The neural dynamics of sensory focus

Task-Related Sensorimotor Adjustments Increase the Sensory Range in Electrolocation

Perception and motor control traditionally are studied separately. However, motor activity can serve as a scaffold to shape the sensory flow. This tight link between motor actions and sensing is particularly evident in active sensory systems. Here, we investigate how the weakly electric mormyrid fish Gnathonemus petersii of undetermined sex structure their sensing and motor behavior while learning a perceptual task. We find systematic adjustments of the motor behavior that correlate with an increased performance. Using a model to compute the electrosensory input, we show that these behavioral adjustments improve the sensory input. As we find low neuronal detection thresholds at the level of medullary electrosensory neurons, it seems that the behavior-driven improvements of the sensory input are highly suitable to overcome the sensory limitations, thereby increasing the sensory range. Our results show that motor control is an active component of sensory learning, demonstrating that a detailed understanding of contribution of motor actions to sensing is needed to understand even seemingly simple behaviors.SIGNIFICANCE STATEMENT Motor-guided sensation and perception are intertwined, with motor behavior serving as a scaffold to shape the sensory input. We characterized how the weakly electric mormyrid fish Gnathonemus petersii, as it learns a perceptual task, restructures its sensorimotor behavior. We find that systematic adjustments of the motor behavior correlate with increased performance and a shift of the sensory attention of the animal. Analyzing the afferent electrosensory input shows that a significant gain in information results from these sensorimotor adjustments. Our results show that motor control can be an active component of sensory learning. Researching the sensory corollaries of motor control thus can be crucial to understand sensory sensation and perception under naturalistic conditions.

Neural activity in a hippocampus-like region of the teleost pallium is associated with active sensing and navigation

Most vertebrates use active sensing strategies for perception, cognition and control of motor activity. These strategies include directed body/sensor movements or increases in discrete sensory sampling events. The weakly electric fish, Gymnotus sp., uses its active electric sense during navigation in the dark. Electric organ discharge rate undergoes transient increases during navigation to increase electrosensory sampling. Gymnotus also use stereotyped backward swimming as an important form of active sensing that brings objects toward the electroreceptor dense fovea-like head region. We wirelessly recorded neural activity from the pallium of freely swimming Gymnotus. Spiking activity was sparse and occurred only during swimming. Notably, most units tended to fire during backward swims and their activity was on average coupled to increases in sensory sampling. Our results provide the first characterization of neural activity in a hippocampal (CA3)-like region of a teleost fish brain and connects it to active sensing of spatial environmental features.

M current regulates firing mode and spike reliability in a collision-detecting neuron

All animals must detect impending collisions to escape and reliably discriminate them from nonthreatening stimuli, thus preventing false alarms. Therefore, it is no surprise that animals have evolved highly selective and sensitive neurons dedicated to such tasks. We examined a well-studied collision-detection neuron in the grasshopper ( Schistocerca americana) using in vivo electrophysiology, pharmacology, and computational modeling. This lobula giant movement detector (LGMD) neuron is excitable by inputs originating from each ommatidia of the compound eye. It possesses many intrinsic properties that increase its selectivity to objects approaching on a collision course, including switching between burst and nonburst firing. In this study, we demonstrate that the LGMD neuron exhibits a large M current, generated by noninactivating K⁺ channels, that shortens the temporal window of dendritic integration, regulates a firing mode switch between burst and isolated spiking, increases the precision of spike timing, and increases the reliability of spike propagation to downstream motor centers. By revealing how the M current increases the LGMD's ability to detect impending collisions, our results suggest that similar channels may play an analogous role in other collision detection circuits. NEW & NOTEWORTHY The ability to reliably detect impending collisions is a critical survival skill. The nervous systems of many animals have developed dedicated neurons for accomplishing this task. We used a mix of in vivo electrophysiology and computational modeling to investigate the role of M potassium channels within one such collision-detecting neuron and show that through regulation of burst firing and enhancement of spiking reliability, the M current increases the ability to detect impending collisions.

