than sensory-driven activity. Ongoing activity exists even in primary sensory cortices and has been hypothesized to reflect recapitulation of previous sensory experiences, or expectations of possible sensory events. Alternatively, ongoing activity could be related to behavioral and cognitive states. RATIONALE Much previous work has linked spontaneous neural activity to behavior through one-dimensional measures like running speed and pupil diameter. However, mice perform diverse behaviors consisting of whisking, licking, sniffing, and other facial movements. We hypothesized that there exists a multidimensional representation of behavior in visual cortex and that previously reported “noise” during stimulus presentations may in fact be behaviorally driven. To investigate this, we recorded the activity of ~10,000 neurons in visual cortex of awake mice using two-photon calcium imaging, while simultaneously monitoring the facial movements using an infrared camera. In a second set of experiments, we recorded the activity of thousands of neurons across the brain using eight simultaneous Neuropixels probes, again videographically monitoring facial behavior. RESULTS First, we found that ongoing activity in visual cortex is high dimensional: More than a hundred latent dimensions could be reliably extracted from the population activity. We found that a third of this activity could be predicted by a multidimensional model of the mouse’s behavior, extracted from the face video. This behaviorally related activity was not limited to visual cortex. We observed multidimensional representations of behavior in electrophysiological recordings from frontal, sensorimotor, and retrosplenial cortex; hippocampus; striatum; thalamus; and midbrain. Even though both behavior and neural activity contained fast–time scale fluctuations on the order of 200 ms, they were only related to each other at a time scale of about 1 s. We next investigated how this spontaneous, behavior-related signal interacts with stimulus responses. The representation of sensory stimuli and behavioral variables was mixed in the same neurons: The fractions of each neuron’s variance explained by stimuli and by behavior were only slightly negatively correlated, and neurons with similar stimulus responses did not have more similar behavioral correlates. Nevertheless, at a population level, the neural dimensions encoding motor variables overlapped with those encoding visual stimuli along only one dimension, which coherently increased or decreased the activity of the entire population. Activity in all other behaviorally driven dimensions continued unperturbed regardless of sensory stimulation. CONCLUSION The brainwide representation of behavioral variables suggests that information encoded nearly anywhere in the forebrain is combined with behavioral state variables into a mixed representation. We found that these multidimensional signals are present both during ongoing activity and during passive viewing of a stimulus. This suggests that previously reported noise during stimulus presentations may consist primarily of behavioral-state information. What benefit could this ubiquitous mixing of sensory and motor information provide? The most appropriate behavior for an animal to perform at any moment depends on the combination of available sensory data, ongoing motor actions, and purely internal variables such as motivational drives. Integration of sensory inputs with motor actions must therefore occur somewhere in the nervous system. Our data indicate that it happens as early as primary sensory cortex. Large-scale neural population recordings can be predicted from behavior. We used new recording technologies to simultaneously monitor the activity of ~10,000 neurons in a single brain area and ~3000 neurons from across the brain (top left). These neurons showed slow–time scale patterns of coactivation restricted to subsets of neurons which were distributed across the brain (top right). The patterns of neural activity appeared to be driven by specific spontaneous behaviors that the animals engaged in during the experiment. We tracked these spontaneous behaviors by projecting a video recording of the mouse face onto a set of canonical “eigenfaces” (bottom left) and used these projections to predict a large fraction of the neural activity (bottom right). t, time; PC, principal component. Neuronal populations in sensory cortex produce variable responses to sensory stimuli and exhibit intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Recording more than 10,000 neurons in mouse visual cortex, we observed that spontaneous activity reliably encoded a high-dimensional latent state, which was partially related to the mouse’s ongoing behavior and was represented not just in visual cortex but also across the forebrain. Sensory inputs did not interrupt this ongoing signal but added onto it a representation of external stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.