Imaging dedicated and multifunctional neural circuits generating distinct behaviors

Surprising multifunctionality of a Tritonia swim CPG neuron: C2 drives the early phase of postswim crawling despite being silent during the behavior

In response to a suitably aversive skin stimulus, the marine mollusk Tritonia diomedea launches an escape swim followed by several minutes of high-speed crawling. The two escape behaviors are highly dissimilar: whereas the swim is a muscular behavior involving alternating ventral and dorsal whole body flexions, the crawl is a nonrhythmic gliding behavior mediated by the beating of foot cilia. The serotonergic dorsal swim interneurons (DSIs) are members of the swim central pattern generator (CPG) and also strongly drive crawling. Although the swim network is very well understood, the Tritonia crawling network to date comprises only three neurons: the DSIs and pedal neurons 5 and 21 (Pd5 and Pd21). Since Tritonia's swim network has been suggested to have arisen from a preexisting crawling network, we examined the possible role that another swim CPG neuron, C2, may play in crawling. Because of its complete silence in the postswim crawling period, C2 had not previously been considered to play a role in driving crawling. However, semi-intact preparation experiments demonstrated that a brief C2 spike train surprisingly and strongly drives the foot cilia for ∼30 s, something that cannot be explained by its synaptic connections to Pd5 and Pd21. Voltage-sensitive dye (VSD) imaging in the pedal ganglion identified many candidate crawling motor neurons that fire at an elevated rate after the swim and also revealed several pedal neurons that are strongly excited by C2. It is intriguing that unlike the DSIs, which fire tonically after the swim to drive crawling, C2 does so despite its postswim silence.NEW & NOTEWORTHY Tritonia swim central pattern generator (CPG) neuron C2 surprisingly and strongly drives the early phase of postswim crawling despite being silent during this period. In decades of research, C2 had not been suspected of driving crawling because of its complete silence after the swim. Voltage-sensitive dye imaging revealed that the Tritonia crawling motor network may be much larger than previously known and also revealed that many candidate crawling neurons are excited by C2.

A tonically active master neuron modulates mutually exclusive motor states at two timescales

Continuity of behaviors requires animals to make smooth transitions between mutually exclusive behavioral states. Neural principles that govern these transitions are not well understood. Caenorhabditis elegans spontaneously switch between two opposite motor states, forward and backward movement, a phenomenon thought to reflect the reciprocal inhibition between interneurons AVB and AVA. Here, we report that spontaneous locomotion and their corresponding motor circuits are not separately controlled. AVA and AVB are neither functionally equivalent nor strictly reciprocally inhibitory. AVA, but not AVB, maintains a depolarized membrane potential. While AVA phasically inhibits the forward promoting interneuron AVB at a fast timescale, it maintains a tonic, extrasynaptic excitation on AVB over the longer timescale. We propose that AVA, with tonic and phasic activity of opposite polarities on different timescales, acts as a master neuron to break the symmetry between the underlying forward and backward motor circuits. This master neuron model offers a parsimonious solution for sustained locomotion consisted of mutually exclusive motor states.

Nested neural circuits generate distinct acoustic signals during Drosophila courtship

Many motor control systems generate multiple movements using a common set of muscles. How are premotor circuits able to flexibly generate diverse movement patterns? Here, we characterize the neuronal circuits that drive the distinct courtship songs of Drosophila melanogaster. Male flies vibrate their wings toward females to produce two different song modes-pulse and sine song-which signal species identity and male quality. Using cell-type-specific genetic reagents and the connectome, we provide a cellular and synaptic map of the circuits in the male ventral nerve cord that generate these songs and examine how activating or inhibiting each cell type within these circuits affects the song. Our data reveal that the song circuit is organized into two nested feedforward pathways with extensive reciprocal and feedback connections. The larger network produces pulse song, the more complex and ancestral song form. A subset of this network produces sine song, the simpler and more recent form. Such nested organization may be a common feature of motor control circuits in which evolution has layered increasing flexibility onto a basic movement pattern.

Modulation by Neuropeptides with Overlapping Targets Results in Functional Overlap in Oscillatory Circuit Activation

Neuromodulation lends flexibility to neural circuit operation but the general notion that different neuromodulators sculpt neural circuit activity into distinct and characteristic patterns is complicated by interindividual variability. In addition, some neuromodulators converge onto the same signaling pathways, with similar effects on neurons and synapses. We compared the effects of three neuropeptides on the rhythmic pyloric circuit in the stomatogastric ganglion of male crabs, Cancer borealis Proctolin (PROC), crustacean cardioactive peptide (CCAP), and red pigment concentrating hormone (RPCH) activate the same modulatory inward current, I MI, and have convergent actions on synapses. However, while PROC targets all four neuron types in the core pyloric circuit, CCAP and RPCH target the same subset of only two neurons. After removal of spontaneous neuromodulator release, none of the neuropeptides restored the control cycle frequency, but all restored the relative timing between neuron types. Consequently, differences between neuropeptide effects were mainly found in the spiking activity of different neuron types. We performed statistical comparisons using the Euclidean distance in the multidimensional space of normalized output attributes to obtain a single measure of difference between modulatory states. Across preparations, the circuit output in PROC was distinguishable from CCAP and RPCH, but CCAP and RPCH were not distinguishable from each other. However, we argue that even between PROC and the other two neuropeptides, population data overlapped enough to prevent reliable identification of individual output patterns as characteristic for a specific neuropeptide. We confirmed this notion by showing that blind classifications by machine learning algorithms were only moderately successful.Significance Statement It is commonly assumed that distinct behaviors or circuit activities can be elicited by different neuromodulators. Yet it is unknown to what extent these characteristic actions remain distinct across individuals. We use a well-studied circuit model of neuromodulation to examine the effects of three neuropeptides, each known to produce a distinct activity pattern in controlled studies. We find that, when compared across individuals, the three peptides elicit activity patterns that are either statistically indistinguishable or show too much overlap to be labeled characteristic. We ascribe this to interindividual variability and overlapping subcellular actions of the modulators. Because both factors are common in all neural circuits, these findings have broad significance for understanding chemical neuromodulatory actions while considering interindividual variability.

Seeing double with a multifunctional reservoir computer

Multifunctional biological neural networks exploit multistability in order to perform multiple tasks without changing any network properties. Enabling artificial neural networks (ANNs) to obtain certain multistabilities in order to perform several tasks, where each task is related to a particular attractor in the network's state space, naturally has many benefits from a machine learning perspective. Given the association to multistability, in this paper, we explore how the relationship between different attractors influences the ability of a reservoir computer (RC), which is a dynamical system in the form of an ANN, to achieve multifunctionality. We construct the "seeing double" problem in order to systematically study how a RC reconstructs a coexistence of attractors when there is an overlap between them. As the amount of overlap increases, we discover that for multifunctionality to occur, there is a critical dependence on a suitable choice of the spectral radius for the RC's internal network connections. A bifurcation analysis reveals how multifunctionality emerges and is destroyed as the RC enters a chaotic regime that can lead to chaotic itinerancy.

Natural behavior is the language of the brain

The breadth and complexity of natural behaviors inspires awe. Understanding how our perceptions, actions, and internal thoughts arise from evolved circuits in the brain has motivated neuroscientists for generations. Researchers have traditionally approached this question by focusing on stereotyped behaviors, either natural or trained, in a limited number of model species. This approach has allowed for the isolation and systematic study of specific brain operations, which has greatly advanced our understanding of the circuits involved. At the same time, the emphasis on experimental reductionism has left most aspects of the natural behaviors that have shaped the evolution of the brain largely unexplored. However, emerging technologies and analytical tools make it possible to comprehensively link natural behaviors to neural activity across a broad range of ethological contexts and timescales, heralding new modes of neuroscience focused on natural behaviors. Here we describe a three-part roadmap that aims to leverage the wealth of behaviors in their naturally occurring distributions, linking their variance with that of underlying neural processes to understand how the brain is able to successfully navigate the everyday challenges of animals' social and ecological landscapes. To achieve this aim, experimenters must harness one challenge faced by all neurobiological systems, namely variability, in order to gain new insights into the language of the brain.

