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What are the spatial and temporal scales of brainwide neuronal activity, and how do activities at different scales interact? We used SCAPE microscopy to image a large fraction of the central brain of adult Drosophila melanogaster with high spatiotemporal resolution while flies engaged in a variety of behaviors, including running, grooming and flailing. This revealed neural representations of behavior on multiple spatial and temporal scales. The activity of most neurons across the brain correlated (or, in some cases, anticorrelated) with running and flailing over timescales that ranged from seconds to almost a minute. Grooming elicited a much weaker global response. Although these behaviors accounted for a large fraction of neural activity, residual activity not directly correlated with behavior was high dimensional. Many dimensions of the residual activity reflect the activity of small clusters of spatially organized neurons that may correspond to genetically defined cell types. These clusters participate in the global dynamics, indicating that neural activity reflects a combination of local and broadly distributed components. This suggests that microcircuits with highly specified functions are provided with knowledge of the larger context in which they operate, conferring a useful balance of specificity and flexibility.

A popular approach to quantifying animal behavior from video data is through discrete behavioral segmentation, wherein video frames are labeled as containing one or more behavior classes such as walking or grooming. Sequence models learn to map behavioral features extracted from video frames to discrete behaviors, and both supervised and unsupervised methods are common. However, each approach has its drawbacks: supervised models require a time-consuming annotation step where humans must hand label the desired behaviors; unsupervised models may fail to accurately segment particular behaviors of interest. We introduce a semi-supervised approach that addresses these challenges by constructing a sequence model loss function with (1) a standard supervised loss that classifies a sparse set of hand labels; (2) a weakly supervised loss that classifies a set of easy-to-compute heuristic labels; and (3) a self-supervised loss that predicts the evolution of the behavioral features. With this approach, we show that a large number of unlabeled frames can improve supervised segmentation in the regime of sparse hand labels and also show that a small number of hand labeled frames can increase the precision of unsupervised segmentation.

SCAPE microscopy permits high-speed 3D imaging of neuronal activity throughout the brain of behaving, adult Drosophila, as well as activity in the entire nervous system of freely crawling Drosophila larvae. Latest results will be presented.

Neurons in piriform cortex receive input from a random collection of glomeruli, resulting in odor representations that lack the stereotypic organization of the olfactory bulb. We have performed in vivo optical imaging and mathematical modeling to demonstrate that correlations are retained in the transformation from bulb to piriform cortex, a feature essential for generalization across odors. Random connectivity also implies that the piriform representation of a given odor will differ among different individuals and across brain hemispheres in a single individual. We show that these different representations can nevertheless support consistent agreement about odor quality across a range of odors. Our model also demonstrates that, whereas odor discrimination and categorization require far fewer neurons than reside in piriform cortex, consistent generalization may require the full complement of piriform neurons.

SummaryIn what regime does the cortical circuit operate? Our intracellular studies of surround suppression in cat primary visual cortex (V1) provide strong evidence on this question. Although suppression has been thought to arise from an increase in lateral inhibition, we find that the inhibition that cells receive is reduced, not increased, by a surround stimulus. Instead, suppression is mediated by a withdrawal of excitation. Thalamic recordings and previous work show that these effects cannot be explained by a withdrawal of thalamic input. We find in theoretical work that this behavior can only arise if V1 operates as an inhibition-stabilized network (ISN), in which excitatory recurrence alone is strong enough to destabilize visual responses but feedback inhibition maintains stability. We confirm two strong tests of this scenario experimentally and show through simulation that observed cell-to-cell variability in surround effects, from facilitation to suppression, can arise naturally from variability in the ISN.

Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons., Author Summary Neuronal responses are often characterized by the rate at which action potentials are generated rather than by the timing of individual spikes. Firing-rate descriptions of neural activity are appealing because of their comparative simplicity, but it is important to develop models that faithfully approximate dynamic features arising from spiking. In particular, synchronization or partial synchronization of spikes is an important feature that cannot be described by typical firing-rate models. Here we develop a model that is nearly as simple as the simplest firing-rate models and yet can account for a number of aspects of spiking dynamics, including partial synchrony. The model matches the dynamic activity of networks of spiking neurons with surprising accuracy. By expanding the range of dynamic phenomena that can be described by simple firing-rate equations, this model should be useful in guiding intuition about and understanding of neural circuit function.

VR lends itself to the study of intersensory calibration in self-motion perception. However, proper calibration of visual and locomotor self-motion in VR is made complicated by the compression of perceived distance and by unfamiliar modes of locomotion. Although adaptation is fairly rapid with exposure to novel sensorimotor correlations, here it is shown that good initial calibration is found when both (1) the virtual environment is richly structured in near space and (2) locomotion is on solid ground. Previously it had been observed that correct visual speeds seem too slow when walking on a treadmill. Several principles may be involved, including inhibitory sensory prediction, distance compression, and missing peripheral flow in the reduced FOV. However, though a richly-structured near-space environment provides higher rates of peripheral flow, its presence does not improve calibration when walking on a treadmill. Conversely, walking on solid ground still shows relatively poor calibration in an empty (though well-textured) virtual hallway. Because walking on solid ground incorporates well-calibrated mechanisms that can assess speed of self-motion independent of vision, these observations suggest that near space may have been better calibrated in the HMD. Near-space obstacle avoidance systems may also be involved. Order effects in the data from the treadmill experiment indicate that recalibration of self-motion perception occurred during the experiment.