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

Combining voltage-sensitive dye, carbon fiber array, and extracellular nerve electrodes using a 3-D printed recording chamber and manipulators

BACKGROUND

The analyses of neuronal circuits require high-throughput technologies for stimulating and recording many neurons simultaneously with single-neuron precision. Voltage-sensitive dyes (VSDs) have enabled the monitoring of membrane potentials of many (10-100 s) neurons simultaneously. Carbon fiber electrode (CFE) arrays allow for stimulation and recording of many neurons simultaneously, including intracellularly.

NEW METHOD

Combining CFE with VSD leverages the advantages of both technologies, allowing for stimulation of single neurons while recording the activity of the entire network. 3-D printing technology was used to develop a chamber to simultaneously perform VSD imaging, CFE array recording, and extracellular recording from individual glass electrodes.

RESULTS

Aplysia buccal ganglia were stained with VSD and imaged while also recording using a CFE array and extracellular nerve electrodes. Coincident spiking activity was recorded by VSD, CFE, and extracellular nerve electrodes. Current injection with CFE electrodes could activate and inhibit individual neurons as detected by VSD and nerve recordings.

COMPARISON TO EXISTING METHODS

The large size of traditional manipulators limits the number of electrodes used and the number of neurons recorded during an experiment. Here we present a method to build a 3-D printed recording chamber that includes a 3-axis micromanipulator to position a CFE array and eight 2-axis manipulators to position eight extracellular electrodes.

CONCLUSIONS

3-D printing technology can be used to build a custom recording chamber and micromanipulators. Combining these technologies allows for the direct modulation of the activity of neurons while recording the activity of 100 s of neurons simultaneously.

A Single Central Pattern Generator for the Control of a Locomotor Rolling Wave in Mollusc Aplysia

Locomotion in mollusc Aplysia is implemented by a pedal rolling wave, a type of axial locomotion. Well-studied examples of axial locomotion (pedal waves in Drosophila larvae and body waves in leech, lamprey, and fish) are generated in a segmented nervous system via activation of multiple coupled central pattern generators (CPGs). Pedal waves in molluscs, however, are generated by a single pedal ganglion, and it is unknown whether there are single or multiple CPGs that generate rhythmic activity and phase shifts between different body parts. During locomotion in intact Aplysia, bursting activity in the parapedal commissural nerve (PPCN) was found to occur during tail contraction. A cluster of 20 to 30 P1 root neurons (P1Ns) on the ventral surface of the pedal ganglion, active during the pedal wave, were identified. Computational cluster analysis revealed that there are 2 phases to the motor program: phase I (centered around 168°) and phase II (centered around 357°). PPCN activity occurs during phase II. The majority of P1Ns are motoneurons. Coactive P1Ns tend to be electrically coupled. Two classes of pedal interneurons (PIs) were characterized. Class 1 (PI1 and PI2) is active during phase I. Their axons make a loop within the pedal ganglion and contribute to locomotor pattern generation. They are electrically coupled to P1Ns that fire during phase I. Class 2 (PI3) is active during phase II and innervates the contralateral pedal ganglion. PI3 may contribute to bilateral coordination. Overall, our findings support the idea that Aplysia pedal waves are generated by a single CPG.

Dimensionality reduction and recurrence analysis reveal hidden structures of striatal pathological states

A pipeline is proposed here to describe different features to study brain microcircuits on a histological scale using multi-scale analyses, including the uniform manifold approximation and projection (UMAP) dimensional reduction technique and modularity algorithm to identify neuronal ensembles, Runs tests to show significant ensembles activation, graph theory to show trajectories between ensembles, and recurrence analyses to describe how regular or chaotic ensembles dynamics are. The data set includes ex-vivo NMDA-activated striatal tissue in control conditions as well as experimental models of disease states: decorticated, dopamine depleted, and L-DOPA-induced dyskinetic rodent samples. The goal was to separate neuronal ensembles that have correlated activity patterns. The pipeline allows for the demonstration of differences between disease states in a brain slice. First, the ensembles were projected in distinctive locations in the UMAP space. Second, graphs revealed functional connectivity between neurons comprising neuronal ensembles. Third, the Runs test detected significant peaks of coactivity within neuronal ensembles. Fourth, significant peaks of coactivity were used to show activity transitions between ensembles, revealing recurrent temporal sequences between them. Fifth, recurrence analysis shows how deterministic, chaotic, or recurrent these circuits are. We found that all revealed circuits had recurrent activity except for the decorticated circuits, which tended to be divergent and chaotic. The Parkinsonian circuits exhibit fewer transitions, becoming rigid and deterministic, exhibiting a predominant temporal sequence that disrupts transitions found in the controls, thus resembling the clinical signs of rigidity and paucity of movements. Dyskinetic circuits display a higher recurrence rate between neuronal ensembles transitions, paralleling clinical findings: enhancement in involuntary movements. These findings confirm that looking at neuronal circuits at the histological scale, recording dozens of neurons simultaneously, can show clear differences between control and diseased striatal states: "fingerprints" of the disease states. Therefore, the present analysis is coherent with previous ones of striatal disease states, showing that data obtained from the tissue are robust. At the same time, it adds heuristic ways to interpret circuitry activity in different states.