Motor Rhythm Dissection From the Backward Circuit in C. elegans

Motor rhythm is initiated and sustained by oscillatory neuronal activity. We recently discovered that the A-class excitatory motor neurons (MNs) (A-MNs) function as intrinsic oscillators. They drive backward locomotion by generating rhythmic postsynaptic currents (rPSCs) in body wall muscles. Molecular underpinning of the rPSCs, however, is not fully elucidated. We report here that there are three types of the rPSC patterns, namely the phasic, tonic, and long-lasting, each with distinct kinetics and channel-dependence. The Na⁺ leak channel is required for all rPSC patterns. The tonic rPSCs exhibit strong dependence on the high-voltage-gated Ca²⁺ channels. Three K⁺ channels, the BK-type Ca²⁺-activated K⁺ channel, Na⁺-activated K⁺ channel, and voltage-gated K⁺ channel (Kv4), primarily inhibit tonic and long-lasting rPSCs with varying degrees and preferences. The elaborate regulation of rPSCs by different channels, through increasing or decreasing the rPSCs frequency and/or charge, correlates with the changes in the reversal velocity for respective channel mutants. The molecular dissection of different A-MNs-rPSC components therefore reveals different mechanisms for multiplex motor rhythm.

Contributions of h- and Na+/K+ Pump Currents to the Generation of Episodic and Continuous Rhythmic Activities

Developing spinal motor networks produce a diverse array of outputs, including episodic and continuous patterns of rhythmic activity. Variation in excitability state and neuromodulatory tone can facilitate transitions between episodic and continuous rhythms; however, the intrinsic mechanisms that govern these rhythms and their transitions are poorly understood. Here, we tested the capacity of a single central pattern generator (CPG) circuit with tunable properties to generate multiple outputs. To address this, we deployed a computational model composed of an inhibitory half-center oscillator (HCO). Following predictions of our computational model, we tested the contributions of key properties to the generation of an episodic rhythm produced by isolated spinal cords of the newborn mouse. The model recapitulates the diverse state-dependent rhythms evoked by dopamine. In the model, episodic bursting depended predominantly on the endogenous oscillatory properties of neurons, with Na⁺/K⁺ ATPase pump (I_Pump) and hyperpolarization-activated currents (I_h) playing key roles. Modulation of either I_Pump or I_h produced transitions between episodic and continuous rhythms and silence. As maximal activity of I_Pump decreased, the interepisode interval and period increased along with a reduction in episode duration. Decreasing maximal conductance of I_h decreased episode duration and increased interepisode interval. Pharmacological manipulations of I_h with ivabradine, and I_Pump with ouabain or monensin in isolated spinal cords produced findings consistent with the model. Our modeling and experimental results highlight key roles of I_h and I_Pump in producing episodic rhythms and provide insight into mechanisms that permit a single CPG to produce multiple patterns of rhythmicity.

Asymmetric and transient properties of reciprocal activity of antagonists during the paw-shake response in the cat

Mutually inhibitory populations of neurons, half-center oscillators (HCOs), are commonly involved in the dynamics of the central pattern generators (CPGs) driving various rhythmic movements. Previously, we developed a multifunctional, multistable symmetric HCO model which produced slow locomotor-like and fast paw-shake-like activity patterns. Here, we describe asymmetric features of paw-shake responses in a symmetric HCO model and test these predictions experimentally. We considered bursting properties of the two model half-centers during transient paw-shake-like responses to short perturbations during locomotor-like activity. We found that when a current pulse was applied during the spiking phase of one half-center, let’s call it #1, the consecutive burst durations (BDs) of that half-center increased throughout the paw-shake response, while BDs of the other half-center, let’s call it #2, only changed slightly. In contrast, the consecutive interburst intervals (IBIs) of half-center #1 changed little, while IBIs of half-center #2 increased. We demonstrated that this asymmetry between the half-centers depends on the phase of the locomotor-like rhythm at which the perturbation was applied. We suggest that the fast transient response reflects functional asymmetries of slow processes that underly the locomotor-like pattern; e.g., asymmetric levels of inactivation across the two half-centers for a slowly inactivating inward current. We compared model results with those of in-vivo paw-shake responses evoked in locomoting cats and found similar asymmetries. Electromyographic (EMG) BDs of anterior hindlimb muscles with flexor-related activity increased in consecutive paw-shake cycles, while BD of posterior muscles with extensor-related activity did not change, and vice versa for IBIs of anterior flexors and posterior extensors. We conclude that EMG activity patterns during paw-shaking are consistent with the proposed mechanism producing transient paw-shake-like bursting patterns found in our multistable HCO model. We suggest that the described asymmetry of paw-shaking responses could implicate a multifunctional CPG controlling both locomotion and paw-shaking.

The existence of multifunctional central pattern generators (CPGs), circuits which control more than one rhythmic motor behavior, is an intriguing hypothesis. We suggest that the cat paw-shaking response could be a transient response of the locomotor CPG. Our general prediction is that this CPG is multifunctional, and in addition to the locomotor rhythm, it can generate a transient, ten-times faster, paw-shake-like response to a stimulus. In our multistable half-center oscillator (HCO) CPG model, we applied perturbations to the locomotor pattern which resulted in a transient paw-shake-like pattern that eventually returned back to the locomotor pattern. We showed that the inactivation of the slow inward current that drives the locomotor rhythm produced asymmetry of the transient flexor and extensor activity in a symmetric HCO model. To test predictions from our model about the transient nature of the paw-shake response, we compared burst durations (BDs) and interburst intervals (IBIs) of the model half-centers in consecutive cycles of paw-shake-like responses with the BD and IBI of electromyographic (EMG) activity bursts of cat hindlimb flexors and extensors recorded during a paw-shake response. In both cases, we found similar asymmetric trends in the BD and IBI throughout a paw-shake response.

Nociceptive pain and anxiety in equines: Physiological and behavioral alterations

Pain and anxiety are two of the most important concerns in clinical veterinary medicine because they arise as consequences of multiple factors that can severely affect animal welfare. The aim of the present review was to provide a description and interpretation of the physiological and behavioral alterations associated with pain and anxiety in equines. To this end, we conducted an extensive review of diverse sources on the topic. The article begins by describing the neurophysiological pathway of pain, followed by a discussion of the importance of the limbic system in responses to pain and anxiety, since prolonged exposure to situations that cause stress and pain generates such physiological changes as tachycardia, tachypnea, hypertension, hyperthermia, and heart rate variability (HRV), often accompanied by altered emotional states, deficient rest, and even aggressiveness. In the long term, animals may show deficiencies in their ability to deal with changes in the environment due to alterations in the functioning of their immune, nervous, and endocrinologic systems. In conclusion, pain and anxiety directly impact the homeostasis of organisms, so it is necessary to conduct objective evaluations of both sensations using behavioral scales, like the horse grimace scale, complemented by assessments of blood biomarkers to analyze their correlation with physiological parameters: Heart rate, respiratory rate, HRV, theparasympathetic tone activity index, lactate and glucose levels, and temperature. Additional tools - infrared thermography, for example - can also be used in these efforts to improve the quality of life and welfare of horses.

Modeling Post-Scratching Locomotion with Two Rhythm Generators and a Shared Pattern Formation

This study aimed to present a model of post-scratching locomotion with two intermixed central pattern generator (CPG) networks, one for scratching and another for locomotion. We hypothesized that the rhythm generator layers for each CPG are different, with the condition that both CPGs share their supraspinal circuits and their motor outputs at the level of their pattern formation networks. We show that the model reproduces the post-scratching locomotion latency of 6.2 ± 3.5 s, and the mean cycle durations for scratching and post-scratching locomotion of 0.3 ± 0.09 s and 1.7 ± 0.6 s, respectively, which were observed in a previous experimental study. Our findings show how the transition of two rhythmic movements could be mediated by information exchanged between their CPG circuits through routes converging in a common pattern formation layer. This integrated organization may provide flexible and effective connectivity despite the rigidity of the anatomical connections in the spinal cord circuitry.

