Recalibration of path integration in hippocampal place cells.

Hippocampal place cells are spatially tuned neurons that serve as elements of a 'cognitive map' in the mammalian brain1. To detect the animal's location, place cells are thought to rely upon two interacting mechanisms: sensing the position of the animal relative to familiar landmarks2,3 and measuring the distance and direction that the animal has travelled from previously occupied locations4-7. The latter mechanism-known as path integration-requires a finely tuned gain factor that relates the animal's self-movement to the updating of position on the internal cognitive map, as well as external landmarks to correct the positional error that accumulates8,9. Models of hippocampal place cells and entorhinal grid cells based on path integration treat the path-integration gain as a constant9-14, but behavioural evidence in humans suggests that the gain is modifiable15. Here we show, using physiological evidence from rat hippocampal place cells, that the path-integration gain is a highly plastic variable that can be altered by persistent conflict between self-motion cues and feedback from external landmarks. In an augmented-reality system, visual landmarks were moved in proportion to the movement of a rat on a circular track, creating continuous conflict with path integration. Sustained exposure to this cue conflict resulted in predictable and prolonged recalibration of the path-integration gain, as estimated from the place cells after the landmarks were turned off. We propose that this rapid plasticity keeps the positional update in register with the movement of the rat in the external world over behavioural timescales. These results also demonstrate that visual landmarks not only provide a signal to correct cumulative error in the path-integration system4,8,16-19, but also rapidly fine-tune the integration computation itself.

Control becomes habitual early on when learning a novel motor skill.

When people perform the same task repeatedly, their behavior becomes habitual, or inflexible to changes in the goals or structure of a task. Although habits have been hypothesized to be a key aspect of motor skill acquisition, there has been little empirical work investigating the relationship between skills and habits. To better understand this relationship, we examined whether and when people's behavior would become habitual as they learned a challenging new motor skill: maneuvering an on-screen cursor with a nonintuitive bimanual mapping from hand to cursor position. After participants practiced using this mapping for up to 10 days, we altered the mapping between the hands and the cursor to assess whether participants could flexibly adjust their behavior or would habitually persist in performing the task the way they had originally learned. We found that participants' behavior became habitual within 2 days of practice, at which point they were still relatively unskilled. Further practice led to improved skill but did not alter the strength of habitual behavior. These data demonstrate that motor skills become habitual after relatively little training but can nevertheless further improve with practice. We suggest that building habits early in learning may be a crucial step in acquiring new motor skills.NEW & NOTEWORTHY Habits and motor skills have often been thought to be deeply related, but very few studies have empirically examined the relationship between the two. We present evidence that habits emerge early in learning, long before a motor skill has been fully learned. Our results suggest that habits may play an integral role in the learning and performance of motor skills from even the early stages of acquiring a new skill.

Complementary spatial and timing control in rhythmic arm movements.

Volitional rhythmic motor behaviors such as limb cycling and locomotion exhibit spatial and timing regularity. Such rhythmic movements are executed in the presence of exogenous visual and nonvisual cues, and previous studies have shown the pivotal role that vision plays in guiding spatial and temporal regulation. However, the influence of nonvisual information conveyed through auditory or touch sensory pathways, and its effect on control, remains poorly understood. To characterize the function of nonvisual feedback in rhythmic arm control, we designed a paddle juggling task in which volunteers bounced a ball off a rigid elastic surface to a target height in virtual reality by moving a physical handle with the right hand. Feedback was delivered at two key phases of movement: visual feedback at ball peaks only and simultaneous audio and haptic feedback at ball-paddle collisions. In contrast to previous work, we limited visual feedback to the minimum required for jugglers to assess spatial accuracy, and we independently perturbed the spatial dimensions and the timing of feedback. By separately perturbing this information, we evoked dissociable effects on spatial accuracy and timing, confirming that juggling, and potentially other rhythmic tasks, involves two complementary processes with distinct dynamics: spatial error correction and feedback timing synchronization. Moreover, we show evidence that audio and haptic feedback provide sufficient information for the brain to control the timing synchronization process by acting as a metronome-like cue that triggers hand movement. NEW & NOTEWORTHY Vision contains rich information for control of rhythmic arm movements; less is known, however, about the role of nonvisual feedback (touch and sound). Using a virtual ball bouncing task allowing independent real-time manipulation of spatial location and timing of cues, we show their dissociable roles in regulating motor behavior. We confirm that visual feedback is used to correct spatial error and provide new evidence that nonvisual event cues act to reset the timing of arm movements.