Comprehensive analysis of locomotion dynamics in the protochordate Ciona intestinalis reveals how neuromodulators flexibly shape its behavioral repertoire

Vertebrate nervous systems can generate a remarkable diversity of behaviors. However, our understanding of how behaviors may have evolved in the chordate lineage is limited by the lack of neuroethological studies leveraging our closest invertebrate relatives. Here, we combine high-throughput video acquisition with pharmacological perturbations of bioamine signaling to systematically reveal the global structure of the motor behavioral repertoire in the Ciona intestinalis larvae. Most of Ciona’s postural variance can be captured by 6 basic shapes, which we term “eigencionas.” Motif analysis of postural time series revealed numerous stereotyped behavioral maneuvers including “startle-like” and “beat-and-glide.” Employing computational modeling of swimming dynamics and spatiotemporal embedding of postural features revealed that behavioral differences are generated at the levels of motor modules and the transitions between, which may in part be modulated by bioamines. Finally, we show that flexible motor module usage gives rise to diverse behaviors in response to different light stimuli.

Vertebrate nervous systems can generate a remarkable diversity of behaviors, but how did these evolve in the chordate lineage? A study of the protochordate Ciona intestinalis reveals novel insights into how a simple chordate brain uses neuromodulators to control its behavioral repertoire.

The evolution of synaptic and cognitive capacity: Insights from the nervous system transcriptome of Aplysia

The gastropod mollusk Aplysia is an important model for cellular and molecular neurobiological studies, particularly for investigations of molecular mechanisms of learning and memory. We developed an optimized assembly pipeline to generate an improved Aplysia nervous system transcriptome. This improved transcriptome enabled us to explore the evolution of cognitive capacity at the molecular level. Were there evolutionary expansions of neuronal genes between this relatively simple gastropod Aplysia (20,000 neurons) and Octopus (500 million neurons), the invertebrate with the most elaborate neuronal circuitry and greatest behavioral complexity? Are the tremendous advances in cognitive power in vertebrates explained by expansion of the synaptic proteome that resulted from multiple rounds of whole genome duplication in this clade? Overall, the complement of genes linked to neuronal function is similar between Octopus and Aplysia. As expected, a number of synaptic scaffold proteins have more isoforms in humans than in Aplysia or Octopus. However, several scaffold families present in mollusks and other protostomes are absent in vertebrates, including the Fifes, Lev10s, SOLs, and a NETO family. Thus, whereas vertebrates have more scaffold isoforms from select families, invertebrates have additional scaffold protein families not found in vertebrates. This analysis provides insights into the evolution of the synaptic proteome. Both synaptic proteins and synaptic plasticity evolved gradually, yet the last deuterostome-protostome common ancestor already possessed an elaborate suite of genes associated with synaptic function, and critical for synaptic plasticity.

Voltage imaging identifies spinal circuits that modulate locomotor adaptation in zebrafish

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

Beyond dimension reduction: Stable electric fields emerge from and allow representational drift

It is known that the exact neurons maintaining a given memory (the neural ensemble) change from trial to trial. This raises the question of how the brain achieves stability in the face of this representational drift. Here, we demonstrate that this stability emerges at the level of the electric fields that arise from neural activity. We show that electric fields carry information about working memory content. The electric fields, in turn, can act as "guard rails" that funnel higher dimensional variable neural activity along stable lower dimensional routes. We obtained the latent space associated with each memory. We then confirmed the stability of the electric field by mapping the latent space to different cortical patches (that comprise a neural ensemble) and reconstructing information flow between patches. Stable electric fields can allow latent states to be transferred between brain areas, in accord with modern engram theory.