Symmetry kills the square in a multifunctional reservoir computer

The learning capabilities of a reservoir computer (RC) can be stifled due to symmetry in its design. Including quadratic terms in the training of a RC produces a "square readout matrix" that breaks the symmetry to quell the influence of "mirror-attractors," which are inverted copies of the RC's solutions in state space. In this paper, we prove analytically that certain symmetries in the training data forbid the square readout matrix to exist. These analytical results are explored numerically from the perspective of "multifunctionality," by training the RC to specifically reconstruct a coexistence of the Lorenz attractor and its mirror-attractor. We demonstrate that the square readout matrix emerges when the position of one attractor is slightly altered, even if there are overlapping regions between the attractors or if there is a second pair of attractors. We also find that at large spectral radius values of the RC's internal connections, the square readout matrix reappears prior to the RC crossing the edge of chaos.

Anatomy and activity patterns in a multifunctional motor neuron and its surrounding circuits

Dorsal Excitor motor neuron DE-3 in the medicinal leech plays three very different dynamical roles in three different behaviors. Without rewiring its anatomical connectivity, how can a motor neuron dynamically switch roles to play appropriate roles in various behaviors? We previously used voltage-sensitive dye imaging to record from DE-3 and most other neurons in the leech segmental ganglion during (fictive) swimming, crawling, and local-bend escape (Tomina and Wagenaar, 2017). Here, we repeated that experiment, then re-imaged the same ganglion using serial blockface electron microscopy and traced DE-3's processes. Further, we traced back the processes of DE-3's presynaptic partners to their respective somata. This allowed us to analyze the relationship between circuit anatomy and the activity patterns it sustains. We found that input synapses important for all the behaviors were widely distributed over DE-3's branches, yet that functional clusters were different during (fictive) swimming vs. crawling.

Multifunctionality in a reservoir computer

Multifunctionality is a well observed phenomenological feature of biological neural networks and considered to be of fundamental importance to the survival of certain species over time. These multifunctional neural networks are capable of performing more than one task without changing any network connections. In this paper, we investigate how this neurological idiosyncrasy can be achieved in an artificial setting with a modern machine learning paradigm known as "reservoir computing." A training technique is designed to enable a reservoir computer to perform tasks of a multifunctional nature. We explore the critical effects that changes in certain parameters can have on the reservoir computers' ability to express multifunctionality. We also expose the existence of several "untrained attractors"; attractors that dwell within the prediction state space of the reservoir computer were not part of the training. We conduct a bifurcation analysis of these untrained attractors and discuss the implications of our results.

The brain as a dynamically active organ

Nervous systems are typically described as static networks passively responding to external stimuli (i.e., the 'sensorimotor hypothesis'). However, for more than a century now, evidence has been accumulating that this passive-static perspective is wrong. Instead, evidence suggests that nervous systems dynamically change their connectivity and actively generate behavior so their owners can achieve goals in the world, some of which involve controlling their sensory feedback. This review provides a brief overview of the different historical perspectives on general brain function and details some select modern examples falsifying the sensorimotor hypothesis.

A tale of two leeches: Toward the understanding of the evolution and development of behavioral neural circuits

In the animal kingdom, behavioral traits encompass a broad spectrum of biological phenotypes that have critical roles in adaptive evolution, but an EvoDevo approach has not been broadly used to study behavior evolution. Here, we propose that, by integrating two leech model systems, each of which has already attained some success in its respective field, it is possible to take on behavioral traits with an EvoDevo approach. We first identify the developmental changes that may theoretically lead to behavioral evolution and explain why an EvoDevo study of behavior is challenging. Next, we discuss the pros and cons of the two leech model species, Hirudo, a classic model for invertebrate neurobiology, and Helobdella, an emerging model for clitellate developmental biology, as models for behavioral EvoDevo research. Given the limitations of each leech system, neither is particularly strong for behavioral EvoDevo. However, the two leech systems are complementary in their technical accessibilities, and they do exhibit some behavioral similarities and differences. By studying them in parallel and together with additional leech species such as Haementeria, it is possible to explore the different levels of behavioral development and evolution.

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates

The development of transgenic invertebrate preparations in which the activity of specifiable sets of neurons can be recorded and manipulated with light represents a revolutionary advance for studies of the neural basis of behavior. However, a downside of this development is its tendency to focus investigators on a very small number of "designer" organisms (e.g., C. elegans and Drosophila), potentially negatively impacting the pursuit of comparative studies across many species, which is needed for identifying general principles of network function. The present article illustrates how optical recording with voltage-sensitive dyes in the brains of non-transgenic gastropod species can be used to rapidly (i.e., within the time course of single experiments) reveal features of the functional organization of their neural networks with single-cell resolution. We outline in detail the dissection, staining, and recording methods used by our laboratory to obtain action potential traces from dozens to ~150 neurons during behaviorally relevant motor programs in the CNS of multiple gastropod species, including one new to neuroscience - the nudibranch Berghia stephanieae. Imaging is performed with absorbance voltage-sensitive dyes and a 464-element photodiode array that samples at 1,600 frames/second, fast enough to capture all action potentials generated by the recorded neurons. Multiple several-minute recordings can be obtained per preparation with little to no signal bleaching or phototoxicity. The raw optical data collected through the methods described can subsequently be analyzed through a variety of illustrated methods. Our optical recording approach can be readily used to probe network activity in a variety of non-transgenic species, making it well suited for comparative studies of how brains generate behavior.

Organizing principles of whole-brain functional connectivity in zebrafish larvae

Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and functionally flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchical modular structure and hubs that span module boundaries. We go on to show that spontaneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal basic organizing principles of whole-brain functional brain networks at the mesoscale. Our overarching methodological framework provides a blueprint for studying correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and mesoscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.

Identifying neural substrates of competitive interactions and sequence transitions during mechanosensory responses in Drosophila

Nervous systems have the ability to select appropriate actions and action sequences in response to sensory cues. The circuit mechanisms by which nervous systems achieve choice, stability and transitions between behaviors are still incompletely understood. To identify neurons and brain areas involved in controlling these processes, we combined a large-scale neuronal inactivation screen with automated action detection in response to a mechanosensory cue in Drosophila larva. We analyzed behaviors from 2.9x105 larvae and identified 66 candidate lines for mechanosensory responses out of which 25 for competitive interactions between actions. We further characterize in detail the neurons in these lines and analyzed their connectivity using electron microscopy. We found the neurons in the mechanosensory network are located in different regions of the nervous system consistent with a distributed model of sensorimotor decision-making. These findings provide the basis for understanding how selection and transition between behaviors are controlled by the nervous system.

A multilayer circuit architecture for the generation of distinct locomotor behaviors in Drosophila

Animals generate diverse motor behaviors, yet how the same motor neurons (MNs) generate two distinct or antagonistic behaviors remains an open question. Here, we characterize Drosophila larval muscle activity patterns and premotor/motor circuits to understand how they generate forward and backward locomotion. We show that all body wall MNs are activated during both behaviors, but a subset of MNs change recruitment timing for each behavior. We used TEM to reconstruct a full segment of all 60 MNs and 236 premotor neurons (PMNs), including differentially-recruited MNs. Analysis of this comprehensive connectome identified PMN-MN 'labeled line' connectivity; PMN-MN combinatorial connectivity; asymmetric neuronal morphology; and PMN-MN circuit motifs that could all contribute to generating distinct behaviors. We generated a recurrent network model that reproduced the observed behaviors, and used functional optogenetics to validate selected model predictions. This PMN-MN connectome will provide a foundation for analyzing the full suite of larval behaviors.