Current-Efficient Preamplifier Architecture for CMRR Sensitive Neural Recording Applications

There are neural recording applications in which the amplitude of common-mode interfering signals is several orders of magnitude higher than the amplitude of the signals of interest. This challenging situation for neural amplifiers occurs, among other applications, in neural recordings of weakly electric fish or nerve activity recordings made with cuff electrodes. This paper reports an integrated neural amplifier architecture targeting in-vivo recording of local field potentials and unitary signals from the brain stem of a weakly electric fish Gymnotus omarorum. The proposed architecture offers low noise, high common-mode rejection ratio (CMRR), current-efficiency, and a high-pass frequency fixed without MOS pseudoresistors. The main contributions of this work are the overall architecture coupled with an efficient and simple single-stage circuit for the amplifier main transconductor, and the ability of the amplifier to acquire biopotential signals from high-amplitude common-mode interference in an unshielded environment. A fully-integrated neural preamplifier, which performs well in line with the state-of-the-art of the field while providing enhanced CMRR performance, was fabricated in a 0.5  m CMOS process. Results from measurements show that the gain is 49.5 dB, the bandwidth ranges from 13 Hz to 9.8 kHz, the equivalent input noise is 1.88  V, the CMRR is 87 dB and the Noise Efficiency Factor is 2.1. In addition, in-vivo recordings of weakly electric fish neural activity performed by the proposed amplifier are introduced and favorably compared with those of a commercial laboratory instrumentation system.

Serotonin Selectively Increases Detectability of Motion Stimuli in the Electrosensory System

Serotonergic innervation of sensory areas is found ubiquitously across the central nervous system of vertebrates. Here, we used a system's level approach to investigate the role of serotonin on processing motion stimuli in the electrosensory system of the weakly electric fish Apteronotus albifrons. We found that exogenous serotonin application increased the firing activity of pyramidal neural responses to both looming and receding motion. Separating spikes belonging to bursts from those that were isolated revealed that this effect was primarily due to increased burst firing. Moreover, when investigating whether firing activity during stimulation could be discriminated from baseline (i.e., in the absence of stimulation), we found that serotonin increased stimulus discriminability only for some stimuli. This is because increased burst firing was most prominent for these. Further, the effects of serotonin were highly heterogeneous, with some neurons displaying large while others instead displaying minimal changes in responsiveness following serotonin application. Further analysis revealed that serotonin application had the greatest effect on neurons with low baseline firing rates and little to no effect on neurons with high baseline firing rates. Finally, the effects of serotonin on sensory neuron responses were largely independent of object velocity. Our results therefore reveal a novel function for the serotonergic system in selectively enhancing discriminability for motion stimuli.

Motion parallax in electric sensing

A crucial step in forming spatial representations of the environment involves the estimation of relative distance. Active sampling through specific movements is considered essential for optimizing the sensory flow that enables the extraction of distance cues. However, in electric sensing, direct evidence for the generation and exploitation of sensory flow is lacking. Weakly electric fish rely on a self-generated electric field to navigate and capture prey in the dark. This electric sense provides a blurred representation of the environment, making the exquisite sensory abilities of electric fish enigmatic. Stereotyped back-and-forth swimming patterns reminiscent of visual peering movements are suggestive of the active generation of sensory flow, but how motion contributes to the disambiguation of the electrosensory world remains unclear. Here, we show that a dipole-like electric field geometry coupled to motion provides the physical basis for a nonvisual parallax. We then show in a behavioral assay that this cue is used for electrosensory distance perception across phylogenetically distant taxa of weakly electric fish. Notably, these species electrically sample the environment in temporally distinct ways (using discrete pulses or quasisinusoidal waves), suggesting a ubiquitous role for parallax in electric sensing. Our results demonstrate that electrosensory information is extracted from sensory flow and used in a behaviorally relevant context. A better understanding of motion-based electric sensing will provide insight into the sensorimotor coordination required for active sensing in general and may lead to improved electric field-based imaging applications in a variety of contexts.

Structural insights into the committed step of bacterial phospholipid biosynthesis

The membrane-integral glycerol 3-phosphate (G3P) acyltransferase PlsY catalyses the committed and essential step in bacterial phospholipid biosynthesis by acylation of G3P, forming lysophosphatidic acid. It contains no known acyltransferase motifs, lacks eukaryotic homologs, and uses the unusual acyl-phosphate as acyl donor, as opposed to acyl-CoA or acyl-carrier protein for other acyltransferases. Previous studies have identified several PlsY inhibitors as potential antimicrobials. Here we determine the crystal structure of PlsY at 1.48 Å resolution, revealing a seven-transmembrane helix fold. Four additional substrate- and product-bound structures uncover the atomic details of its relatively inflexible active site. Structure and mutagenesis suggest a different acylation mechanism of ‘substrate-assisted catalysis’ that, unlike other acyltransferases, does not require a proteinaceous catalytic base to complete. The structure data and a high-throughput enzymatic assay developed in this work should prove useful for virtual and experimental screening of inhibitors against this vital bacterial enzyme.