Neuronal population activity dynamics reveal a low-dimensional signature of operant learning in Aplysia

Learning engages a high-dimensional neuronal population space spanning multiple brain regions. However, it remains unknown whether it is possible to identify a low-dimensional signature associated with operant conditioning, a ubiquitous form of learning in which animals learn from the consequences of behavior. Using single-neuron resolution voltage imaging, here we identify two low-dimensional motor modules in the neuronal population underlying Aplysia feeding. Our findings point to a temporal shift in module recruitment as the primary signature of operant learning. Our findings can help guide characterization of learning signatures in systems in which only a smaller fraction of the relevant neuronal population can be monitored.

Costa et al. use single-neuron resolution voltage imaging to identify two low-dimensional motor modules in the neuronal population underlying Aplysia feeding. Their findings point to a temporal shift in module recruitment as the primary signature of operant learning.

Identification of an allatostatin C signaling system in mollusc Aplysia

Neuropeptides, as pervasive intercellular signaling molecules in the CNS, modulate a variety of behavioral systems in both protostomes and deuterostomes. Allatostatins are neuropeptides in arthropods that inhibit the biosynthesis of juvenile hormones. Based on amino acid sequences, they are divided into three different types in arthropods: allatostatin A, allatostatin B, allatostatin C. Allatostatin C (AstC) was first isolated from Manduca sexta, and it has an important conserved feature of a disulfide bridge formed by two cysteine residues. Moreover, AstC appears to be the ortholog of mammalian somatostatin, and it has functions in common with somatostatin, such as modulating feeding behaviors. The AstC signaling system has been widely studied in arthropods, but minimally studied in molluscs. In this study, we seek to identify the AstC signaling system in the marine mollusc Aplysia californica. We cloned the AstC precursor from the cDNA of Aplysia. We predicted a 15-amino acid peptide with a disulfide bridge, i.e., AstC, using NeuroPred. We then cloned two putative allatostatin C-like receptors and through NCBI Conserved Domain Search we found that they belonged to the G protein-coupled receptor (GPCR) family. In addition, using an inositol monophosphate 1 (IP1) accumulation assay, we showed that Aplysia AstC could activate one of the putative receptors, i.e., the AstC-R, at the lowest EC50, and AstC without the disulfide bridge (AstC') activated AstC-R with the highest EC50. Moreover, four molluscan AstCs with variations of sequences from Aplysia AstC but with the disulfide bridge activated AstC-R at intermediate EC50. In summary, our successful identification of the Aplysia AstC precursor and its receptor (AstC-R) represents the first example in molluscs, and provides an important basis for further studies of the AstC signaling system in Aplysia and other molluscs.

Scalable and accurate method for neuronal ensemble detection in spiking neural networks

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.

Spectral estimation for detecting low-dimensional structure in networks using arbitrary null models

Discovering low-dimensional structure in real-world networks requires a suitable null model that defines the absence of meaningful structure. Here we introduce a spectral approach for detecting a network’s low-dimensional structure, and the nodes that participate in it, using any null model. We use generative models to estimate the expected eigenvalue distribution under a specified null model, and then detect where the data network’s eigenspectra exceed the estimated bounds. On synthetic networks, this spectral estimation approach cleanly detects transitions between random and community structure, recovers the number and membership of communities, and removes noise nodes. On real networks spectral estimation finds either a significant fraction of noise nodes or no departure from a null model, in stark contrast to traditional community detection methods. Across all analyses, we find the choice of null model can strongly alter conclusions about the presence of network structure. Our spectral estimation approach is therefore a promising basis for detecting low-dimensional structure in real-world networks, or lack thereof.

Spontaneous Activity of Neuronal Ensembles in Mouse Motor Cortex: Changes after GABAergic Blockade

