Behavioral Signatures of a Developing Neural Code

Integrating innate and learned behavior through brain circuits

Understanding how innate predispositions and learned experiences interact to shape behavior is a central question in systems neuroscience. Traditionally, innate behaviors, that is, those present without prior learning and governed by evolutionarily conserved neural circuits, have been studied separately from learned behaviors, which depend on experience and neural plasticity. This division has led to a compartmentalized view of behavior and neural circuit organization. Increasing evidence suggests that innate and learned behaviors are not independent, but rather deeply intertwined, with plasticity evident even in circuits classically considered 'innate'. In this opinion, we highlight examples across species that illustrate the dynamic interaction between these behavioral domains and discuss the implications for unifying theoretical and empirical frameworks. We argue that a more integrative approach, namely one that acknowledges the reciprocal influences of innate and learned processes, is essential for advancing our understanding of how neuronal activity drives complex behaviors.

A detailed quantification of larval zebrafish behavioral repertoire uncovers principles of hunting behavior

During goal-directed behavior, animals select actions from a diverse repertoire of movements. Accurately quantifying this complex and high-dimensional repertoire is essential to uncovering the underlying rules that guide the behavior. Here, we developed a low-dimensional mathematical model that accurately reproduces the complete and continuous repertoire of hunting larval zebrafish. We show that fish position and change in heading angle following a movement are coupled, such that the choice of one of them limits the other. This repertoire structure uncovered fundamental principles of movements, showing that fish rotate around an identified rotation point and then move forward or backward. Moreover, it identified a new guiding rule of the hunt: fish turn to face the prey in each movement and then move forward or backward. These results present the first low-dimensional and continuous description of movements repertoire, uncover guiding behavioral rules, and offer insights into the underlying motor control.

Millisecond-scale motor coding precedes sensorimotor learning in songbirds

A key goal of the nervous system in young animals is to learn motor skills. Songbirds learn to sing as juveniles, providing a unique opportunity to identify the neural correlates of skill acquisition. Prior studies have shown that spike rate variability in vocal motor cortex decreases substantially during song acquisition, suggesting a transition from rate-based neural control to the millisecond-precise motor codes known to underlie adult vocal performance. By distinguishing how the ensemble of spike patterns fired by cortical neurons (the "neural vocabulary") and the relationship between spike patterns and song acoustics (the "neural code") change during song acquisition, we quantified how vocal control changes across learning in juvenile Bengalese finches. We found that despite the expected drop in rate variability (a learning-related change in spike vocabulary), the precision of the neural code in the youngest singers is the same as in adults, with 1-2 ms variations in spike timing transduced into quantifiably different behaviors. In contrast, fluctuations in firing rates on longer timescales fail to affect the motor output in both juvenile and adult animals. The consistent presence of millisecond-scale motor coding during changing levels of spike rate and behavioral variability suggests that learning-related changes in cortical activity reflect the brain's changing its spiking vocabulary to better match the underlying motor code, rather than a change in the precision of the code itself.

The Visual Systems of Zebrafish

The zebrafish visual system has become a paradigmatic preparation for behavioral and systems neuroscience. Around 40 types of retinal ganglion cells (RGCs) serve as matched filters for stimulus features, including light, optic flow, prey, and objects on a collision course. RGCs distribute their signals via axon collaterals to 12 retinorecipient areas in forebrain and midbrain. The major visuomotor hub, the optic tectum, harbors nine RGC input layers that combine information on multiple features. The retinotopic map in the tectum is locally adapted to visual scene statistics and visual subfield-specific behavioral demands. Tectal projections to premotor centers are topographically organized according to behavioral commands. The known connectivity in more than 20 processing streams allows us to dissect the cellular basis of elementary perceptual and cognitive functions. Visually evoked responses, such as prey capture or loom avoidance, are controlled by dedicated multistation pathways that-at least in the larva-resemble labeled lines. This architecture serves the neuronal code's purpose of driving adaptive behavior.