Patients with Cerebellar Ataxia Do Not Benefit from Limb Weights.

Patients with cerebellar ataxia are sometimes treated by the addition of mass to the limbs, though this practice has received limited study. Recent work suggests that adding mass to the limbs might have predictable effects on the pattern of cerebellar dysmetria (i.e., over or undershooting) that depends on a hypothesized mismatch between the actual limb inertia and the brain's estimate of limb inertia. Based on this model, we predicted that addition of mass would only be effective in reducing dysmetria in hypometric patients. Cerebellar patients were challenged with making a single-joint, single degree of freedom reaching movement while various limb masses were tested. In this task, some single-jointed reaches were improved by adding masses that were optimized in a patient-specific manner. However, this improvement did not translate to multi-joint movements. In multi-joint movements, the "best" patient-specific masses (as determined in a single-joint task) generally exacerbated subjects' reaching errors. This finding raises questions as to the merits of adding limb weights as a therapy to mitigate the effects of dysmetria.

Mutually opposing forces during locomotion can eliminate the tradeoff between maneuverability and stability.

A surprising feature of animal locomotion is that organisms typically produce substantial forces in directions other than what is necessary to move the animal through its environment, such as perpendicular to, or counter to, the direction of travel. The effect of these forces has been difficult to observe because they are often mutually opposing and therefore cancel out. Indeed, it is likely that these forces do not contribute directly to movement but may serve an equally important role: to simplify and enhance the control of locomotion. To test this hypothesis, we examined a well-suited model system, the glass knifefish Eigenmannia virescens, which produces mutually opposing forces during a hovering behavior that is analogous to a hummingbird feeding from a moving flower. Our results and analyses, which include kinematic data from the fish, a mathematical model of its swimming dynamics, and experiments with a biomimetic robot, demonstrate that the production and differential control of mutually opposing forces is a strategy that generates passive stabilization while simultaneously enhancing maneuverability. Mutually opposing forces during locomotion are widespread across animal taxa, and these results indicate that such forces can eliminate the tradeoff between stability and maneuverability, thereby simplifying neural control.

The visual representation of 3D object orientation in parietal cortex.

An accurate representation of three-dimensional (3D) object orientation is essential for interacting with the environment. Where and how the brain visually encodes 3D object orientation remains unknown, but prior studies suggest the caudal intraparietal area (CIP) may be involved. Here, we develop rigorous analytical methods for quantifying 3D orientation tuning curves, and use these tools to the study the neural coding of surface orientation. Specifically, we show that single neurons in area CIP of the rhesus macaque jointly encode the slant and tilt of a planar surface, and that across the population, the distribution of preferred slant-tilts is not statistically different from uniform. This suggests that all slant-tilt combinations are equally represented in area CIP. Furthermore, some CIP neurons are found to also represent the third rotational degree of freedom that determines the orientation of the image pattern on the planar surface. Together, the present results suggest that CIP is a critical neural locus for the encoding of all three rotational degrees of freedom specifying an object's 3D spatial orientation.

Haptic feedback enhances rhythmic motor control by reducing variability, not improving convergence rate.