The mouse motor cortex exhibits spontaneous activity in the form of temporal sequences of neuronal ensembles in vitro without the need of tissue stimulation. These neuronal ensembles are defined as groups of neurons with a strong correlation between its firing patterns, generating what appears to be a predetermined neural conduction mode that needs study. Each ensemble is commonly accompanied by one or more parvalbumin expressing neurons (PV+) or fast spiking interneurons. Many of these interneurons have functional connections between them, helping to form a circuit configuration similar to a small-world network. However, rich club metrics show that most connected neurons are neurons not expressing parvalbumin, mainly pyramidal neurons (PV-) suggesting feed-forward propagation through pyramidal cells. Ensembles with PV+ neurons are connected to these hubs. When ligand-gated fast GABAergic transmission is blocked, temporal sequences of ensembles collapse into a unique synchronous and recurrent ensemble, showing the need of inhibition for coding cortical spontaneous activity. This new ensemble has a duration and electrophysiological characteristics of brief recurrent interictal epileptiform discharges (IEDs) composed by the coactivity of both PV- and PV+ neurons, demonstrating that GABA transmission impedes its occurrence. Synchronous ensembles are clearly divided into two clusters one of them lasting longer and mainly composed by PV+ neurons. Because an ictal-like event was not recorded after several minutes of IEDs recording, it is inferred that an external stimulus and/or fast GABA transmission are necessary for its appearance, making this preparation ideal to study both the neuronal machinery to encode cortical spontaneous activity and its transformation into brief non-ictal epileptiform discharges.

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.

Movement Decomposition in the Primary Motor Cortex

A complex action can be described as the composition of a set of elementary movements. While both kinematic and dynamic elements have been proposed to compose complex actions, the structure of movement decomposition and its neural representation remain unknown. Here, we examined movement decomposition by modeling the temporal dynamics of neural populations in the primary motor cortex of macaque monkeys performing forelimb reaching movements. Using a hidden Markov model, we found that global transitions in the neural population activity are associated with a consistent segmentation of the behavioral output into acceleration and deceleration epochs with directional selectivity. Single cells exhibited modulation of firing rates between the kinematic epochs, with abrupt changes in spiking activity timed with the identified transitions. These results reveal distinct encoding of acceleration and deceleration phases at the level of M1, and point to a specific pattern of movement decomposition that arises from the underlying neural activity. A similar approach can be used to probe the structure of movement decomposition in different brain regions, possibly controlling different temporal scales, to reveal the hierarchical structure of movement composition.

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.

Critical role of protein kinase G in the long-term balance between defensive and appetitive behaviors induced by aversive stimuli in Aplysia

This study investigated the signaling cascades involved in the long-term storage of the balance between defensive and appetitive behaviors observed when the mollusk Aplysia is exposed to aversive experience. In Aplysia, repeated trials of aversive stimuli induce concurrent sensitization of defensive withdrawal reflexes and suppression of feeding for at least 24 h. This long-term storage of the balance between withdrawal reflexes and feeding is sustained, at least in part, by increased excitability of the tail sensory neurons (SNs) controlling the withdrawal reflexes, and by decreased excitability of feeding decision-making neuron B51. Nitric oxide (NO) is required for the induction of both long-term sensitization and feeding suppression. At the cellular level, NO is also required for long-term decreased B51 excitability but not for long-term increased SN excitability. Here, we characterized the signaling cascade downstream of NO contributing to the long-term storage of the balance between withdrawal reflexes and feeding. We found protein kinase G (PKG) necessary for both long-term sensitization and feeding suppression, indicating that a NO-PKG cascade governs the long-term storage of the balance between defensive and appetitive responses in Aplysia. The role of PKG on feeding suppression was paralleled at the cellular level where a cGMP-PKG pathway was required for long-term decreased B51 excitability. In the defensive circuit, the cGMP-PKG pathway was not necessary for long-term increased SN excitability, suggesting that other cellular correlates of long-term sensitization might depend on the GMP-PKG cascade to sustain the behavioral change.

Electrocorticographic dissociation of alpha and beta rhythmic activity in the human sensorimotor system

This study uses electrocorticography in humans to assess how alpha- and beta-band rhythms modulate excitability of the sensorimotor cortex during psychophysically-controlled movement imagery. Both rhythms displayed effector-specific modulations, tracked spectral markers of action potentials in the local neuronal population, and showed spatially systematic phase relationships (traveling waves). Yet, alpha- and beta-band rhythms differed in their anatomical and functional properties, were weakly correlated, and traveled along opposite directions across the sensorimotor cortex. Increased alpha-band power in the somatosensory cortex ipsilateral to the selected arm was associated with spatially-unspecific inhibition. Decreased beta-band power over contralateral motor cortex was associated with a focal shift from relative inhibition to excitation. These observations indicate the relevance of both inhibition and disinhibition mechanisms for precise spatiotemporal coordination of movement-related neuronal populations, and illustrate how those mechanisms are implemented through the substantially different neurophysiological properties of sensorimotor alpha- and beta-band rhythms.

