Valence and State-Dependent Population Coding in Dopaminergic Neurons in the Fly Mushroom Body

Neuromodulation permits flexibility of synapses, neural circuits, and ultimately behavior. One neuromodulator, dopamine, has been studied extensively in its role as a reward signal during learning and memory across animal species. Newer evidence suggests that dopaminergic neurons (DANs) can modulate sensory perception acutely, thereby allowing an animal to adapt its behavior and decision making to its internal and behavioral state. In addition, some data indicate that DANs are not homogeneous but rather convey different types of information as a heterogeneous population. We have investigated DAN population activity and how it could encode relevant information about sensory stimuli and state by taking advantage of the confined anatomy of DANs innervating the mushroom body (MB) of the fly Drosophila melanogaster. Using in vivo calcium imaging and a custom 3D image registration method, we found that the activity of the population of MB DANs encodes innate valence information of an odor or taste as well as the physiological state of the animal. Furthermore, DAN population activity is strongly correlated with movement, consistent with a role of dopamine in conveying behavioral state to the MB. Altogether, our data and analysis suggest that DAN population activities encode innate odor and taste valence, movement, and physiological state in a MB-compartment-specific manner. We propose that dopamine shapes innate perception through combinatorial population coding of sensory valence, physiological, and behavioral context

Evidence accumulation during a sensorimotor decision task revealed by whole-brain imaging

Although animals can accumulate sensory evidence over considerable time scales to appropriately select behavior, little is known about how the vertebrate brain as a whole accomplishes this. In this study, we developed a new sensorimotor decision-making assay in larval zebrafish based on whole-field visual motion. Fish responded by swimming in the direction of perceived motion, such that the latency to initiate swimming and the fraction of correct turns were modulated by motion strength. Using whole-brain functional imaging, we identified neural activity relevant to different stages of the decision-making process, including the momentary evaluation and accumulation of sensory evidence. This activity is distributed in functional clusters across different brain regions and is characterized by a wide range of time constants. In addition, we found that the caudal interpeduncular nucleus (IPN), a circular structure located ventrally on the midline of the brain, reliably encodes the left and right turning rates. Dragomir et al. use a new decision-making assay in larval zebrafish to show that fish modulate their behavior depending on stimulus strength. A whole-brain imaging functional screen reveals neurons that integrate sensory evidence over the course of seconds

Sensorimotor representations in cerebellar granule cells in larval zebrafish are dense, spatially organized, and non-temporally patterned

A fundamental question in neurobiology is how animals integrate external sensory information from their environment with self-generated motor and sensory signals in order to guide motor behavior and adaptation. The cerebellum is a vertebrate hind-brain region where all of these signals converge and that has been implicated in the acquisition, coordination, and calibration of motor activity. Theories of cerebellar function postulate that granule cells encode a variety of sensorimotor signals in the cerebellar input layer. These models suggest that representations should be high-dimensional, sparse, and temporally patterned. However, in vivo physiological recordings addressing these points have been limited and in particular have been unable to measure the spatiotemporal dynamics of population-wide activity. In this study, we use both calcium imaging and electrophysiology in the awake larval zebrafish to investigate how cerebellar granule cells encode three types of sensory stimuli as well as stimulus-evoked motor behaviors. We find that a large fraction of all granule cells are active in response to these stimuli, such that representations are not sparse at the population level. We find instead that most responses belong to only one of a small number of distinct activity profiles, which are temporally homogeneous and anatomically clustered. We furthermore identify granule cells that are active during swimming behaviors and others that are multimodal for sensory and motor variables. When we pharmacologically change the threshold of a stimulus-evoked behavior, we observe correlated changes in these representations. Finally, electrophysiological data show no evidence for temporal patterning in the coding of different stimulus durations

Do neural networks for segmentation understand insideness?

The insideness problem is an aspect of image segmentation that consists of determining which pixels are inside and outside a region. Deep neural networks (DNNs) excel in segmentation benchmarks, but it is unclear if they have the ability to solve the insideness problem as it requires evaluating long-range spatial dependencies. In this letter, we analyze the insideness problem in isolation, without texture or semantic cues, such that other aspects of segmentation do not interfere in the analysis. We demonstrate that DNNs for segmentation with few units have sufficient complexity to solve the insideness for any curve. Yet such DNNs have severe problems with learning general solutions. Only recurrent networks trained with small images learn solutions that generalize well to almost any curve. Recurrent networks can decompose the evaluation of long-range dependencies into a sequence of local operations, and learning with small images alleviates the common difficulties of training recurrent networks with a large number of unrolling steps