A key feature of reactive behaviors is the ability to spatially localize a salient stimulus and act accordingly. Such sensory-motor transformations must be particularly fast and well tuned in escape behaviors, in which both the speed and accuracy of the evasive response determine whether an animal successfully avoids predation [1]. We studied the escape behavior of the fruit fly, Drosophila, and found that flies can use visual information to plan a jump directly away from a looming threat. This is surprising, given the architecture of the pathway thought to mediate escape [2, 3]. Using high-speed videography, we found that approximately 200 ms before takeoff, flies begin a series of postural adjustments that determine the direction of their escape. These movements position their center of mass so that leg extension will push them away from the expanding visual stimulus. These preflight movements are not the result of a simple feed-forward motor program because their magnitude and direction depend on the flies’ initial postural state. Furthermore, flies plan a takeoff direction even in instances when they choose not to jump. This sophisticated motor program is evidence for a form of rapid, visually mediated motor planning in a genetically accessible model organism.

For sensory signals to control an animal’s behavior, they must first be transformed into a format appropriate for use by its motor systems. This fundamental problem is faced by all animals, including humans. Beyond simple reflexes, little is known about how such sensorimotor transformations take place. Here we describe how the outputs of a well-characterized population of fly visual interneurons, lobula plate tangential cells (LPTCs), are used by the animal’s gaze-stabilizing neck motor system. The LPTCs respond to visual input arising from both self-rotations and translations of the fly. The neck motor system however is involved in gaze stabilization and thus mainly controls compensatory head rotations. We investigated how the neck motor system is able to selectively extract rotation information from the mixed responses of the LPTCs. We recorded extracellularly from fly neck motor neurons (NMNs) and mapped the directional preferences across their extended visual receptive fields. Our results suggest that-like the tangential cells-NMNs are tuned to panoramic retinal image shifts, or optic flow fields, which occur when the fly rotates about particular body axes. In many cases, tangential cells and motor neurons appear to be tuned to similar axes of rotation, resulting in a correlation between the coordinate systems the two neural populations employ. However, in contrast to the primarily monocular receptive fields of the tangential cells, most NMNs are sensitive to visual motion presented to either eye. This results in the NMNs being more selective for rotation than the LPTCs. Thus, the neck motor system increases its rotation selectivity by a comparatively simple mechanism: the integration of binocular visual motion information.

The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy's performance in spike extraction and scalability are state-of-the-art.

Neurons integrate synaptic inputs within their dendrites and produce spiking outputs, which then propagate down the axon and back into the dendrites where they contribute to plasticity. Mapping the voltage dynamics in dendritic arbors of live animals is crucial for understanding neuronal computation and plasticity rules. Here we combine patterned channelrhodopsin activation with dual-plane structured illumination voltage imaging, for simultaneous perturbation and monitoring of dendritic and somatic voltage in Layer 2/3 pyramidal neurons in anesthetized and awake mice. We examined the integration of synaptic inputs and compared the dynamics of optogenetically evoked, spontaneous, and sensory-evoked back-propagating action potentials (bAPs). Our measurements revealed a broadly shared membrane voltage throughout the dendritic arbor, and few signatures of electrical compartmentalization among synaptic inputs. However, we observed spike rate acceleration-dependent propagation of bAPs into distal dendrites. We propose that this dendritic filtering of bAPs may play a critical role in activity-dependent plasticity.

Motor systems must continuously adapt their output to maintain a desired trajectory. While the spinal circuits underlying rhythmic locomotion are well described, little is known about how the network modulates its output strength. A major challenge has been the difficulty of recording from spinal neurons during behavior. Here, we use voltage imaging to map the membrane potential of large populations of glutamatergic neurons throughout the spinal cord of the larval zebrafish during fictive swimming in a virtual environment. We characterized a previously undescribed subpopulation of tonic-spiking ventral V3 neurons whose spike rate correlated with swimming strength and bout length. Optogenetic activation of V3 neurons led to stronger swimming and longer bouts but did not affect tail beat frequency. Genetic ablation of V3 neurons led to reduced locomotor adaptation. The power of voltage imaging allowed us to identify V3 neurons as a critical driver of locomotor adaptation in zebrafish.

Motor systems must continuously adapt their output to maintain a desired trajectory. While the spinal circuits underlying rhythmic locomotion are well described, little is known about how the network modulates its output strength. A major challenge has been the difficulty of recording from spinal neurons during behavior. Here, we use voltage imaging to map the membrane potential of large populations of glutamatergic neurons throughout the spinal cord of the larval zebrafish during fictive swimming in a virtual environment. We characterized a previously undescribed subpopulation of tonic-spiking ventral V3 neurons whose spike rate correlated with swimming strength and bout length. Optogenetic activation of V3 neurons led to stronger swimming and longer bouts but did not affect tail beat frequency. Genetic ablation of V3 neurons led to reduced locomotor adaptation. The power of voltage imaging allowed us to identify V3 neurons as a critical driver of locomotor adaptation in zebrafish.