The first step in bacterial phospholipid biosynthesis is the acylation of glycerol 3-phosphate to form lysophosphatidic acid. Here, the authors present the high resolution crystal structure of the glycerol 3-phosphate acyltransferase PlsY, a membrane protein and give insights into its catalytical mechanism.

Differential receptive field organizations give rise to nearly identical neural correlations across three parallel sensory maps in weakly electric fish

Understanding how neural populations encode sensory information thereby leading to perception and behavior (i.e., the neural code) remains an important problem in neuroscience. When investigating the neural code, one must take into account the fact that neural activities are not independent but are actually correlated with one another. Such correlations are seen ubiquitously and have a strong impact on neural coding. Here we investigated how differences in the antagonistic center-surround receptive field (RF) organization across three parallel sensory maps influence correlations between the activities of electrosensory pyramidal neurons. Using a model based on known anatomical differences in receptive field center size and overlap, we initially predicted large differences in correlated activity across the maps. However, in vivo electrophysiological recordings showed that, contrary to modeling predictions, electrosensory pyramidal neurons across all three segments displayed nearly identical correlations. To explain this surprising result, we incorporated the effects of RF surround in our model. By systematically varying both the RF surround gain and size relative to that of the RF center, we found that multiple RF structures gave rise to similar levels of correlation. In particular, incorporating known physiological differences in RF structure between the three maps in our model gave rise to similar levels of correlation. Our results show that RF center overlap alone does not determine correlations which has important implications for understanding how RF structure influences correlated neural activity.

Growing evidence across nervous systems and species shows that the activities of neighboring neurons are not independent but are correlated with one another, which has important implications for neural coding. Such correlations are generally thought to be due to shared input. However, how this shared input is integrated by neurons in order to give rise to correlated activity is not well understood in general. Here we investigated how receptive field structure determines correlations between the activities of electrosensory pyramidal neurons in weakly electric fish. To do so, we used a combination of mathematical modeling of the known antagonistic center-surround RF structure as well as in vivo electrophysiological recordings. Our results show that the amount of receptive field center overlap alone is not sufficient to explain experimentally observed neural correlations in general. This is because our experimental data shows that pyramidal neurons with very different amounts of receptive field center overlap display almost identical correlations between their activities. Further, our modeling shows that both receptive field center and surround play important roles in determining correlated activity, such that very different combinations of relative RF surround strength and size can generate nearly identical correlations between neural activities. We discuss the implications of our results for sensory processing.

Sensory Flow as a Basis for a Novel Distance Cue in Freely Behaving Electric Fish

The sensory input that an animal receives is directly linked to its motor activity. Behavior thus enables animals to influence their sensory input, a concept referred to as active sensing. How such behavior can serve as a scaffold for generating sensory information is of general scientific interest. In this article, we investigate how behavior can shape sensory information by using some unique features of the sensorimotor system of the weakly electric fish. Based on quantitative behavioral characterizations and computational reconstruction of sensory input, we show how electrosensory flow is actively created during highly patterned, spontaneous behavior in Gnathonemus petersii. The spatiotemporal structure of the sensory input provides information for the computation of a novel distance cue, which allows for a continuous estimation of distance. This has significant advantages over previously known nondynamic distance estimators as determined from electric image blur. Our investigation of the sensorimotor interactions in pulsatile electrolocation shows, for the first time, that the electrosensory flow contains behaviorally relevant information accessible only through active behavior. As patterned sensory behaviors are a shared feature of (active) sensory systems, our results have general implications for the understanding of (active) sensing, with the proposed sensory flow-based measure being potentially pertinent to a broad range of sensory modalities.

SIGNIFICANCE STATEMENT

Acquisition of sensory information depends on motion, as either an animal or its sensors move. Behavior can thus actively influence the sensory flow; and in this way, behavior can be seen as a manifestation of the brain's integrative functions. The properties of the active pulsatile electrolocation system in Gnathonemus petersii allow for the sensory input to be computationally reconstructed, enabling us to link the informational content of spatiotemporal sensory dynamics to behavior. Our study reveals a novel sensory cue for estimating depth that is actively generated by the fishes' behavior. The physical and behavioral similarities between electrolocation and other active sensory systems suggest that this may be a mechanism shared by (active) sensory systems.

