Cholinergic and inhibitory synapses in a pathway from motor-axon collaterals to motoneurones

Transition to chaos separates learning regimes and relates to measure of consciousness in recurrent neural networks

Recurrent neural networks exhibit chaotic dynamics when the variance in their connection strengths exceed a critical value. Recent work indicates connection variance also modulates learning strategies; networks learn "rich" representations when initialized with low coupling and "lazier" solutions with larger variance. Using Watts-Strogatz networks of varying sparsity, structure, and hidden weight variance, we find that the critical coupling strength dividing chaotic from ordered dynamics also differentiates rich and lazy learning strategies. Training moves both stable and chaotic networks closer to the edge of chaos, with networks learning richer representations before the transition to chaos. In contrast, biologically realistic connectivity structures foster stability over a wide range of variances. The transition to chaos is also reflected in a measure that clinically discriminates levels of consciousness, the perturbational complexity index (PCIst). Networks with high values of PCIst exhibit stable dynamics and rich learning, suggesting a consciousness prior may promote rich learning. The results suggest a clear relationship between critical dynamics, learning regimes and complexity-based measures of consciousness.

Two inhibitory neuronal classes govern acquisition and recall of spinal sensorimotor adaptation

Spinal circuits are central to movement adaptation, yet the mechanisms within the spinal cord responsible for acquiring and retaining behavior upon experience remain unclear. Using a simple conditioning paradigm, we found that dorsal inhibitory neurons are indispensable for adapting protective limb-withdrawal behavior by regulating the transmission of a specific set of somatosensory information to enhance the saliency of conditioning cues associated with limb position. By contrast, maintaining previously acquired motor adaptation required the ventral inhibitory Renshaw cells. Manipulating Renshaw cells does not affect the adaptation itself but flexibly alters the expression of adaptive behavior. These findings identify a circuit basis involving two distinct populations of spinal inhibitory neurons, which enables lasting sensorimotor adaptation independently from the brain.

Prevalent co-release of glutamate and GABA throughout the mouse brain

Several neuronal populations in the brain transmit both the excitatory and inhibitory neurotransmitters, glutamate, and GABA, to downstream neurons. However, it remains largely unknown whether these opposing neurotransmitters are co-released onto the same postsynaptic neuron simultaneously or are independently transmitted at different time and locations (called co-transmission). Here, using whole-cell patch-clamp recording on acute mouse brain slices, we observed biphasic miniature postsynaptic currents, i.e., minis with time-locked excitatory and inhibitory currents, in striatal spiny projection neurons (SPNs). This observation cannot be explained by accidental coincidence of monophasic miniature excitatory and inhibitory postsynaptic currents (mEPSCs and mIPSCs, respectively), arguing for the co-release of glutamate and GABA. Interestingly, these biphasic minis could either be an mEPSC leading an mIPSC or vice versa. Although dopaminergic axons release both glutamate and GABA in the striatum, deletion of dopamine neurons did not eliminate biphasic minis, indicating that the co-release originates from another neuronal type. Importantly, we found that both types of biphasic minis were detected in other neuronal subtypes in the striatum as well as in nine out of ten additionally tested brain regions. Our results suggest that co-release of glutamate and GABA is a prevalent mode of neurotransmission in the brain.

Modeling the Role of Contour Integration in Visual Inference

Under difficult viewing conditions, the brain's visual system uses a variety of recurrent modulatory mechanisms to augment feedforward processing. One resulting phenomenon is contour integration, which occurs in the primary visual (V1) cortex and strengthens neural responses to edges if they belong to a larger smooth contour. Computational models have contributed to an understanding of the circuit mechanisms of contour integration, but less is known about its role in visual perception. To address this gap, we embedded a biologically grounded model of contour integration in a task-driven artificial neural network and trained it using a gradient-descent variant. We used this model to explore how brain-like contour integration may be optimized for high-level visual objectives as well as its potential roles in perception. When the model was trained to detect contours in a background of random edges, a task commonly used to examine contour integration in the brain, it closely mirrored the brain in terms of behavior, neural responses, and lateral connection patterns. When trained on natural images, the model enhanced weaker contours and distinguished whether two points lay on the same versus different contours. The model learned robust features that generalized well to out-of-training-distribution stimuli. Surprisingly, and in contrast with the synthetic task, a parameter-matched control network without recurrence performed the same as or better than the model on the natural-image tasks. Thus, a contour integration mechanism is not essential to perform these more naturalistic contour-related tasks. Finally, the best performance in all tasks was achieved by a modified contour integration model that did not distinguish between excitatory and inhibitory neurons.

