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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.

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.