Stability and performance during rhythmic motor behaviors such as locomotion are critical for survival across taxa: falling down would bode well for neither cheetah nor gazelle. Little is known about how haptic feedback, particularly during discrete events such as the heel-strike event during walking, enhances rhythmic behavior. To determine the effect of haptic cues on rhythmic motor performance, we investigated a virtual paddle juggling behavior, analogous to bouncing a table tennis ball on a paddle. Here, we show that a force impulse to the hand at the moment of ball-paddle collision categorically improves performance over visual feedback alone, not by regulating the rate of convergence to steady state (e.g., via higher gain feedback or modifying the steady-state hand motion), but rather by reducing cycle-to-cycle variability. This suggests that the timing and state cues afforded by haptic feedback decrease the nervous system's uncertainty of the state of the ball to enable more accurate control but that the feedback gain itself is unaltered. This decrease in variability leads to a substantial increase in the mean first passage time, a measure of the long-term metastability of a stochastic dynamical system. Rhythmic tasks such as locomotion and juggling involve intermittent contact with the environment (i.e., hybrid transitions), and the timing of such transitions is generally easy to sense via haptic feedback. This timing information may improve metastability, equating to less frequent falls or other failures depending on the task.

Mechanical processing via passive dynamic properties of the cockroach antenna can facilitate control during rapid running.

The integration of information from dynamic sensory structures operating on a moving body is a challenge for locomoting animals and engineers seeking to design agile robots. As a tactile sensor is a physical linkage mediating mechanical interactions between body and environment, mechanical tuning of the sensor is critical for effective control. We determined the open-loop dynamics of a tactile sensor, specifically the antenna of the American cockroach, Periplaneta americana, an animal that escapes predators by using its antennae during rapid closed-loop tactilely mediated course control. Geometrical measurements and static bending experiments revealed an exponentially decreasing flexural stiffness (EI) from base to tip. Quasi-static experiments with a physical model support the hypothesis that a proximodistally decreasing EI can simplify control by increasing preview distance and allowing effective mapping to a putative control variable--body-to-wall distance--compared with an antenna with constant EI. We measured the free response at the tip of the antenna following step deflections and determined that the antenna rapidly damps large deflections: over 90% of the perturbation is rejected within the first cycle, corresponding to almost one stride period during high-speed running (~50 ms). An impulse-like perturbation near the tip revealed dynamics that were characteristic of an inelastic collision, keeping the antenna in contact with an object after impact. We contend that proximodistally decreasing stiffness, high damping and inelasticity simplify control during high-speed tactile tasks by increasing preview distance, providing a one-dimensional map between antennal bending and body-to-wall distance, and increasing the reliability of tactile information.

Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration.

Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)-a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.

Continuous Bump Attractor Networks Require Explicit Error Coding for Gain Recalibration.

Representations of continuous variables are crucial to create internal models of the external world. A prevailing model of how the brain maintains these representations is given by continuous bump attractor networks (CBANs) in a broad range of brain functions across different areas, such as spatial navigation in hippocampal/entorhinal circuits and working memory in prefrontal cortex. Through recurrent connections, a CBAN maintains a persistent activity bump, whose peak location can vary along a neural space, corresponding to different values of a continuous variable. To track the value of a continuous variable changing over time, a CBAN updates the location of its activity bump based on inputs that encode the changes in the continuous variable (e.g., movement velocity in the case of spatial navigation)-a process akin to mathematical integration. This integration process is not perfect and accumulates error over time. For error correction, CBANs can use additional inputs providing ground-truth information about the continuous variable's correct value (e.g., visual landmarks for spatial navigation). These inputs enable the network dynamics to automatically correct any representation error. Recent experimental work on hippocampal place cells has shown that, beyond correcting errors, ground-truth inputs also fine-tune the gain of the integration process, a crucial factor that links the change in the continuous variable to the updating of the activity bump's location. However, existing CBAN models lack this plasticity, offering no insights into the neural mechanisms and representations involved in the recalibration of the integration gain. In this paper, we explore this gap by using a ring attractor network, a specific type of CBAN, to model the experimental conditions that demonstrated gain recalibration in hippocampal place cells. Our analysis reveals the necessary conditions for neural mechanisms behind gain recalibration within a CBAN. Unlike error correction, which occurs through network dynamics based on ground-truth inputs, gain recalibration requires an additional neural signal that explicitly encodes the error in the network's representation via a rate code. Finally, we propose a modified ring attractor network as an example CBAN model that verifies our theoretical findings. Combining an error-rate code with Hebbian synaptic plasticity, this model achieves recalibration of integration gain in a CBAN, ensuring accurate representation for continuous variables.