Alterations in neurotransmitter co-release in Parkinson's disease

Parkinson's disease is a neurological disorder characterized by degeneration of midbrain dopamine neurons, which results in numerous adaptations in basal ganglia circuits. Research over the past twenty-five years has identified that midbrain dopamine neurons of the substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) co-release multiple other transmitters including glutamate and GABA, in addition to their canonical transmitter, dopamine. This review summarizes previous work characterizing neurotransmitter co-release from dopamine neurons, work examining potential changes in co-release dynamics that result in animal models of Parkinson's disease, and future opportunities for determining how dysfunction in co-release may contribute to circuit dysfunction in Parkinson's disease.

Grabbing neuropeptide signals in the brain

Bioengineered sensors resolve the dynamics of neuropeptide action.

The alpha2 nicotinic acetylcholine receptor, a subunit with unique and selective expression in inhibitory interneurons associated with principal cells

Nicotinic acetylcholine receptors (nAChRs) play crucial roles in various human disorders, with the α7, α4, α6, and α3-containing nAChR subtypes extensively studied in relation to conditions such as Alzheimer's disease, Parkinson's disease, nicotine dependence, mood disorders, and stress disorders. In contrast, the α2-nAChR subunit has received less attention due to its more restricted expression and the scarcity of specific agonists and antagonists for studying its function. Nevertheless, recent research has shed light on the unique expression pattern of the Chrna2 gene, which encodes the α2-nAChR subunit, and its involvement in distinct populations of inhibitory interneurons. This review highlights the structure, pharmacology, localization, function, and disease associations of α2-containing nAChRs and points to the unique expression pattern of the Chrna2 gene and its role in different inhibitory interneuron populations. These populations, including the oriens lacunosum moleculare (OLM) cells in the hippocampus, Martinotti cells in the neocortex, and Renshaw cells in the spinal cord, share common features and contribute to recurrent inhibitory microcircuits. Thus, the α2-nAChR subunit's unique expression pattern in specific interneuron populations and its role in recurrent inhibitory microcircuits highlight its importance in various physiological processes. Further research is necessary to uncover the comprehensive functionality of α2-containing nAChRs, delineate their specific contributions to neuronal circuits, and investigate their potential as therapeutic targets for related disorders.

Spinal Basis of Direction Control during Locomotion in Larval Zebrafish

Navigation requires steering and propulsion, but how spinal circuits contribute to direction control during ongoing locomotion is not well understood. Here, we use drifting vertical gratings to evoke directed "fictive" swimming in intact but immobilized larval zebrafish while performing electrophysiological recordings from spinal neurons. We find that directed swimming involves unilateral changes in the duration of motor output and increased recruitment of motor neurons, without impacting the timing of spiking across or along the body. Voltage-clamp recordings from motor neurons reveal increases in phasic excitation and inhibition on the side of the turn. Current-clamp recordings from premotor interneurons that provide phasic excitation or inhibition reveal two types of recruitment patterns. A direction-agnostic pattern with balanced recruitment on the turning and nonturning sides is primarily observed in excitatory V2a neurons with ipsilateral descending axons, while a direction-sensitive pattern with preferential recruitment on the turning side is dominated by V2a neurons with ipsilateral bifurcating axons. Inhibitory V1 neurons are also divided into direction-sensitive and direction-agnostic subsets, although there is no detectable morphologic distinction. Our findings support the modular control of steering and propulsion by spinal premotor circuits, where recruitment of distinct subsets of excitatory and inhibitory interneurons provide adjustments in direction while on the move.SIGNIFICANCE STATEMENT Spinal circuits play an essential role in coordinating movements during locomotion. However, it is unclear how they participate in adjustments in direction that do not interfere with coordination. Here we have developed a system using larval zebrafish that allows us to directly record electrical signals from spinal neurons during "fictive" swimming guided by visual cues. We find there are subsets of spinal interneurons for coordination and others that drive unilateral asymmetries in motor neuron recruitment for direction control. Our findings suggest a modular organization of spinal premotor circuits that enables uninterrupted adjustments in direction during ongoing locomotion.

The tracts, cytoarchitecture, and neurochemistry of the spinal cord

The human spinal cord can be described using a range of nomenclatures with each providing insight into its structure and function. Here we have comprehensively reviewed the key literature detailing the general structure, configuration of tracts, the cytoarchitecture of Rexed's laminae, and the neurochemistry at the spinal segmental level. The purpose of this review is to detail current anatomical understanding of how the spinal cord is structured and to aid researchers in identifying gaps in the literature that need to be studied to improve our knowledge of the spinal cord which in turn will improve the potential of therapeutic intervention for disorders of the spinal cord.

The Neuronal Cotransmission: Mechanistic Insights From the Autonomic Nervous System

It is now scientifically accepted that neurons have the ability to release multiple transmitter substances simultaneously, yet, cotransmission's functionality is still limited to the scientific community. Acetylcholine is released by the noradrenergic neurons, and then the acetylcholine works prejunctionally in the promotion of the noradrenaline release. This hypothesis significantly challenged the previous idea of autonomic transmission as being a simple process that had a single transmitter. Norepinephrine was thought to be the single transmitter at the sympathetic neurovascular junction according to "Dale's principle". However, more evidence of the involvement of other neurotransmitters has been shown by many researchers in conjunction with Dale's principle and established terms such as adrenergic, purinergic, and peptidergic nerves. With the discovery of cotransmission, we now understand the existence of more than one neurotransmitter at a sympathetic neurovascular junction.