Non-synaptic Plasticity in Leech Touch Cells

The role of Na⁺/K⁺-pumps in activity-dependent synaptic plasticity has been described in both vertebrates and invertebrates. Here, we provide evidence that the Na⁺/K⁺-pump is also involved in activity-dependent non-synaptic cellular plasticity in leech sensory neurons. We show that the resting membrane potential (RMP) of T cells hyperpolarizes in response to repeated somatic current injection, while at the same time their spike count (SC) and the input resistance (IR) increase. Our Hodgkin–Huxley-type neuron model, adjusted to physiological T cell properties, suggests that repetitive action potential discharges lead to increased Na⁺/K⁺-pump activity, which then hyperpolarizes the RMP. In consequence, a slow, non-inactivating current decreases, which is presumably mediated by voltage-dependent, low-threshold potassium channels. Closing of these putative M-type channels due to hyperpolarization of the resting potential increases the IR of the cell, leading to a larger number of spikes. By this mechanism, the response behavior switches from rapidly to slowly adapting spiking. These changes in spiking behavior also effect other T cells on the same side of the ganglion, which are connected via a combination of electrical and chemical synapses. An increased SC in the presynaptic T cell results in larger postsynaptic responses (PRs) in the other T cells. However, when the number of elicited presynaptic spikes is kept constant, the PR does not change. These results suggest that T cells change their responses in an activity-dependent manner through non-synaptic rather than synaptic plasticity. These changes might act as a gain-control mechanism. Depending on the previous activity, this gain could scale the relative impacts of synaptic inputs from other mechanoreceptors, versus the spike responses to tactile skin stimulation. This multi-tasking ability, and its flexible adaptation to previous activity, might make the T cell a key player in a preparatory network, enabling the leech to perform fast behavioral reactions to skin stimulation.

Multimodal sensory information is represented by a combinatorial code in a sensorimotor system

A ubiquitous feature of the nervous system is the processing of simultaneously arriving sensory inputs from different modalities. Yet, because of the difficulties of monitoring large populations of neurons with the single resolution required to determine their sensory responses, the cellular mechanisms of how populations of neurons encode different sensory modalities often remain enigmatic. We studied multimodal information encoding in a small sensorimotor system of the crustacean stomatogastric nervous system that drives rhythmic motor activity for the processing of food. This system is experimentally advantageous, as it produces a fictive behavioral output in vitro, and distinct sensory modalities can be selectively activated. It has the additional advantage that all sensory information is routed through a hub ganglion, the commissural ganglion, a structure with fewer than 220 neurons. Using optical imaging of a population of commissural neurons to track each individual neuron's response across sensory modalities, we provide evidence that multimodal information is encoded via a combinatorial code of recruited neurons. By selectively stimulating chemosensory and mechanosensory inputs that are functionally important for processing of food, we find that these two modalities were processed in a distributed network comprising the majority of commissural neurons imaged. In a total of 12 commissural ganglia, we show that 98% of all imaged neurons were involved in sensory processing, with the two modalities being processed by a highly overlapping set of neurons. Of these, 80% were multimodal, 18% were unimodal, and only 2% of the neurons did not respond to either modality. Differences between modalities were represented by the identities of the neurons participating in each sensory condition and by differences in response sign (excitation versus inhibition), with 46% changing their responses in the other modality. Consistent with the hypothesis that the commissural network encodes different sensory conditions in the combination of activated neurons, a new combination of excitation and inhibition was found when both pathways were activated simultaneously. The responses to this bimodal condition were distinct from either unimodal condition, and for 30% of the neurons, they were not predictive from the individual unimodal responses. Thus, in a sensorimotor network, different sensory modalities are encoded using a combinatorial code of neurons that are activated or inhibited. This provides motor networks with the ability to differentially respond to categorically different sensory conditions and may serve as a model to understand higher-level processing of multimodal information.

MDN brain descending neurons coordinately activate backward and inhibit forward locomotion

Command-like descending neurons can induce many behaviors, such as backward locomotion, escape, feeding, courtship, egg-laying, or grooming (we define ‘command-like neuron’ as a neuron whose activation elicits or ‘commands’ a specific behavior). In most animals, it remains unknown how neural circuits switch between antagonistic behaviors: via top-down activation/inhibition of antagonistic circuits or via reciprocal inhibition between antagonistic circuits. Here, we use genetic screens, intersectional genetics, circuit reconstruction by electron microscopy, and functional optogenetics to identify a bilateral pair of Drosophila larval ‘mooncrawler descending neurons’ (MDNs) with command-like ability to coordinately induce backward locomotion and block forward locomotion; the former by stimulating a backward-active premotor neuron, and the latter by disynaptic inhibition of a forward-specific premotor neuron. In contrast, direct monosynaptic reciprocal inhibition between forward and backward circuits was not observed. Thus, MDNs coordinate a transition between antagonistic larval locomotor behaviors. Interestingly, larval MDNs persist into adulthood, where they can trigger backward walking. Thus, MDNs induce backward locomotion in both limbless and limbed animals.

When we choose to make one kind of movement, it often prevents us making another. We cannot move forward and backward at the same time, for example, and a horse cannot simultaneously gallop and walk. These ‘antagonistic’ behaviors often use the same group of muscles, but the muscles contract in a different order. This requires exquisite control over muscle contractions.

Neurons located in the central nervous system form circuits to produce distinct patterns of muscle contractions and to switch between these patterns. Smooth, rapid switching between behaviors is important for animal escape and survival, as well as for performing fine movements. However, we know little about how the activity of the neuronal circuits enables this.

Carreira-Rosario, Zarin, Clark et al. set out to identify the underlying neuronal circuitry that allows larval fruit flies to transition between crawling forward and backward. Results from a combination of genetics and microscopy techniques revealed that a neuron called the Mooncrawler Descending Neuron (MDN) induces a switch from forward to backward travel. MDN activates a neuron that stops the larvae crawling forward, and at the same time activates a different neuron that is only active when the larvae crawl backward. Carreira-Rosario et al. also found that MDN triggers backward crawling in the six-limbed adult fly.

Understanding how a single neuron – in this case MDN – can trigger a smooth switch between opposing behaviors could be beneficial for the medical and robotics fields. In the medical field, understanding how movement is generated could help to improve therapies that fix damage to the relevant neuronal circuits. Understanding how behavioral transitions occur may also help to design autonomous robots that can navigate complex terrain.

Bifurcations of Limit Cycles in a Reduced Model of the Xenopus Tadpole Central Pattern Generator

We present the study of a minimal microcircuit controlling locomotion in two-day-old Xenopus tadpoles. During swimming, neurons in the spinal central pattern generator (CPG) generate anti-phase oscillations between left and right half-centres. Experimental recordings show that the same CPG neurons can also generate transient bouts of long-lasting in-phase oscillations between left-right centres. These synchronous episodes are rarely recorded and have no identified behavioural purpose. However, metamorphosing tadpoles require both anti-phase and in-phase oscillations for swimming locomotion. Previous models have shown the ability to generate biologically realistic patterns of synchrony and swimming oscillations in tadpoles, but a mathematical description of how these oscillations appear is still missing. We define a simplified model that incorporates the key operating principles of tadpole locomotion. The model generates the various outputs seen in experimental recordings, including swimming and synchrony. To study the model, we perform detailed one- and two-parameter bifurcation analysis. This reveals the critical boundaries that separate different dynamical regimes and demonstrates the existence of parameter regions of bi-stable swimming and synchrony. We show that swimming is stable in a significantly larger range of parameters, and can be initiated more robustly, than synchrony. Our results can explain the appearance of long-lasting synchrony bouts seen in experiments at the start of a swimming episode.

Functional Recovery of a Locomotor Network after Injury: Plasticity beyond the Central Nervous System

Many animals depend on descending information from the brain for the initiation and proper execution of locomotion. Interestingly, after injury and the loss of such inputs, locomotor function can sometimes be regained without the regrowth of central connections. In the medicinal leech, Hirudo verbana, we have shown that crawling reemerges after removal of descending inputs. Here, we studied the mechanisms underlying this return of locomotion by asking if central pattern generators (CPGs) in crawl-recovered leeches are sufficient to produce crawl-specific intersegmental coordination. From recovered animals, we treated isolated chains of ganglia with dopamine to activate the crawl CPGs (one crawl CPG per ganglion) and observed fictive crawl-like bursting in the dorsal-longitudinal-excitor motoneuron (DE-3), an established crawl-monitor neuron. However, these preparations did not exhibit crawl-specific coordination across the CPGs. Although the crawl CPGs always generated bidirectional activation of adjacent CPGs, we never observed crawl-appropriate intersegmental phase delays. Because central circuits alone were unable to organize crawl-specific coordination, we tested the coordinating role of the peripheral nervous system. In transected leeches normally destined for recovery, we removed afferent information to the anterior-most (lead) ganglion located below the nerve-cord transection site. In these dually treated animals, overt crawling was greatly delayed or prevented. After filling the peripheral nerves with Neurobiotin tracer distal to the nerve-root lesion, we found a perfect correlation between regrowth of peripheral neuronal fibers and crawl recovery. Our study establishes that during recovery after injury, crawl-specific intersegmental coordination switches to a new dependence on afferent information.