Novel Technological Advances in Functional Connectomics in C. elegans

The complete structure and connectivity of the Caenorhabditis elegans nervous system ("mind of a worm") was first published in 1986, representing a critical milestone in the field of connectomics. The reconstruction of the nervous system (connectome) at the level of synapses provided a unique perspective of understanding how behavior can be coded within the nervous system. The following decades have seen the development of technologies that help understand how neural activity patterns are connected to behavior and modulated by sensory input. Investigations on the developmental origins of the connectome highlight the importance of role of neuronal cell lineages in the final connectivity matrix of the nervous system. Computational modeling of neuronal dynamics not only helps reconstruct the biophysical properties of individual neurons but also allows for subsequent reconstruction of whole-organism neuronal network models. Hence, combining experimental datasets with theoretical modeling of neurons generates a better understanding of organismal behavior. This review discusses some recent technological advances used to analyze and perturb whole-organism neuronal function along with developments in computational modeling, which allows for interrogation of both local and global neural circuits, leading to different behaviors. Combining these approaches will shed light into how neural networks process sensory information to generate the appropriate behavioral output, providing a complete understanding of the worm nervous system.

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.

Serial-section atlas of the Tritonia pedal ganglion

The pedal ganglion of the nudibranch gastropod Tritonia diomedea has been the focus of neurophysiological studies for more than 50 yr. These investigations have examined the neural basis of behaviors as diverse as swimming, crawling, reflex withdrawals, orientation to water flow, orientation to the earth's magnetic field, and learning. Despite this sustained research focus, most studies have confined themselves to the layer of neurons that are visible on the ganglion surface, leaving many neurons, which reside in deeper layers, largely unknown and thus unstudied. To facilitate work on such neurons, the present study used serial-section light microscopy to generate a detailed pictorial atlas of the pedal ganglion. One pedal ganglion was sectioned horizontally at 2-µm intervals and another vertically at 5-µm intervals. The resulting images were examined separately or combined into stacks to generate movie tours through the ganglion. These were also used to generate 3D reconstructions of individual neurons and rotating movies of digitally desheathed whole ganglia to reveal all surface neurons. A complete neuron count of the horizontally sectioned ganglion yielded 1,885 neurons. Real and virtual sections from the image stacks were used to reveal the morphology of individual neurons, as well as the major axon bundles traveling within the ganglion to and between its several nerves and connectives. Extensive supplemental data are provided, as well as a link to the Dryad Data Repository site, where the complete sets of high-resolution serial-section images can be downloaded. NEW & NOTEWORTHY Because of the large size and relatively low numbers of their neurons, gastropod mollusks are widely used for investigations of the neural basis of behavior. Most studies, however, focus on the neurons visible on the ganglion surface, leaving the majority, located out of sight below the surface, unexamined. The present light microscopy study generates the first detailed visual atlas of all neurons of the highly studied Tritonia pedal ganglion.

Origin and circuitry of spinal locomotor interneurons generating different speeds

The spinal circuitry governing the undulatory movements of swimming vertebrates consist of excitatory and commissural inhibitory interneurons and motor neurons. This locomotor network generates the rhythmic output, coordinate left/right alternation, and permit communication across segments. Through evolution, more complex movement patterns have emerged, made possible by sub-specialization of neural populations within the spinal cord. Walking tetrapods use a similar basic circuitry, but have added layers of complexity for the coordination of intralimbic flexor and extensor muscles as well as interlimbic coordination between the body halves and fore/hindlimbs. Although the basics of these circuits are known there is a gap in our knowledge regarding how different speeds and gaits are coordinated. Analysing subpopulations among described neuronal populations may bring insight into how changes in locomotor output are orchestrated by a hard-wired network.

Flexible microelectrode array for interfacing with the surface of neural ganglia

OBJECTIVE

The dorsal root ganglia (DRG) are promising nerve structures for sensory neural interfaces because they provide centralized access to primary afferent cell bodies and spinal reflex circuitry. In order to harness this potential, new electrode technologies are needed which take advantage of the unique properties of DRG, specifically the high density of neural cell bodies at the dorsal surface. Here we report initial in vivo results from the development of a flexible non-penetrating polyimide electrode array interfacing with the surface of ganglia.

APPROACH

Multiple layouts of a 64-channel iridium electrode (420 µm²) array were tested, with pitch as small as 25 µm. The buccal ganglia of invertebrate sea slug Aplysia californica were used to develop handling and recording techniques with ganglionic surface electrode arrays (GSEAs). We also demonstrated the GSEA's capability to record single- and multi-unit activity from feline lumbosacral DRG related to a variety of sensory inputs, including cutaneous brushing, joint flexion, and bladder pressure.