A synaptic corollary discharge signal suppresses midbrain visual processing during saccade-like locomotion

In motor control, the brain not only sends motor commands to the periphery, but also generates concurrent internal signals known as corollary discharge (CD) that influence sensory information processing around the time of movement. CD signals are important for identifying sensory input arising from self-motion and to compensate for it, but the underlying mechanisms remain unclear. Using whole-cell patch clamp recordings from neurons in the zebrafish optic tectum, we discovered an inhibitory synaptic signal, temporally locked to spontaneous and visually driven locomotion. This motor-related inhibition was appropriately timed to counteract visually driven excitatory input arising from the fish's own motion, and transiently suppressed tectal spiking activity. High-resolution calcium imaging revealed localized motor-related signals in the tectal neuropil and the upstream torus longitudinalis, suggesting that CD enters the tectum via this pathway. Together, our results show how visual processing is suppressed during self-motion by motor-related phasic inhibition. This may help explain perceptual saccadic suppression observed in many species.

Whole-brain imaging of freely-moving zebrafish

One of the holy grails of neuroscience is to record the activity of every neuron in the brain while an animal moves freely and performs complex behavioral tasks. While important steps forward have been taken recently in large-scale neural recording in rodent models, single neuron resolution across the entire mammalian brain remains elusive. In contrast the larval zebrafish offers great promise in this regard. Zebrafish are a vertebrate model with substantial homology to the mammalian brain, but their transparency allows whole-brain recordings of genetically-encoded fluorescent indicators at single-neuron resolution using optical microscopy techniques. Furthermore zebrafish begin to show a complex repertoire of natural behavior from an early age, including hunting small, fast-moving prey using visual cues. Until recently work to address the neural bases of these behaviors mostly relied on assays where the fish was immobilized under the microscope objective, and stimuli such as prey were presented virtually. However significant progress has recently been made in developing brain imaging techniques for zebrafish which are not immobilized. Here we discuss recent advances, focusing particularly on techniques based on light-field microscopy. We also draw attention to several important outstanding issues which remain to be addressed to increase the ecological validity of the results obtained.

fmr1 Mutation Alters the Early Development of Sensory Coding and Hunting and Social Behaviors in Larval Zebrafish

Autism spectrum disorders (ASDs) are developmental in origin; however, little is known about how they affect the early development of behavior and sensory coding. The most common inherited form of autism is Fragile X syndrome (FXS), caused by a mutation in FMR1 Mutation of fmr1 in zebrafish causes anxiety-like behavior, hyperactivity, and hypersensitivity in auditory and visual processing. Here, we show that zebrafish fmr1-/- mutant larvae of either sex also display changes in hunting behavior, tectal coding, and social interaction. During hunting, they were less successful at catching prey and displayed altered behavioral sequences. In the tectum, representations of prey-like stimuli were more diffuse and had higher dimensionality. In a social behavioral assay, they spent more time observing a conspecific but responded more slowly to social cues. However, when given a choice of rearing environment fmr1-/- larvae preferred one with reduced visual stimulation, and rearing them in this environment reduced genotype-specific effects on tectal excitability. Together, these results shed new light on how fmr1-/- changes the early development of neural systems and behavior in a vertebrate.SIGNIFICANCE STATEMENT Autism spectrum disorders (ASDs) are caused by changes in early neural development. Animal models of ASDs offer the opportunity to study these developmental processes in greater detail than in humans. Here, we found that a zebrafish mutant for a gene which in humans causes one type of ASD showed early alterations in hunting behavior, social behavior, and how visual stimuli are represented in the brain. However, we also found that mutant fish preferred reduced visual stimulation, and rearing them in this environment reduced alterations in neural activity patterns. These results suggest interesting new directions for using zebrafish as a model to study the development of brain and behavior in ASDs, and how the impact of ASDs could potentially be reduced.