Data-driven analysis of motor activity implicates 5-HT2A neurons in backward locomotion of larval Drosophila

Rhythmic animal behaviors are regulated in part by neural circuits called the central pattern generators (CPGs). Classifying neural population activities correlated with body movements and identifying the associated component neurons are critical steps in understanding CPGs. Previous methods that classify neural dynamics obtained by dimension reduction algorithms often require manual optimization which could be laborious and preparation-specific. Here, we present a simpler and more flexible method that is based on the pre-trained convolutional neural network model VGG-16 and unsupervised learning, and successfully classifies the fictive motor patterns in Drosophila larvae under various imaging conditions. We also used voxel-wise correlation mapping to identify neurons associated with motor patterns. By applying these methods to neurons targeted by 5-HT2A-GAL4, which we generated by the CRISPR/Cas9-system, we identified two classes of interneurons, termed Seta and Leta, which are specifically active during backward but not forward fictive locomotion. Optogenetic activation of Seta and Leta neurons increased backward locomotion. Conversely, thermogenetic inhibition of 5-HT2A-GAL4 neurons or application of a 5-HT2 antagonist decreased backward locomotion induced by noxious light stimuli. This study establishes an accelerated pipeline for activity profiling and cell identification in larval Drosophila and implicates the serotonergic system in the modulation of backward locomotion.

Control of transitions between locomotor-like and paw shake-like rhythms in a model of a multistable central pattern generator

The ability of the same neuronal circuit to control different motor functions is an actively debated concept. Previously, we showed in a model that a single multistable central pattern generator (CPG) could produce two different rhythmic motor patterns, slow and fast, corresponding to cat locomotion and paw shaking. A locomotor-like rhythm (~1 Hz) and a paw shake-like rhythm (~10 Hz) did coexist in our model, and, by applying a single pulse of current, we could switch the CPG from one regime to another (Bondy B, Klishko AN, Edwards DH, Prilutsky BI, Cymbalyuk G. In: Neuromechanical Modeling of Posture and Locomotion, 2016). Here we investigated the roles of slow intrinsic ionic currents in this multistability. The CPG is modeled as a half-center oscillator circuit comprising two reciprocally inhibitory neurons. Each neuron is equipped with two slow inward currents, a Na⁺ current ( I_NaS) and a Ca²⁺ current ( I_CaS). I_CaS inactivates much more slowly and at more hyperpolarized voltages than I_NaS. We demonstrate that I_NaS is the primary current driving the paw shake-like bursting. I_CaS is crucial for the locomotor-like bursting, and it is inactivated during the paw shake-like activity. We investigate the sensitivity of the bursting regimes to perturbations, using a pulse of current to induce a switch from one regime to the other, and we demonstrate that the transition duration is dependent on pulse amplitude and application phase. We also investigate the modulatory roles of the strength of various currents on characteristics of these rhythms and show that their effects are regime specific. We conclude that a multistable CPG is physiologically plausible and derive testable predictions of the model. NEW & NOTEWORTHY Little is known about how a single central pattern generator could produce multiple rhythms. We describe a novel mechanism for multistability of bursting regimes with strongly distinct periods. The proposed mechanism emphasizes the role of intrinsic cellular dynamics over synaptic dynamics in the production of multistability. We describe how the temporal characteristics of multiple rhythms could be controlled by neuromodulation and how single pulses of current could produce a switch between regimes in a functional fashion.

The stomatogastric nervous system of the medicinal leech: its anatomy, physiology and associated aminergic neurons

Blood feeding is an essential and signature activity of the medicinal leech species Hirudo verbana. Despite keen interest in understanding the neuronal substrates of this behavior, a major component of the nervous system associated with feeding has remained overlooked. In this study, for the first time, we report on the presence and characteristics of five stomatogastric ganglia (STGs) comprising the visceral stomatogastric nervous system (STN) of the leech. Although a brief report was published by Ruth Hanke in 1948 indicating that a ring of three ganglia (not five) was associated with the cephalic ganglia, this information was never integrated into subsequent neurobiological studies of feeding. Here, the anatomical features of the STGs are described, as are the morphological and electrophysiological characteristics of neurons originating in them. We also determined that two of the five STGs (STG-1 and STG-3) each contained two relatively large (ca. 40 µm diameter) serotonergic neurons. The STN was also enriched with dopaminergic and serotonergic arborizations; however, no intrinsic dopaminergic somata were observed. The trajectory of the serotonergic large lateral (LL) neuron, a command-like cell for feeding, was documented to project directly to the STN and not to the jaw and pharyngeal musculature as previously reported, thus reopening the important question of how the LL cell activates and coordinates biting activity with pharyngeal swallowing. Additional studies revealed that the LL cell is excited by blood serum applied to the lip and is strongly inhibited by dopamine. These findings provide a new foundation for understanding the regulation and modulation of neural networks involved in feeding.

Higher Network Activity Induced by Tactile Compared to Electrical Stimulation of Leech Mechanoreceptors

The tiny ensemble of neurons in the leech ganglion can discriminate the locations of touch stimuli on the skin as precisely as a human fingertip. The leech uses this ability to locally bend the body-wall away from the stimulus. It is assumed that a three-layered feedforward network of pressure mechanoreceptors, interneurons, and motor neurons controls this behavior. Most previous studies identified and characterized the local bend network based on electrical stimulation of a single pressure mechanoreceptor, which was sufficient to trigger the local bend response. Recent studies showed, however, that up to six mechanoreceptors of three types innervating the stimulated patch of skin carry information about both touch intensity and location simultaneously. Therefore, we hypothesized that interneurons involved in the local bend network might require the temporally concerted inputs from the population of mechanoreceptors representing tactile stimuli, to decode the tactile information and to provide appropriate synaptic inputs to the motor neurons. We examined the influence of current injection into a single mechanoreceptor on activity of postsynaptic interneurons in the network and compared it to responses of interneurons to skin stimulation with different pressure intensities. We used voltage-sensitive dye imaging to monitor the graded membrane potential changes of all visible cells on the ventral side of the ganglion. Our results showed that stimulation of a single mechanoreceptor activates several local bend interneurons, consistent with previous intracellular studies. Tactile skin stimulation, however, evoked a more pronounced, longer-lasting, stimulus intensity-dependent network dynamics involving more interneurons. We concluded that the underlying local bend network enables a non-linear processing of tactile information provided by population of mechanoreceptors. This task requires a more complex network structure than previously assumed, probably containing polysynaptic interneuron connections and feedback loops. This small, experimentally well-accessible neuronal system highlights the general importance of selecting adequate sensory stimulation to investigate the network dynamics in the context of natural behavior.

Effects of Touch Location and Intensity on Interneurons of the Leech Local Bend Network

Touch triggers highly precise behavioural responses in the leech. The underlying network of this so-called local bend reflex consists of three layers of individually characterised neurons. While the population of mechanosensory cells provide multiplexed information about the stimulus, not much is known about how interneurons process this information. Here, we analyse the responses of two local bend interneurons (cell 157 and 159) to a mechanical stimulation of the skin and show their response characteristics to naturalistic stimuli. Intracellular dye-fills combined with structural imaging revealed that these interneurons are synaptically coupled to all three types of mechanosensory cells (T, P, and N cells). Since tactile stimulation of the skin evokes spikes in one to two cells of each of the latter types, interneurons combine inputs from up to six mechanosensory cells. We find that properties of touch location and intensity can be estimated reliably and accurately based on the graded interneuron responses. Connections to several mechanosensory cell types and specific response characteristics of the interneuron types indicate specialised filter and integration properties within this small neuronal network, thus providing evidence for more complex signal processing than previously thought.