MAIN RESULTS

We recorded action potentials from a variety of Aplysia neurons activated by nerve stimulation, and units were observed firing simultaneously on closely spaced electrode sites. We also recorded single- and multi-unit activity associated with sensory inputs from feline DRG. We utilized spatial oversampling of action potentials on closely-spaced electrode sites to estimate the location of neural sources at between 25 µm and 107 µm below the DRG surface. We also used the high spatial sampling to demonstrate a possible spatial sensory map of one feline's DRG. We obtained activation of sensory fibers with low-amplitude stimulation through individual or groups of GSEA electrode sites.

SIGNIFICANCE

Overall, the GSEA has been shown to provide a variety of information types from ganglia neurons and to have significant potential as a tool for neural mapping and interfacing.

Energy Scarcity Promotes a Brain-wide Sleep State Modulated by Insulin Signaling in C. elegans

Neural information processing entails a high energetic cost, but its maintenance is crucial for animal survival. However, the brain’s energy conservation strategies are incompletely understood. Employing functional brain-wide imaging and quantitative behavioral assays, we describe a neuronal strategy in Caenorhabditis elegans that balances energy availability and expenditure. Upon acute food deprivation, animals exhibit a transiently elevated state of arousal, indicated by foraging behaviors and increased responsiveness to food-related cues. In contrast, long-term starvation suppresses these behaviors and biases animals to intermittent sleep episodes. Brain-wide neuronal population dynamics, which are likely energetically costly but important for behavior, are robust to starvation while animals are awake. However, during starvation-induced sleep, brain dynamics are systemically downregulated. Neuromodulation via insulin-like signaling is required to transiently maintain the animals’ arousal state upon acute food deprivation. Our data suggest that the regulation of sleep and wakefulness supports optimal energy allocation.

•

Starvation shifts the behavioral strategy from exploration to intermittent sleep

•

Brain-wide neuronal population dynamics are robust to starvation

•

Neuromodulation via insulin signaling maintains wakefulness during short fasting

•

The insulin receptor DAF-2 acts in a network of sensory neurons and interneurons

Neuronal Population Activity in Spinal Motor Circuits: Greater Than the Sum of Its Parts

The core elements of stereotypical movements such as locomotion, scratching and breathing are generated by networks in the lower brainstem and the spinal cord. Ensemble activities in spinal motor networks had until recently been merely a black box, but with the emergence of ultra-thin Silicon multi-electrode technology it was possible to reveal the spiking activity of larger parts of the network. A series of experiments revealed unexpected features of spinal networks, such as multiple spiking regimes and lognormal firing rate distributions. The lognormality renders the widespread idea of a typical firing rate ± standard deviation an ill-suited description, and therefore these findings define a new arithmetic of motor networks. Focusing on the population activity behind motor pattern generation this review summarizes this advance and discusses its implications.

Critical Points and Traveling Wave in Locomotion: Experimental Evidence and Some Theoretical Considerations

The central pattern generator (CPG) architecture for rhythm generation remains partly elusive. We compare cat and frog locomotion results, where the component unrelated to pattern formation appears as a temporal grid, and traveling wave respectively. Frog spinal cord microstimulation with N-methyl-D-Aspartate (NMDA), a CPG activator, produced a limited set of force directions, sometimes tonic, but more often alternating between directions similar to the tonic forces. The tonic forces were topographically organized, and sites evoking rhythms with different force subsets were located close to the constituent tonic force regions. Thus CPGs consist of topographically organized modules. Modularity was also identified as a limited set of muscle synergies whose combinations reconstructed the EMGs. The cat CPG was investigated using proprioceptive inputs during fictive locomotion. Critical points identified both as abrupt transitions in the effect of phasic perturbations, and burst shape transitions, had biomechanical correlates in intact locomotion. During tonic proprioceptive perturbations, discrete shifts between these critical points explained the burst durations changes, and amplitude changes occurred at one of these points. Besides confirming CPG modularity, these results suggest a fixed temporal grid of anchoring points, to shift modules onsets and offsets. Frog locomotion, reconstructed with the NMDA synergies, showed a partially overlapping synergy activation sequence. Using the early synergy output evoked by NMDA at different spinal sites, revealed a rostrocaudal topographic organization, where each synergy is preferentially evoked from a few, albeit overlapping, cord regions. Comparing the locomotor synergy sequence with this topography suggests that a rostrocaudal traveling wave would activate the synergies in the proper sequence for locomotion. This output was reproduced in a two-layer model using this topography and a traveling wave. Together our results suggest two CPG components: modules, i.e., synergies; and temporal patterning, seen as a temporal grid in the cat, and a traveling wave in the frog. Animal and limb navigation have similarities. Research relating grid cells to the theta rhythm and on segmentation during navigation may relate to our temporal grid and traveling wave results. Winfree's mathematical work, combining critical phases and a traveling wave, also appears important. We conclude suggesting tracing, and imaging experiments to investigate our CPG model.