Sensorimotor adaptation to destabilizing dynamics in weakly electric fish.

Humans and other animals can readily learn to compensate for changes in the dynamics of movement. Such changes can result from an injury or changes in the weight of carried objects. These changes in dynamics can lead not only to reduced performance but also to dramatic instabilities. We evaluated the impacts of compensatory changes in control policies in relation to stability and robustness in Eigenmannia virescens, a species of weakly electric fish. We discovered that these fish retune their sensorimotor control system in response to experimentally generated destabilizing dynamics. Specifically, we used an augmented reality system to manipulate sensory feedback during an image stabilization task in which a fish maintained its position within a refuge. The augmented reality system measured the fish's movements in real time. These movements were passed through a high-pass filter and multiplied by a gain factor before being fed back to the refuge motion. We adjusted the gain factor to gradually destabilize the fish's sensorimotor loop. The fish retuned their sensorimotor control system to compensate for the experimentally induced destabilizing dynamics. This retuning was partially maintained when the augmented reality feedback was abruptly removed. The compensatory changes in sensorimotor control improved tracking performance as well as control-theoretic measures of robustness, including reduced sensitivity to disturbances and improved phase margins.

Control and recalibration of path integration in place cells using optic flow.

Hippocampal place cells are influenced by both self-motion (idiothetic) signals and external sensory landmarks as an animal navigates its environment. To continuously update a position signal on an internal 'cognitive map', the hippocampal system integrates self-motion signals over time, a process that relies on a finely calibrated path integration gain that relates movement in physical space to movement on the cognitive map. It is unclear whether idiothetic cues alone, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if polarizing position information provided by landmarks is essential for this recalibration. Here, we demonstrate both recalibration of path integration gain and systematic control of place fields by pure optic flow information in freely moving rats. These findings demonstrate that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.

Flexible strategies for flight control: an active role for the abdomen.

Moving animals orchestrate myriad motor systems in response to multimodal sensory inputs. Coordinating movement is particularly challenging in flight control, where animals deal with potential instability and multiple degrees of freedom of movement. Prior studies have focused on wings as the primary flight control structures, for which changes in angle of attack or shape are used to modulate lift and drag forces. However, other actuators that may impact flight performance are reflexively activated during flight. We investigated the visual-abdominal reflex displayed by the hawkmoth Manduca sexta to determine its role in flight control. We measured the open-loop stimulus-response characteristics (measured as a transfer function) between the visual stimulus and abdominal response in tethered moths. The transfer function reveals a 41 ms delay and a high-pass filter behavior with a pass band starting at ~0.5 Hz. We also developed a simplified mathematical model of hovering flight wherein articulation of the thoracic-abdominal joint redirects an average lift force provided by the wings. We show that control of the joint, subject to a high-pass filter, is sufficient to maintain stable hovering, but with a slim stability margin. Our experiments and models suggest a novel mechanism by which articulation of the body or 'airframe' of an animal can be used to redirect lift forces for effective flight control. Furthermore, the small stability margin may increase flight agility by easing the transition from stable flight to a more maneuverable, unstable regime.

Closed-loop stabilization of the Jamming Avoidance Response reveals its locally unstable and globally nonlinear dynamics.