A Novel Mechanism for T1R-Independent Taste Responses to Concentrated Sugars

Recent findings from our laboratory demonstrated that the rostral nucleus of the solitary tract (rNST) retains some responsiveness to sugars in double-knock-out mice lacking either the T1R1+T1R3 (KO1+3) or T1R2+T1R3 (KO2+3) taste receptor heterodimers. Here, we extended these findings in the parabrachial nucleus (PBN) of male and female KO1+3 mice using warm stimuli to optimize sugar responses and employing additional concentrations and pharmacological agents to probe mechanisms. PBN T1R-independent sugar responses, including those to concentrated glucose, were more evident than in rNST. Similar to the NST, there were no "sugar-best" neurons in KO1+3 mice. Nevertheless, 1000 mm glucose activated nearly 55% of PBN neurons, with responses usually occurring in neurons that also displayed acid and amiloride-insensitive NaCl responses. In wild-type (WT) mice, concentrated sugars activated the same electrolyte-sensitive neurons but also "sugar-best" cells. Regardless of genotype, phlorizin, an inhibitor of the sodium-glucose co-transporter (SGLT), a component of a hypothesized alternate glucose-sensing mechanism, did not diminish responses to 1000 mm glucose. The efficacy of concentrated sugars for driving neurons broadly responsive to electrolytes implied an origin from Type III taste bud cells. To test this, we used the carbonic anhydrase (CA) inhibitor dorzolamide (DRZ), previously shown to inhibit amiloride-insensitive sodium responses arising from Type III taste bud cells. Dorzolamide had no effect on sugar-elicited responses in WT sugar-best PBN neurons but strongly suppressed them in WT and KO1+3 electrolyte-generalist neurons. These findings suggest a novel T1R-independent mechanism for hyperosmotic sugars, involving a CA-dependent mechanism in Type III taste bud cells.SIGNIFICANCE STATEMENT Since the discovery of Tas1r receptors for sugars and artificial sweeteners, evidence has accrued that mice lacking these receptors maintain some behavioral, physiological, and neural responsiveness to sugars. But the substrate(s) has remained elusive. Here, we recorded from parabrachial nucleus (PBN) taste neurons and identified T1R-independent responses to hyperosmotic sugars dependent on carbonic anhydrase (CA) and occurring primarily in neurons broadly responsive to NaCl and acid, implying an origin from Type III taste bud cells. The effectiveness of different sugars in driving these T1R-independent responses did not correlate with their efficacy in driving licking, suggesting they evoke a nonsweet sensation. Nevertheless, these salient responses are likely to comprise an adequate cue for learned preferences that occur in the absence of T1R receptors.

From perception to behavior: The neural circuits underlying prey hunting in larval zebrafish

A key challenge for neural systems is to extract relevant information from the environment and make appropriate behavioral responses. The larval zebrafish offers an exciting opportunity for studying these sensing processes and sensory-motor transformations. Prey hunting is an instinctual behavior of zebrafish that requires the brain to extract and combine different attributes of the sensory input and form appropriate motor outputs. Due to its small size and transparency the larval zebrafish brain allows optical recording of whole-brain activity to reveal the neural mechanisms involved in prey hunting and capture. In this review we discuss how the larval zebrafish brain processes visual information to identify and locate prey, the neural circuits governing the generation of motor commands in response to prey, how hunting behavior can be modulated by internal states and experience, and some outstanding questions for the field.

Topographically Localized Modulation of Tectal Cell Spatial Tuning by Complex Natural Scenes

The tuning properties of neurons in the visual system can be contextually modulated by the statistics of the area surrounding their receptive field (RF), particularly when the surround contains natural features. However, stimuli presented in specific egocentric locations may have greater behavioral relevance, raising the possibility that the extent of contextual modulation may vary with position in visual space. To explore this possibility, we utilized the small size and optical transparency of the larval zebrafish to describe the form and spatial arrangement of contextually modulated cells throughout an entire tectal hemisphere. We found that the spatial tuning of tectal neurons to a prey-like stimulus sharpens when the stimulus is presented against a background with the statistics of complex natural scenes, relative to a featureless background. These neurons are confined to a spatially restricted region of the tectum and have receptive fields centered within a region of visual space in which the presence of prey preferentially triggers hunting behavior. Our results suggest that contextual modulation of tectal neurons by complex backgrounds may facilitate prey-localization in cluttered visual environments.