Dynamical networks: Finding, measuring, and tracking neural population activity using network science

Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

Cusps enable line attractors for neural computation

Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, nonlinear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyze system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, and then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling, and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and, thus, dynamically control the processing of graded information.

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.

Unique Configurations of Compression and Truncation of Neuronal Activity Underlie l-DOPA-Induced Selection of Motor Patterns in Aplysia

A key issue in neuroscience is understanding the ways in which neuromodulators such as dopamine modify neuronal activity to mediate selection of distinct motor patterns. We addressed this issue by applying either low or high concentrations of l-DOPA (40 or 250 μM) and then monitoring activity of up to 130 neurons simultaneously in the feeding circuitry of Aplysia using a voltage-sensitive dye (RH-155). l-DOPA selected one of two distinct buccal motor patterns (BMPs): intermediate (low l-DOPA) or bite (high l-DOPA) patterns. The selection of intermediate BMPs was associated with shortening of the second phase of the BMP (retraction), whereas the selection of bite BMPs was associated with shortening of both phases of the BMP (protraction and retraction). Selection of intermediate BMPs was also associated with truncation of individual neuron spike activity (decreased burst duration but no change in spike frequency or burst latency) in neurons active during retraction. In contrast, selection of bite BMPs was associated with compression of spike activity (decreased burst latency and duration and increased spike frequency) in neurons projecting through specific nerves, as well as increased spike frequency of protraction neurons. Finally, large-scale voltage-sensitive dye recordings delineated the spatial distribution of neurons active during BMPs and the modification of that distribution by the two concentrations of l-DOPA.

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.

Neural Manifolds for the Control of Movement

The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the "neural modes." We discuss a model for neural control of movement in which the time-dependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement.

Lognormal firing rate distribution reveals prominent fluctuation-driven regime in spinal motor networks

When spinal circuits generate rhythmic movements it is important that the neuronal activity remains within stable bounds to avoid saturation and to preserve responsiveness. Here, we simultaneously record from hundreds of neurons in lumbar spinal circuits of turtles and establish the neuronal fraction that operates within either a 'mean-driven' or a 'fluctuation-driven' regime. Fluctuation-driven neurons have a 'supralinear' input-output curve, which enhances sensitivity, whereas the mean-driven regime reduces sensitivity. We find a rich diversity of firing rates across the neuronal population as reflected in a lognormal distribution and demonstrate that half of the neurons spend at least 50 % of the time in the 'fluctuation-driven' regime regardless of behavior. Because of the disparity in input-output properties for these two regimes, this fraction may reflect a fine trade-off between stability and sensitivity in order to maintain flexibility across behaviors.

Analysis of the dynamics of temporal relationships of neural activities using optical imaging data

The temporal relationship between the activities of neurons in biological neural systems is critically important for the correct delivery of the functionality of these systems. Fine measurement of temporal relationships of neural activities using micro-electrodes is possible but this approach is very limited due to spatial constraints in the context of physiologically valid settings of neural systems. Optical imaging with voltage-sensitive dyes or calcium dyes can provide data about the activity patterns of many neurons in physiologically valid settings, but the data is relatively noisy. Here we propose a numerical methodology for the analysis of optical neuro-imaging data that allows robust analysis of the dynamics of temporal relationships of neural activities. We provide a detailed description of the methodology and we also assess its robustness. The proposed methodology is applied to analyse the relationship between the activity patterns of PY neurons in the crab stomatogastric ganglion. We show for the first time in a physiologically valid setting that as expected on the basis of earlier results of single neuron recordings exposure to dopamine de-synchronises the activity of these neurons. We also discuss the wider implications and application of the proposed methodology.