The Jamming Avoidance Response, or JAR, in the weakly electric fish has been analyzed at all levels of organization, from whole-organism behavior down to specific ion channels. Nevertheless, a parsimonious description of the JAR behavior in terms of a dynamical system model has not been achieved at least in part due to the fact that 'avoidance' behaviors are both intrinsically unstable and nonlinear. We overcame the instability of the JAR in Eigenmannia virescens by closing a feedback loop around the behavioral response of the animal. Specifically, the instantaneous frequency of a jamming stimulus was tied to the fish's own electrogenic frequency by a feedback law. Without feedback, the fish's own frequency diverges from the stimulus frequency, but appropriate feedback stabilizes the behavior. After stabilizing the system, we measured the responses in the fish's instantaneous frequency to various stimuli. A delayed first-order linear system model fitted the behavior near the equilibrium. Coherence to white noise stimuli together with quantitative agreement across stimulus types supported this local linear model. Next, we examined the intrinsic nonlinearity of the behavior using clamped frequency difference experiments to extend the model beyond the neighborhood of the equilibrium. The resulting nonlinear model is composed of competing motor return and sensory escape terms. The model reproduces responses to step and ramp changes in the difference frequency (df) and predicts a 'snap-through' bifurcation as a function of dF that we confirmed experimentally.

Locomotion- and mechanics-mediated tactile sensing: antenna reconfiguration simplifies control during high-speed navigation in cockroaches.

Animals can expend energy to acquire sensory information by emitting signals and/or moving sensory structures. We propose that the energy from locomotion itself could permit control of a sensor, whereby animals use the energy from movement to reconfigure a passive sensor. We investigated high-speed, antenna-mediated tactile navigation in the cockroach Periplaneta americana. We discovered that the passive antennal flagellum can assume two principal mechanical states, such that the tip is either projecting backward or forward. Using a combination of behavioral and robotic experiments, we demonstrate that a switch in the antenna's state is mediated via the passive interactions between the sensor and its environment, and this switch strongly influences wall-tracking control. When the tip of the antenna is projected backward, the animals maintain greater body-to-wall distance with fewer body collisions and less leg-wall contact than when the tip is projecting forward. We hypothesized that distally pointing mechanosensory hairs at the tip of the antenna mediate the switch in state by interlocking with asperities in the wall surface. To test this hypothesis, we performed laser ablation of chemo-mechanosensory hairs and added artificial hairs to a robotic antenna. In both the natural and artificial systems, the presence of hairs categorically increased an antenna's probability of switching state. Antennal hairs, once thought to only play a role in sensing, are sufficient for mechanically reconfiguring the state of the entire antenna when coupled with forward motion. We show that the synergy between antennal mechanics, locomotion and the environment simplifies tactile sensing.

Active sensing via movement shapes spatiotemporal patterns of sensory feedback.

Previous work has shown that animals alter their locomotor behavior to increase sensing volumes. However, an animal's own movement also determines the spatial and temporal dynamics of sensory feedback. Because each sensory modality has unique spatiotemporal properties, movement has differential and potentially independent effects on each sensory system. Here we show that weakly electric fish dramatically adjust their locomotor behavior in relation to changes of modality-specific information in a task in which increasing sensory volume is irrelevant. We varied sensory information during a refuge-tracking task by changing illumination (vision) and conductivity (electroreception). The gain between refuge movement stimuli and fish tracking responses was functionally identical across all sensory conditions. However, there was a significant increase in the tracking error in the dark (no visual cues). This was a result of spontaneous whole-body oscillations (0.1 to 1 Hz) produced by the fish. These movements were costly: in the dark, fish swam over three times further when tracking and produced more net positive mechanical work. The magnitudes of these oscillations increased as electrosensory salience was degraded via increases in conductivity. In addition, tail bending (1.5 to 2.35 Hz), which has been reported to enhance electrosensory perception, occurred only during trials in the dark. These data show that both categories of movements - whole-body oscillations and tail bends - actively shape the spatiotemporal dynamics of electrosensory feedback.

Beyond the Jamming Avoidance Response: weakly electric fish respond to the envelope of social electrosensory signals.