Excitatory motor neurons are local oscillators for backward locomotion

Cell- or network-driven oscillators underlie motor rhythmicity. The identity of C. elegans oscillators remains unknown. Through cell ablation, electrophysiology, and calcium imaging, we show: (1) forward and backward locomotion is driven by different oscillators; (2) the cholinergic and excitatory A-class motor neurons exhibit intrinsic and oscillatory activity that is sufficient to drive backward locomotion in the absence of premotor interneurons; (3) the UNC-2 P/Q/N high-voltage-activated calcium current underlies A motor neuron's oscillation; (4) descending premotor interneurons AVA, via an evolutionarily conserved, mixed gap junction and chemical synapse configuration, exert state-dependent inhibition and potentiation of A motor neuron's intrinsic activity to regulate backward locomotion. Thus, motor neurons themselves derive rhythms, which are dually regulated by the descending interneurons to control the reversal motor state. These and previous findings exemplify compression: essential circuit properties are conserved but executed by fewer numbers and layers of neurons in a small locomotor network.

Neuromodulatory connectivity defines the structure of a behavioral neural network

Neural networks are typically defined by their synaptic connectivity, yet synaptic wiring diagrams often provide limited insight into network function. This is due partly to the importance of non-synaptic communication by neuromodulators, which can dynamically reconfigure circuit activity to alter its output. Here, we systematically map the patterns of neuromodulatory connectivity in a network that governs a developmentally critical behavioral sequence in Drosophila. This sequence, which mediates pupal ecdysis, is governed by the serial release of several key factors, which act both somatically as hormones and within the brain as neuromodulators. By identifying and characterizing the functions of the neuronal targets of these factors, we find that they define hierarchically organized layers of the network controlling the pupal ecdysis sequence: a modular input layer, an intermediate central pattern generating layer, and a motor output layer. Mapping neuromodulatory connections in this system thus defines the functional architecture of the network.

Why do animals behave the way they do? Behavior occurs in response to signals from the environment, such as those indicating food or danger, or signals from the body, such as those indicating hunger or thirst. The nervous system detects these signals and triggers an appropriate response, such as seeking food or fleeing a threat. But because much of the nervous system takes part in generating these responses, it can make it difficult to understand how even simple behaviors come about.

One behavior that has been studied extensively is molting in insects. Molting enables insects to grow and develop, and involves casting off the outer skeleton of the previous developmental stage. To do this, the insect performs a series of repetitive movements, known as an ecdysis sequence. In the fruit fly, the pupal ecdysis sequence consists of three distinct patterns rhythmic abdominal movement. A hormone called ecdysis triggering hormone, or ETH for short, initiates this sequence by triggering the release of two further hormones, Bursicon and CCAP. All three hormones act on the nervous system to coordinate molting behavior, but exactly how they do so is unclear.

Diao et al. have now used genetic tools called Trojan exons to identify the neurons of fruit flies on which these hormones act. Trojan exons are short sequences of DNA that can be inserted into non-coding regions of a target gene to mark or manipulate the cells that express it. When a cell uses its copy of the target gene to make a protein, it also makes the product encoded by the Trojan exon. Using this technique, Diao et al. identified three sets of neurons that produce receptor proteins that recognize the molting hormones. Neurons with ETH receptors start the molting process by activating neurons that make Bursicon and CCAP. Neurons with Bursicon receptors then generate motor rhythms within the nervous system. Finally, neurons with CCAP receptors respond to these rhythms and produce the abdominal movements of the ecdysis sequence.

Many other animal behaviors depend on substances like ETH, Bursicon and CCAP, which act within the brain to change the activity of neurons and circuits. The work of Diao et al. suggests that identifying the sites at which such substances act can help reveal the circuits that govern complex behaviors.

A double-sided microscope to realize whole-ganglion imaging of membrane potential in the medicinal leech

Studies of neuronal network emergence during sensory processing and motor control are greatly facilitated by technologies that allow us to simultaneously record the membrane potential dynamics of a large population of neurons in single cell resolution. To achieve whole-brain recording with the ability to detect both small synaptic potentials and action potentials, we developed a voltage-sensitive dye (VSD) imaging technique based on a double-sided microscope that can image two sides of a nervous system simultaneously. We applied this system to the segmental ganglia of the medicinal leech. Double-sided VSD imaging enabled simultaneous recording of membrane potential events from almost all of the identifiable neurons. Using data obtained from double-sided VSD imaging, we analyzed neuronal dynamics in both sensory processing and generation of behavior and constructed functional maps for identification of neurons contributing to these processes.

In every animal, networks of nerve cells work together to interpret signals from the environment and to coordinate responses. Being able to record the activity of all the neurons in a brain at once would greatly advance our understanding of how the brain works. Yet it is not possible to do this for a human brain, which contains several billion neurons. The medicinal leech, on the other hand, has a much simpler nervous system. It has 21 brain-like units called segmental ganglia, which control how the parts of its body move, and each one contains about 400 neurons arranged on a single layer.

The activity of large populations of neurons can be monitored using a technique called fluorescent imaging. Most fluorescent dyes, however, are not sensitive enough to report low levels of activity or fast enough to track individual nerve impulses. Also, current microscopy techniques only allow one surface to be imaged at any one time. These limitations constrain the kinds of questions that neuroscientists can ask about how networks of nerve cells function.

Tomina and Wagenaar have now developed a double-sided fluorescent microscope system that allows a ganglion in a medicinal leech to be viewed from both sides at once. Using a new generation of dyes, which rapidly change their brightness as individual neurons become active or are inhibited, subtle changes in the activity of hundreds of individual neurons were monitored at the same time. In a test of the system, Tomina and Wagenaar recorded activity for different leech behaviors, like bending, swimming and crawling. For the first time, the relationships between neurons on both sides of the ganglion could be seen.

This new technique for examining the activity in neuronal circuitry will allow complex networks of neurons to be studied in more detail. The data that these images generate could then be analyzed mathematically to better understand how the brain processes information from its senses and generates behavior.

A spiral attractor network drives rhythmic locomotion

The joint activity of neural populations is high dimensional and complex. One strategy for reaching a tractable understanding of circuit function is to seek the simplest dynamical system that can account for the population activity. By imaging Aplysia's pedal ganglion during fictive locomotion, here we show that its population-wide activity arises from a low-dimensional spiral attractor. Evoking locomotion moved the population into a low-dimensional, periodic, decaying orbit - a spiral - in which it behaved as a true attractor, converging to the same orbit when evoked, and returning to that orbit after transient perturbation. We found the same attractor in every preparation, and could predict motor output directly from its orbit, yet individual neurons' participation changed across consecutive locomotion bouts. From these results, we propose that only the low-dimensional dynamics for movement control, and not the high-dimensional population activity, are consistent within and between nervous systems.

Spatial distribution of intermingling pools of projection neurons with distinct targets: A 3D analysis of the commissural ganglia in Cancer borealis

Projection neurons play a key role in carrying long-distance information between spatially distant areas of the nervous system and in controlling motor circuits. Little is known about how projection neurons with distinct anatomical targets are organized, and few studies have addressed their spatial organization at the level of individual cells. In the paired commissural ganglia (CoGs) of the stomatogastric nervous system of the crab Cancer borealis, projection neurons convey sensory, motor, and modulatory information to several distinct anatomical regions. While the functions of descending projection neurons (dPNs) which control downstream motor circuits in the stomatogastric ganglion are well characterized, their anatomical distribution as well as that of neurons projecting to the labrum, brain, and thoracic ganglion have received less attention. Using cell membrane staining, we investigated the spatial distribution of CoG projection neurons in relation to all CoG neurons. Retrograde tracing revealed that somata associated with different axonal projection pathways were not completely spatially segregated, but had distinct preferences within the ganglion. Identified dPNs had diameters larger than 70% of CoG somata and were restricted to the most medial and anterior 25% of the ganglion. They were contained within a cluster of motor neurons projecting through the same nerve to innervate the labrum, indicating that soma position was independent of function and target area. Rather, our findings suggest that CoG neurons projecting to a variety of locations follow a generalized rule: for all nerve pathway origins, the soma cluster centroids in closest proximity are those whose axons project down that pathway.