As animals adapt to new situations, neuromodulation is a potent way to alter behavior, yet mechanisms by which neuromodulatory nuclei compute during behavior are underexplored. The serotonergic raphe supports motor learning in larval zebrafish by visually detecting distance traveled during swims, encoding action effectiveness, and modulating motor vigor. We found that swimming opens a gate for visual input to cause spiking in serotonergic neurons, enabling encoding of action outcomes and filtering out learning-irrelevant visual signals. Using light-sheet microscopy, voltage sensors, and neurotransmitter/modulator sensors, we tracked millisecond-timescale neuronal input-output computations during behavior. Swim commands initially inhibited serotonergic neurons via GABA, closing the gate to spiking. Immediately after, the gate briefly opened: voltage increased consistent with post-inhibitory rebound, allowing swim-induced visual motion to evoke firing through glutamate, triggering serotonin secretion and modulating motor vigor. Ablating GABAergic neurons impaired raphe coding and motor learning. Thus, serotonergic neuromodulation arises from action-outcome coincidence detection within the raphe, suggesting the existence of similarly fast and precise circuit computations across neuromodulatory nuclei.

Voltage-gated ion channels support electrochemical activity in cells and are largely responsible for information flow throughout the nervous systems. The voltage sensor domains in these channels sense changes in transmembrane potential and control ion flux across membranes. The X-ray structures of a few voltage-gated ion channels in detergents have been determined and have revealed clear structural variations among their respective voltage sensor domains. More recent studies demonstrated that lipids around a voltage-gated channel could directly alter its conformational state in membrane. Because of these disparities, the structural basis for voltage sensing in native membranes remains elusive. Here, through electron-crystallographic analysis of membrane-embedded proteins, we present the detailed view of a voltage-gated potassium channel in its inactivated state. Contrary to all known structures of voltage-gated ion channels in detergents, our data revealed a unique conformation in which the four voltage sensor domains of a voltage-gated potassium channel from Aeropyrum pernix (KvAP) form a ring structure that completely surrounds the pore domain of the channel. Such a structure is named the voltage sensor ring. Our biochemical and electrophysiological studies support that the voltage sensor ring represents a physiological conformation. These data together suggest that lipids exert strong effects on the channel structure and that these effects may be changed upon membrane disruption. Our results have wide implications for lipid-protein interactions in general and for the mechanism of voltage sensing in particular.

1. The voltage- and space-clamp errors associated with the use of a somatic electrode to measure current from dendritic synapses are evaluated using both equivalent-cylinder and morphologically realistic models of neuronal dendritic trees. 2. As a first step toward understanding the properties of synaptic current distortion under voltage-clamp conditions, the attenuation of step and sinusoidal voltage changes are evaluated in equivalent cylinder models. Demonstration of the frequency-dependent attenuation of voltage in the cable is then used as a framework for understanding the distortion of synaptic currents generated at sites remote from the somatic recording electrode and measured in the voltage-clamp recording configuration. 3. Increases in specific membrane resistivity (Rm) are shown to reduce steady-state voltage attenuation, while producing only minimal reduction in attenuation of transient voltage changes. Experimental manipulations that increase Rm therefore improve the accuracy of estimates of reversal potential for electrotonically remote synapses, but do not significantly reduce the attenuation of peak current. In addition, increases in Rm have the effect of slowing the kinetics of poorly clamped synaptic currents. 4. The effects of the magnitude of the synaptic conductance and its kinetics on the measured synaptic currents are also examined and discussed. The error in estimating parameters from measured synaptic currents is greatest for synapses with fast kinetics and large conductances. 5. A morphologically realistic model of a CA3 pyramidal neuron is used to demonstrate the generality of the conclusions derived from equivalent cylinder models. The realistic model is also used to fit synaptic currents generated by stimulation of mossy fiber (MF) and commissural/associational (C/A) inputs to CA3 neurons and to estimate the amount of distortion of these measured currents. 6. Anatomic data from the CA3 pyramidal neuron model are used to construct a simplified two-cylinder CA3 model. This model is used to estimate the electrotonic distances of MF synapses (which are located proximal to the soma) and perforant path (PP) synapses (which are located at the distal ends of the apical dendrites) and the distortion of synaptic current parameters measured for these synapses. 7. Results from the equivalent-cylinder models, the morphological CA3 model, and the simplified CA3 model all indicate that the amount of distortion of synaptic currents increases steeply as a function of distance from the soma. MF synapses close to the soma are likely to be subject only to small space-clamp errors, whereas MF synapses farther from the soma are likely to be substantially attenuated.(ABSTRACT TRUNCATED AT 400 WORDS)