Recent studies have shown that central nervous system neurons in weakly electric fish respond to artificially constructed electrosensory envelopes, but the behavioral relevance of such stimuli is unclear. Here we investigate the possibility that social context creates envelopes that drive behavior. When Eigenmannia virescens are in groups of three or more, the interactions between their pseudo-sinusoidal electric fields can generate 'social envelopes'. We developed a simple mathematical prediction for how fish might respond to such social envelopes. To test this prediction, we measured the responses of E. virescens to stimuli consisting of two sinusoids, each outside the range of the Jamming Avoidance Response (JAR), that when added to the fish's own electric field produced low-frequency (below 10 Hz) social envelopes. Fish changed their electric organ discharge (EOD) frequency in response to these envelopes, which we have termed the Social Envelope Response (SER). In 99% of trials, the direction of the SER was consistent with the mathematical prediction. The SER was strongest in response to the lowest initial envelope frequency tested (2 Hz) and depended on stimulus amplitude. The SER generally resulted in an increase of the envelope frequency during the course of a trial, suggesting that this behavior may be a mechanism for avoiding low-frequency social envelopes. Importantly, the direction of the SER was not predicted by the superposition of two JAR responses: the SER was insensitive to the amplitude ratio between the sinusoids used to generate the envelope, but was instead predicted by the sign of the difference of difference frequencies.

Wide-angle, monocular head tracking using passive markers.

BACKGROUND: Camera images can encode large amounts of visual information of an animal and its environment, enabling high fidelity 3D reconstruction of the animal and its environment using computer vision methods. Most systems, both markerless (e.g. deep learning based) and marker-based, require multiple cameras to track features across multiple points of view to enable such 3D reconstruction. However, such systems can be expensive and are challenging to set up in small animal research apparatuses. NEW METHODS: We present an open-source, marker-based system for tracking the head of a rodent for behavioral research that requires only a single camera with a potentially wide field of view. The system features a lightweight visual target and computer vision algorithms that together enable high-accuracy tracking of the six-degree-of-freedom position and orientation of the animal's head. The system, which only requires a single camera positioned above the behavioral arena, robustly reconstructs the pose over a wide range of head angles (360° in yaw, and approximately ± 120° in roll and pitch). RESULTS: Experiments with live animals demonstrate that the system can reliably identify rat head position and orientation. Evaluations using a commercial optical tracker device show that the system achieves accuracy that rivals commercial multi-camera systems. COMPARISON WITH EXISTING METHODS: Our solution significantly improves upon existing monocular marker-based tracking methods, both in accuracy and in allowable range of motion. CONCLUSIONS: The proposed system enables the study of complex behaviors by providing robust, fine-scale measurements of rodent head motions in a wide range of orientations.

Advances in non-invasive tracking of wave-type electric fish in natural and laboratory settings.

Recent technological advances greatly improved the possibility to study freely behaving animals in natural conditions. However, many systems still rely on animal-mounted devices, which can already bias behavioral observations. Alternatively, animal behaviors can be detected and tracked in recordings of stationary sensors, e.g., video cameras. While these approaches circumvent the influence of animal-mounted devices, identification of individuals is much more challenging. We take advantage of the individual-specific electric fields electric fish generate by discharging their electric organ (EOD) to record and track their movement and communication behaviors without interfering with the animals themselves. EODs of complete groups of fish can be recorded with electrode arrays submerged in the water and then be tracked for individual fish. Here, we present an improved algorithm for tracking electric signals of wave-type electric fish. Our algorithm benefits from combining and refining previous approaches of tracking individual specific EOD frequencies and spatial electric field properties. In this process, the similarity of signal pairs in extended data windows determines their tracking order, making the algorithm more robust against detection losses and intersections. We quantify the performance of the algorithm and show its application for a data set recorded with an array of 64 electrodes distributed over a 12 m2 section of a stream in the Llanos, Colombia, where we managed, for the first time, to track Apteronotus leptorhynchus over many days. These technological advances make electric fish a unique model system for a detailed analysis of social and communication behaviors, with strong implications for our research on sensory coding.