Rhythm generation, coordination, and initiation in the vocal pathways of male African clawed frogs

Central pattern generators (CPGs) in the brain stem are considered to underlie vocalizations in many vertebrate species, but the detailed mechanisms underlying how motor rhythms are generated, coordinated, and initiated remain unclear. We addressed these issues using isolated brain preparations of Xenopus laevis from which fictive vocalizations can be elicited. Advertisement calls of male X. laevis that consist of fast and slow trills are generated by vocal CPGs contained in the brain stem. Brain stem central vocal pathways consist of a premotor nucleus [dorsal tegmental area of medulla (DTAM)] and a laryngeal motor nucleus [a homologue of nucleus ambiguus (n.IX-X)] with extensive reciprocal connections between the nuclei. In addition, DTAM receives descending inputs from the extended amygdala. We found that unilateral transection of the projections between DTAM and n.IX-X eliminated premotor fictive fast trill patterns but did not affect fictive slow trills, suggesting that the fast and slow trill CPGs are distinct; the slow trill CPG is contained in n.IX-X, and the fast trill CPG spans DTAM and n.IX-X. Midline transections that eliminated the anterior, posterior, or both commissures caused no change in the temporal structure of fictive calls, but bilateral synchrony was lost, indicating that the vocal CPGs are contained in the lateral halves of the brain stem and that the commissures synchronize the two oscillators. Furthermore, the elimination of the inputs from extended amygdala to DTAM, in addition to the anterior commissure, resulted in autonomous initiation of fictive fast but not slow trills by each hemibrain stem, indicating that the extended amygdala provides a bilateral signal to initiate fast trills.

NEW & NOTEWORTHY

Central pattern generators (CPGs) are considered to underlie vocalizations in many vertebrate species, but the detailed mechanisms underlying their functions remain unclear. We addressed this question using an isolated brain preparation of African clawed frogs. We discovered that two vocal phases are mediated by anatomically distinct CPGs, that there are a pair of CPGs contained in the left and right half of the brain stem, and that mechanisms underlying initiation of the two vocal phases are distinct.

Competitive Disinhibition Mediates Behavioral Choice and Sequences in Drosophila

Even a simple sensory stimulus can elicit distinct innate behaviors and sequences. During sensorimotor decisions, competitive interactions among neurons that promote distinct behaviors must ensure the selection and maintenance of one behavior, while suppressing others. The circuit implementation of these competitive interactions is still an open question. By combining comprehensive electron microscopy reconstruction of inhibitory interneuron networks, modeling, electrophysiology, and behavioral studies, we determined the circuit mechanisms that contribute to the Drosophila larval sensorimotor decision to startle, explore, or perform a sequence of the two in response to a mechanosensory stimulus. Together, these studies reveal that, early in sensory processing, (1) reciprocally connected feedforward inhibitory interneurons implement behavioral choice, (2) local feedback disinhibition provides positive feedback that consolidates and maintains the chosen behavior, and (3) lateral disinhibition promotes sequence transitions. The combination of these interconnected circuit motifs can implement both behavior selection and the serial organization of behaviors into a sequence.

Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks

Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such as identifying a neuron/brain region across animals/subjects, become exponentially more difficult in high dimensions, such as recognizing dozens of neurons/brain regions simultaneously. We present a framework and tools for functional neurocartography-the large-scale mapping of neural activity during behavioral states. Using a voltage-sensitive dye (VSD), we imaged the multifunctional responses of hundreds of leech neurons during several behaviors to identify and functionally map homologous neurons. We extracted simple features from each of these behaviors and combined them with anatomical features to create a rich medium-dimensional feature space. This enabled us to use machine learning techniques and visualizations to characterize and account for intersubject variability, piece together a canonical atlas of neural activity, and identify two behavioral networks. We identified 39 neurons (18 pairs, 3 unpaired) as part of a canonical swim network and 17 neurons (8 pairs, 1 unpaired) involved in a partially overlapping preparatory network. All neurons in the preparatory network rapidly depolarized at the onsets of each behavior, suggesting that it is part of a dedicated rapid-response network. This network is likely mediated by the S cell, and we referenced VSD recordings to an activity atlas to identify multiple cells of interest simultaneously in real time for further experiments. We targeted and electrophysiologically verified several neurons in the swim network and further showed that the S cell is presynaptic to multiple neurons in the preparatory network. This study illustrates the basic framework to map neural activity in high dimensions with large-scale recordings and how to extract the rich information necessary to perform analyses in light of intersubject variability.

Macroscopic Neural Oscillation during Skilled Reaching Movements in Humans

The neural mechanism of skilled movements, such as reaching, has been considered to differ from that of rhythmic movement such as locomotion. It is generally thought that skilled movements are consciously controlled by the brain, while rhythmic movements are usually controlled autonomously by the spinal cord and brain stem. However, several studies in recent decades have suggested that neural networks in the spinal cord may also be involved in the generation of skilled movements. Moreover, a recent study revealed that neural activities in the motor cortex exhibit rhythmic oscillations corresponding to movement frequency during reaching movements as rhythmic movements. However, whether the oscillations are generated in the spinal cord or the cortical circuit in the motor cortex causes the oscillations is unclear. If the spinal cord is involved in the skilled movements, then similar rhythmic oscillations with time delays should be found in macroscopic neural activity. We measured whole-brain MEG signals during reaching. The MEG signals were analyzed using a dynamical analysis method. We found that rhythmic oscillations with time delays occur in all subjects during reaching movements. The results suggest that the corticospinal system is involved in the generation and control of the skilled movements as rhythmic movements.

Developmental Characterization of Tail Movements in the Appendicularian Urochordate Oikopleura dioica

Using high-speed video cinematography, we characterized kinematically the spontaneous tail movements made by the appendicularian urochordate Oikopleura dioica. Videos of young adult (1-day-old) animals discriminated 4 cardinal movement types: bending, nodding, swimming and filtering, each of which had a characteristic signature including cyclicity, event or cycle duration, cycle frequency, cycle frequency variation, laterality, tail muscle segment coordination and episode duration. Bending exhibited a more common, unilateral form (single bending) and a rarer, bilateral form (alternating bending). Videos of developing animals showed that bending and swimming appeared in rudimentary form starting just after hatching and exhibited developmental changes in movement excursion, duration and frequency, whereas nodding and filtering appeared in the fully mature form in young adults at the time of first house production. More complex behaviors were associated with inflating, entering and exiting the house. We also assessed the influence of descending inputs by separating the tail (which contains all muscles and most likely the neural circuits that generate most motor outputs) from the head. Isolated tails spontaneously generated either bending or swimming movements in abnormally protracted episodes. This together with other observations of interactions between bending and swimming behaviors indicates the presence of several types of descending inputs that regulate the activity of the pattern generating circuitry in the tail nervous system.

Optically transparent multi-suction electrode arrays

Multielectrode arrays (MEAs) allow for acquisition of multisite electrophysiological activity with submillisecond temporal resolution from neural preparations. The signal to noise ratio from such arrays has recently been improved by substrate perforations that allow negative pressure to be applied to the tissue; however, such arrays are not optically transparent, limiting their potential to be combined with optical-based technologies. We present here multi-suction electrode arrays (MSEAs) in quartz that yield a substantial increase in the detected number of units and in signal to noise ratio from mouse cortico-hippocampal slices and mouse retina explants. This enables the visualization of stronger cross correlations between the firing rates of the various sources. Additionally, the MSEA's transparency allows us to record voltage sensitive dye activity from a leech ganglion with single neuron resolution using widefield microscopy simultaneously with the electrode array recordings. The combination of enhanced electrical signals and compatibility with optical-based technologies should make the MSEA a valuable tool for investigating neuronal circuits.

Synaptic circuits and their variations within different columns in the visual system of Drosophila

We reconstructed the synaptic circuits of seven columns in the second neuropil or medulla behind the fly's compound eye. These neurons embody some of the most stereotyped circuits in one of the most miniaturized of animal brains. The reconstructions allow us, for the first time to our knowledge, to study variations between circuits in the medulla's neighboring columns. This variation in the number of synapses and the types of their synaptic partners has previously been little addressed because methods that visualize multiple circuits have not resolved detailed connections, and existing connectomic studies, which can see such connections, have not so far examined multiple reconstructions of the same circuit. Here, we address the omission by comparing the circuits common to all seven columns to assess variation in their connection strengths and the resultant rates of several different and distinct types of connection error. Error rates reveal that, overall, <1% of contacts are not part of a consensus circuit, and we classify those contacts that supplement (E+) or are missing from it (E-). Autapses, in which the same cell is both presynaptic and postsynaptic at the same synapse, are occasionally seen; two cells in particular, Dm9 and Mi1, form ≥ 20-fold more autapses than do other neurons. These results delimit the accuracy of developmental events that establish and normally maintain synaptic circuits with such precision, and thereby address the operation of such circuits. They also establish a precedent for error rates that will be required in the new science of connectomics.