Feedback Synthesizes Neural Codes for Motion

In senses as diverse as vision, hearing, touch, and the electrosense, sensory neurons receive bottom-up input from the environment, as well as top-down input from feedback loops involving higher brain regions [1-4]. Through connectivity with local inhibitory interneurons, these feedback loops can exert both positive and negative control over fundamental aspects of neural coding, including bursting [5, 6] and synchronous population activity [7, 8]. Here we show that a prominent midbrain feedback loop synthesizes a neural code for motion reversal in the hindbrain electrosensory ON- and OFF-type pyramidal cells. This top-down mechanism generates an accurate bidirectional encoding of object position, despite the inability of the electrosensory afferents to generate a consistent bottom-up representation [9, 10]. The net positive activity of this midbrain feedback is additionally regulated through a hindbrain feedback loop, which reduces stimulus-induced bursting and also dampens the ON and OFF cell responses to interfering sensory input [11]. We demonstrate that synthesis of motion representations and cancellation of distracting signals are mediated simultaneously by feedback, satisfying an accepted definition of spatial attention [12]. The balance of excitatory and inhibitory feedback establishes a "focal" distance for optimized neural coding, whose connection to a classic motion-tracking behavior provides new insight into the computational roles of feedback and active dendrites in spatial localization [13, 14].

Electrosensory neural responses to natural electro-communication stimuli are distributed along a continuum

Neural heterogeneities are seen ubiquitously within the brain and greatly complicate classification efforts. Here we tested whether the responses of an anatomically well-characterized sensory neuron population to natural stimuli could be used for functional classification. To do so, we recorded from pyramidal cells within the electrosensory lateral line lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus in response to natural electro-communication stimuli as these cells can be anatomically classified into six different types. We then used two independent methodologies to functionally classify responses: one relies of reducing the dimensionality of a feature space while the other directly compares the responses themselves. Both methodologies gave rise to qualitatively similar results: while ON and OFF-type cells could easily be distinguished from one another, ELL pyramidal neuron responses are actually distributed along a continuum rather than forming distinct clusters due to heterogeneities. We discuss the implications of our results for neural coding and highlight some potential advantages.

Waveform sensitivity of electroreceptors in the pulse-type weakly electric fish Gymnotus omarorum

As in most sensory systems, electrosensory images in weakly electric fish are encoded in two parallel pathways, fast and slow. From work on wave-type electric fish, these fast and slow pathways are thought to encode the time and amplitude of electrosensory signals, respectively. The present study focuses on the primary afferents giving origin to the slow path of the pulse-type weakly electric fish Gymnotus omarorum We found that burst duration coders respond with a high-frequency train of spikes to each electric organ discharge. They also show high sensitivity to phase-frequency distortions of the self-generated local electric field. We explored this sensitivity by manipulating the longitudinal impedance of a probe cylinder to modulate the stimulus waveform, while extracellularly recording isolated primary afferents. Resistive loads only affect the amplitude of the re-afferent signals without distorting the waveform. Capacitive loads cause large waveform distortions aside from amplitude changes. Stepping from a resistive to a capacitive load in such a way that the stimulus waveform was distorted, without changing its total energy, caused strong changes in latency, inter-spike interval and number of spikes of primary afferent responses. These burst parameters are well correlated suggesting that they may contribute synergistically in driving downstream neurons. This correlation also suggests that each receptor encodes a single parameter in the stimulus waveform. The finding of waveform distortion sensitivity is relevant because it may contribute to: (a) enhance electroreceptive range in the peripheral 'electrosensory field', (b) a better identification of living prey at the 'foveal electrosensory field' and (c) detect the presence and orientation of conspecifics. Our results also suggest a revision of the classical view of amplitude and time encoding by fast and slow pathways in pulse-type electric fish.

Electric fish genomics: Progress, prospects, and new tools for neuroethology

Electric fish have served as a model system in biology since the 18th century, providing deep insight into the nature of bioelectrogenesis, the molecular structure of the synapse, and brain circuitry underlying complex behavior. Neuroethologists have collected extensive phenotypic data that span biological levels of analysis from molecules to ecosystems. This phenotypic data, together with genomic resources obtained over the past decades, have motivated new and exciting hypotheses that position the weakly electric fish model to address fundamental 21^st century biological questions. This review article considers the molecular data collected for weakly electric fish over the past three decades, and the insights that data of this nature has motivated. For readers relatively new to molecular genetics techniques, we also provide a table of terminology aimed at clarifying the numerous acronyms and techniques that accompany this field. Next, we pose a research agenda for expanding genomic resources for electric fish research over the next 10years. We conclude by considering some of the exciting research prospects for neuroethology that electric fish genomics may offer over the coming decades, if the electric fish community is successful in these endeavors.