From calcium imaging to graph topology

Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.

Inference of Multiplicative Factors Underlying Neural Variability in Calcium Imaging Data

Understanding brain function requires disentangling the high-dimensional activity of populations of neurons. Calcium imaging is an increasingly popular technique for monitoring such neural activity, but computational tools for interpreting extracted calcium signals are lacking. While there has been a substantial development of factor-analysis-type methods for neural spike train analysis, similar methods targeted at calcium imaging data are only beginning to emerge. Here we develop a flexible modeling framework that identifies low-dimensional latent factors in calcium imaging data with distinct additive and multiplicative modulatory effects. Our model includes spike-and-slab sparse priors that regularize additive factor activity and gaussian process priors that constrain multiplicative effects to vary only gradually, allowing for the identification of smooth and interpretable changes in multiplicative gain. These factors are estimated from the data using a variational expectation-maximization algorithm that requires a differentiable reparameterization of both continuous and discrete latent variables. After demonstrating our method on simulated data, we apply it to experimental data from the zebrafish optic tectum, uncovering low-dimensional fluctuations in multiplicative excitability that govern trial-to-trial variation in evoked responses.

The tectum/superior colliculus as the vertebrate solution for spatial sensory integration and action

The superior colliculus, or tectum in the case of non-mammalian vertebrates, is a part of the brain that registers events in the surrounding space, often through vision and hearing, but also through electrosensation, infrared detection, and other sensory modalities in diverse vertebrate lineages. This information is used to form maps of the surrounding space and the positions of different salient stimuli in relation to the individual. The sensory maps are arranged in layers with visual input in the uppermost layer, other senses in deeper positions, and a spatially aligned motor map in the deepest layer. Here, we will review the organization and intrinsic function of the tectum/superior colliculus and the information that is processed within tectal circuits. We will also discuss tectal/superior colliculus outputs that are conveyed directly to downstream motor circuits or via the thalamus to cortical areas to control various aspects of behavior. The tectum/superior colliculus is evolutionarily conserved among all vertebrates, but tailored to the sensory specialties of each lineage, and its roles have shifted with the emergence of the cerebral cortex in mammals. We will illustrate both the conserved and divergent properties of the tectum/superior colliculus through vertebrate evolution by comparing tectal processing in lampreys belonging to the oldest group of extant vertebrates, larval zebrafish, rodents, and other vertebrates including primates.

Spontaneous and evoked activity patterns diverge over development

The immature brain is highly spontaneously active. Over development this activity must be integrated with emerging patterns of stimulus-evoked activity, but little is known about how this occurs. Here we investigated this question by recording spontaneous and evoked neural activity in the larval zebrafish tectum from 4 to 15 days post-fertilisation. Correlations within spontaneous and evoked activity epochs were comparable over development, and their neural assemblies refined in similar ways. However, both the similarity between evoked and spontaneous assemblies, and also the geometric distance between spontaneous and evoked patterns, decreased over development. At all stages of development, evoked activity was of higher dimension than spontaneous activity. Thus, spontaneous and evoked activity do not converge over development in this system, and these results do not support the hypothesis that spontaneous activity evolves to form a Bayesian prior for evoked activity.

Behavior needs neural variability

Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.

Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data

The pattern of neural activity evoked by a stimulus can be substantially affected by ongoing spontaneous activity. Separating these two types of activity is particularly important for calcium imaging data given the slow temporal dynamics of calcium indicators. Here we present a statistical model that decouples stimulus-driven activity from low dimensional spontaneous activity in this case. The model identifies hidden factors giving rise to spontaneous activity while jointly estimating stimulus tuning properties that account for the confounding effects that these factors introduce. By applying our model to data from zebrafish optic tectum and mouse visual cortex, we obtain quantitative measurements of the extent that neurons in each case are driven by evoked activity, spontaneous activity, and their interaction. By not averaging away potentially important information encoded in spontaneous activity, this broadly applicable model brings new insight into population-level neural activity within single trials.