Selective Gating of Neuronal Activity by Intrinsic Properties in Distinct Motor Rhythms

Many neural circuits show fast reconfiguration following altered sensory or modulatory inputs to generate stereotyped outputs. In the motor circuit of Xenopus tadpoles, I study how certain voltage-dependent ionic currents affect firing thresholds and contribute to circuit reconfiguration to generate two distinct motor patterns, swimming and struggling. Firing thresholds of excitatory interneurons [i.e., descending interneurons (dINs)] in the swimming central pattern generator are raised by depolarization due to the inactivation of Na(+) currents. In contrast, the thresholds of other types of neurons active in swimming or struggling are raised by hyperpolarization from the activation of fast transient K(+) currents. The firing thresholds are then compared with the excitatory synaptic drives, which are revealed by blocking action potentials intracellularly using QX314 during swimming and struggling. During swimming, transient K(+) currents lower neuronal excitability and gate out neurons with weak excitation, whereas their inactivation by strong excitation in other neurons increases excitability and enables fast synaptic potentials to drive reliable firing. During struggling, continuous sensory inputs lead to high levels of network excitation. This allows the inactivation of Na(+) currents and suppression of dIN activity while inactivating transient K(+) currents, recruiting neurons that are not active in swimming. Therefore, differential expression of these currents between neuron types can explain why synaptic strength does not predict firing reliability/intensity during swimming and struggling. These data show that intrinsic properties can override fast synaptic potentials, mediate circuit reconfiguration, and contribute to motor-pattern switching.

Imaging fictive locomotor patterns in larval Drosophila

We have established a preparation in larval Drosophila to monitor fictive locomotion simultaneously across abdominal and thoracic segments of the isolated CNS with genetically encoded Ca²⁺ indicators. The Ca²⁺ signals closely followed spiking activity measured electrophysiologically in nerve roots. Three motor patterns are analyzed. Two comprise waves of Ca²⁺ signals that progress along the longitudinal body axis in a posterior-to-anterior or anterior-to-posterior direction. These waves had statistically indistinguishable intersegmental phase delays compared with segmental contractions during forward and backward crawling behavior, despite being ∼10 times slower. During these waves, motor neurons of the dorsal longitudinal and transverse muscles were active in the same order as the muscle groups are recruited during crawling behavior. A third fictive motor pattern exhibits a left-right asymmetry across segments and bears similarities with turning behavior in intact larvae, occurring equally frequently and involving asymmetry in the same segments. Ablation of the segments in which forward and backward waves of Ca²⁺ signals were normally initiated did not eliminate production of Ca²⁺ waves. When the brain and subesophageal ganglion (SOG) were removed, the remaining ganglia retained the ability to produce both forward and backward waves of motor activity, although the speed and frequency of waves changed. Bilateral asymmetry of activity was reduced when the brain was removed and abolished when the SOG was removed. This work paves the way to studying the neural and genetic underpinnings of segmentally coordinated motor pattern generation in Drosophila with imaging techniques.

Modular deconstruction reveals the dynamical and physical building blocks of a locomotion motor program

The neural substrates of motor programs are only well understood for small, dedicated circuits. Here we investigate how a motor program is constructed within a large network. We imaged populations of neurons in the Aplysia pedal ganglion during execution of a locomotion motor program. We found that the program was built from a very small number of dynamical building blocks, including both neural ensembles and low-dimensional rotational dynamics. These map onto physically discrete regions of the ganglion, so that the motor program has a corresponding modular organization in both dynamical and physical space. Using this dynamic map, we identify the population potentially implementing the rhythmic pattern generator and find that its activity physically traces a looped trajectory, recapitulating its low-dimensional rotational dynamics. Our results suggest that, even in simple invertebrates, neural motor programs are implemented by large, distributed networks containing multiple dynamical systems.

Monitoring Integrated Activity of Individual Neurons Using FRET-Based Voltage-Sensitive Dyes

Pairs of membrane-associated molecules exhibiting fluorescence resonance energy transfer (FRET) provide a sensitive technique to measure changes in a cell's membrane potential. One of the FRET pair binds to one surface of the membrane and the other is a mobile ion that dissolves in the lipid bilayer. The voltage-related signal can be measured as a change in the fluorescence of either the donor or acceptor molecules, but measuring their ratio provides the largest and most noise-free signal. This technology has been used in a variety of ways; three are documented in this chapter: (1) high throughput drug screening, (2) monitoring the activity of many neurons simultaneously during a behavior, and (3) finding synaptic targets of a stimulated neuron. In addition, we provide protocols for using the dyes on both cultured neurons and leech ganglia. We also give an updated description of the mathematical basis for measuring the coherence between electrical and optical signals. Future improvements of this technique include faster and more sensitive dyes that bleach more slowly, and the expression of one of the FRET pair genetically.

Transition of pattern generation: the phenomenon of post-scratching locomotion

A fundamental problem in neurophysiology is the understanding of neuronal mechanisms by which the central nervous system produces a sequence of voluntary or involuntary motor acts from a diverse repertory of movements. These kinds of transitions between motor acts are extremely complex; however, they could be analyzed in a more simple form in decerebrate animals in the context of spinal central pattern generation. Here, we present for the first time a physiological phenomenon of post-scratching locomotion in which decerebrate cats exhibit a compulsory locomotor activity after an episode of scratching. We found flexor, extensor and intermediate single interneurons rhythmically firing in the same phase during both scratching and the subsequent post-scratching locomotion. Because no changes in phase of these neurons from scratching to post-scratching locomotion were found, we suggest that in the lumbar spinal cord there are neurons associated with both motor tasks. Moreover, because of its high reproducibility we suggest that the study of post-scratching fictive locomotion, together with the unitary recording of neurons, could become a useful tool to study neuronal mechanisms underlying transitions from one rhythmic motor task to another, and to study in more detail the central pattern generator circuitry in the spinal cord.

Control of spoken vowel acoustics and the influence of phonetic context in human speech sensorimotor cortex

Speech production requires the precise control of vocal tract movements to generate individual speech sounds (phonemes) which, in turn, are rapidly organized into complex sequences. Multiple productions of the same phoneme can exhibit substantial variability, some of which is inherent to control of the vocal tract and its biomechanics, and some of which reflects the contextual effects of surrounding phonemes ("coarticulation"). The role of the CNS in these aspects of speech motor control is not well understood. To address these issues, we recorded multielectrode cortical activity directly from human ventral sensory-motor cortex (vSMC) during the production of consonant-vowel syllables. We analyzed the relationship between the acoustic parameters of vowels (pitch and formants) and cortical activity on a single-trial level. We found that vSMC activity robustly predicted acoustic parameters across vowel categories (up to 80% of variance), as well as different renditions of the same vowel (up to 25% of variance). Furthermore, we observed significant contextual effects on vSMC representations of produced phonemes that suggest active control of coarticulation: vSMC representations for vowels were biased toward the representations of the preceding consonant, and conversely, representations for consonants were biased toward upcoming vowels. These results reveal that vSMC activity for phonemes are not invariant and provide insight into the cortical mechanisms of coarticulation.

Recent developments in VSD imaging of small neuronal networks

Voltage-sensitive dye (VSD) imaging is a powerful technique that can provide, in single experiments, a large-scale view of network activity unobtainable with traditional sharp electrode recording methods. Here we review recent work using VSDs to study small networks and highlight several results from this approach. Topics covered include circuit mapping, network multifunctionality, the network basis of decision making, and the presence of variably participating neurons in networks. Analytical tools being developed and applied to large-scale VSD imaging data sets are discussed, and the future prospects for this exciting field are considered.