Aplysia Locomotion: Network and Behavioral Actions of GdFFD, a D-Amino Acid-Containing Neuropeptide

One emerging principle is that neuromodulators, such as neuropeptides, regulate multiple behaviors, particularly motivated behaviors, e.g., feeding and locomotion. However, how neuromodulators act on multiple neural networks to exert their actions remains poorly understood. These actions depend on the chemical form of the peptide, e.g., an alternation of L- to D- form of an amino acid can endow the peptide with bioactivity, as is the case for the Aplysia peptide GdFFD (where dF indicates D-phenylalanine). GdFFD has been shown to act as an extrinsic neuromodulator in the feeding network, while the all L-amino acid form, GFFD, was not bioactive. Given that both GdFFD/GFFD are also present in pedal neurons that mediate locomotion, we sought to determine whether they impact locomotion. We first examined effects of both peptides on isolated ganglia, and monitored fictive programs using the parapedal commissural nerve (PPCN). Indeed, GdFFD was bioactive and GFFD was not. GdFFD increased the frequency with which neural activity was observed in the PPCN. In part, there was an increase in bursting spiking activity that resembled fictive locomotion. Additionally, there was significant activity between bursts. To determine how the peptide-induced activity in the isolated CNS is translated into behavior, we recorded animal movements, and developed a computer program to automatically track the animal and calculate the path of movement and velocity of locomotion. We found that GdFFD significantly reduced locomotion and induced a foot curl. These data suggest that the increase in PPCN activity observed in the isolated CNS during GdFFD application corresponds to a reduction, rather than an increase, in locomotion. In contrast, GFFD had no effect. Thus, our study suggests that GdFFD may act as an intrinsic neuromodulator in the Aplysia locomotor network. More generally, our study indicates that physiological and behavioral analyses should be combined to evaluate peptide actions.

How does the brain solve muscle redundancy? Filling the gap between optimization and muscle synergy hypotheses

The question of how the central nervous system coordinates redundant muscles has been a long-standing problem in motor neuroscience. The optimization hypothesis posits that the brain can select the muscle activation pattern that minimizes the motor effort cost from among many solutions that satisfy the requirements of the task. On the other hand, the muscle-synergy hypothesis proposes that neurally established functional groupings of muscles alleviate the computational burden associated with motor control and learning. Although the two hypotheses are not mutually exclusive, the relationship between them has not been well analyzed. This is probably because both hypotheses are formulated mathematically without a clear concept of their neural implementation. Here, we introduce a biologically plausible hypothesis ("the forgetting hypothesis") for how optimization is realized by a population of neurons. We further demonstrate that low-dimensional structure can be detected in an optimal network even if no muscle-synergies are explicitly assumed. Finally, we briefly discuss an inherent difficulty in testing the muscle-synergy hypothesis, which arises when population level optimization is assumed.

The modular and integrative functional architecture of the human brain

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules' processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author-topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network's modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules' functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain's modular yet integrated implementation of cognitive functions.

Global brain dynamics embed the motor command sequence of Caenorhabditis elegans

While isolated motor actions can be correlated with activities of neuronal networks, an unresolved problem is how the brain assembles these activities into organized behaviors like action sequences. Using brain-wide calcium imaging in Caenorhabditis elegans, we show that a large proportion of neurons across the brain share information by engaging in coordinated, dynamical network activity. This brain state evolves on a cycle, each segment of which recruits the activities of different neuronal sub-populations and can be explicitly mapped, on a single trial basis, to the animals' major motor commands. This organization defines the assembly of motor commands into a string of run-and-turn action sequence cycles, including decisions between alternative behaviors. These dynamics serve as a robust scaffold for action selection in response to sensory input. This study shows that the coordination of neuronal activity patterns into global brain dynamics underlies the high-level organization of behavior.

Unraveling a locomotor network, many neurons at a time

In this issue of Neuron, Bruno et al. (2015) use large-scale recordings in Aplysia, and apply novel dimensionality-reduction techniques to define dynamical building blocks involved in locomotor behavior. These techniques open new avenues to the study of neuronal networks.

Monitoring Spiking Activity of Many Individual Neurons in Invertebrate Ganglia

Optical recording with fast voltage sensitive dyes makes it possible, in suitable preparations, to simultaneously monitor the action potentials of large numbers of individual neurons. Here we describe methods for doing this, including considerations of different dyes and imaging systems, methods for correlating the optical signals with their source neurons, procedures for getting good signals, and the use of Independent Component Analysis for spike-sorting raw optical data into single neuron traces. These combined tools represent a powerful approach for large-scale recording of neural networks with high temporal and spatial resolution.