Neural correlations enable invariant coding and perception of natural stimuli in weakly electric fish

Neural representations of behaviorally relevant stimulus features displaying invariance with respect to different contexts are essential for perception. However, the mechanisms mediating their emergence and subsequent refinement remain poorly understood in general. Here, we demonstrate that correlated neural activity allows for the emergence of an invariant representation of natural communication stimuli that is further refined across successive stages of processing in the weakly electric fish Apteronotus leptorhynchus. Importantly, different patterns of input resulting from the same natural communication stimulus occurring in different contexts all gave rise to similar behavioral responses. Our results thus reveal how a generic neural circuit performs an elegant computation that mediates the emergence and refinement of an invariant neural representation of natural stimuli that most likely constitutes a neural correlate of perception.

DOI:http://dx.doi.org/10.7554/eLife.12993.001

We can effortlessly recognize an object – a car, for example – in many different contexts such as when seen from behind, under different lighting levels or even from different viewpoints. This phenomenon is known as perceptual invariance: objects are correctly recognized, despite variations in exactly what is seen (or otherwise sensed). However, it is still not clear how the brain processes perceptual information to recognize the same object under a wide variety of contexts.

Some fish, such as the brown ghost knifefish, produce a weak electric signal that they can alter to communicate with other members of their species. A communication call may be produced in a variety of contexts that alter which aspects of the signal nearby fish detect. Despite this, fish tend to respond to a given communication call in the same way regardless of its context; this suggests that these fish also have perceptual invariance.

The communication calls of weakly electric fish can be easily mimicked in a laboratory and produce reliable behavioral responses, which makes these fish a good model for understanding how perceptual invariance might be coded in the brain. Therefore, Metzen et al. recorded the activity of the receptor neurons that first respond to communication calls in weakly electric fish. The results revealed that a given communication signal made the firing patterns of all receptor neurons in the fish’s brain more similar to each other, regardless of the signal’s context. This occurs despite the changes in context causing single receptor neurons to respond in different ways. At each stage of the process by which information is transmitted from the receptor neurons to neurons deeper in the brain, the similarity in the neurons’ firing patterns is refined, thereby giving rise to perceptual invariance.

While perceptual invariance to a given object in different contexts is desirable, it is also important to be able to distinguish between different objects. This implies that neurons should respond similarly to stimuli associated with the same object and differently to stimuli associated with different objects. Further studies are now needed to confirm whether this is the case.

DOI:http://dx.doi.org/10.7554/eLife.12993.002

Active sensing associated with spatial learning reveals memory-based attention in an electric fish

Active sensing behaviors reveal what an animal is attending to and how it changes with learning. Gymnotus sp, a gymnotiform weakly electric fish, generates an electric organ discharge (EOD) as discrete pulses to actively sense its surroundings. We monitored freely behaving gymnotid fish in a large dark "maze" and extracted their trajectories and EOD pulse pattern and rate while they learned to find food with electrically detectable landmarks as cues. After training, they more rapidly found food using shorter, more stereotyped trajectories and spent more time near the food location. We observed three forms of active sensing: sustained high EOD rates per unit distance (sampling density), transient large increases in EOD rate (E-scans) and stereotyped scanning movements (B-scans) were initially strong at landmarks and food, but, after learning, intensified only at the food location. During probe (no food) trials, after learning, the fish's search area and intense active sampling was still centered on the missing food location, but now also increased near landmarks. We hypothesize that active sensing is a behavioral manifestation of attention and essential for spatial learning; the fish use spatial memory of landmarks and path integration to reach the expected food location and confine their attention to this region.

Contrast coding in the electrosensory system: parallels with visual computation

To identify and interact with moving objects, including other members of the same species, an animal's nervous system must correctly interpret patterns of contrast in the physical signals (such as light or sound) that it receives from the environment. In weakly electric fish, the motion of objects in the environment and social interactions with other fish create complex patterns of contrast in the electric fields that they produce and detect. These contrast patterns can extend widely over space and time and represent a multitude of relevant features, as is also true for other sensory systems. Mounting evidence suggests that the computational principles underlying contrast coding in electrosensory neural networks are conserved elements of spatiotemporal processing that show strong parallels with the vertebrate visual system.