Choice selective inhibition drives stability and competition in decision circuits

During perceptual decision-making, the firing rates of cortical neurons reflect upcoming choices. Recent work showed that excitatory and inhibitory neurons are equally selective for choice. However, the functional consequences of inhibitory choice selectivity in decision-making circuits are unknown. We developed a circuit model of decision-making which accounts for the specificity of inputs to and outputs from inhibitory neurons. We found that selective inhibition expands the space of circuits supporting decision-making, allowing for weaker or stronger recurrent excitation when connected in a competitive or feedback motif. The specificity of inhibitory outputs sets the trade-off between speed and accuracy of decisions by either stabilizing or destabilizing the saddle-point dynamics underlying decisions in the circuit. Recurrent neural networks trained to make decisions display the same dependence on inhibitory specificity and the strength of recurrent excitation. Our results reveal two concurrent roles for selective inhibition in decision-making circuits: stabilizing strongly connected excitatory populations and maximizing competition between oppositely selective populations.

How can artificial neural networks approximate the brain?

The article reviews the history development of artificial neural networks (ANNs), then compares the differences between ANNs and brain networks in their constituent unit, network architecture, and dynamic principle. The authors offer five points of suggestion for ANNs development and ten questions to be investigated further for the interdisciplinary field of brain simulation. Even though brain is a super-complex system with 1011 neurons, its intelligence does depend rather on the neuronal type and their energy supply mode than the number of neurons. It might be possible for ANN development to follow a new direction that is a combination of multiple modules with different architecture principle and multiple computation, rather than very large scale of neural networks with much more uniformed units and hidden layers.

Cholinergic deficiency in the cholinergic system as a pathogenetic link in the formation of various syndromes in COVID-19

According to recent data, several mechanisms of viral invasion of the central nervous system (CNS) have been proposed, one of which is both direct penetration of the virus through afferent nerve fibers and damage to the endothelium of cerebral vessels. It has been proven that the SARS-CoV-2 virus affects pathologically not only the human cardiorespiratory system but is also associated with a wide range of neurological diseases, cerebrovascular accidents, and neuromuscular pathologies. However, the observed post-COVID symptom complex in patients, manifested in the form of headache, "fog in the head," high temperature, muscle weakness, lowering blood pressure, does it make us think about the pathophysiological mechanisms that contribute to the development of this clinical picture? One possible explanation is a disruption in the signaling of the acetylcholine system (AChS) in the body. Viral invasions, and in particular COVID-19, can negatively affect the work of the AChS, disrupting its coordination activities. Therefore, the main goal of this literature review is to analyze the information and substantiate the possible mechanisms for the occurrence of post-COVID syndrome in people who have had COVID-19 from the standpoint of AChS dysfunctions.

Characteristic columnar connectivity caters to cortical computation: Replication, simulation, and evaluation of a microcircuit model

The neocortex, and with it the mammalian brain, achieves a level of computational efficiency like no other existing computational engine. A deeper understanding of its building blocks (cortical microcircuits), and their underlying computational principles is thus of paramount interest. To this end, we need reproducible computational models that can be analyzed, modified, extended and quantitatively compared. In this study, we further that aim by providing a replication of a seminal cortical column model. This model consists of noisy Hodgkin-Huxley neurons connected by dynamic synapses, whose connectivity scheme is based on empirical findings from intracellular recordings. Our analysis confirms the key original finding that the specific, data-based connectivity structure enhances the computational performance compared to a variety of alternatively structured control circuits. For this comparison, we use tasks based on spike patterns and rates that require the systems not only to have simple classification capabilities, but also to retain information over time and to be able to compute nonlinear functions. Going beyond the scope of the original study, we demonstrate that this finding is independent of the complexity of the neuron model, which further strengthens the argument that it is the connectivity which is crucial. Finally, a detailed analysis of the memory capabilities of the circuits reveals a stereotypical memory profile common across all circuit variants. Notably, the circuit with laminar structure does not retain stimulus any longer than any other circuit type. We therefore conclude that the model's computational advantage lies in a sharper representation of the stimuli.

Quadriceps muscle stimulation evokes heteronymous inhibition onto soleus with limited Ia activation compared to femoral nerve stimulation

Heteronymous excitatory feedback from muscle spindles and inhibitory feedback from Golgi tendon organs and recurrent inhibitory circuits can influence motor coordination. The functional role of inhibitory feedback is difficult to determine, because nerve stimulation, the primary method used in humans, cannot evoke inhibition without first activating the largest diameter muscle spindle axons. Here, we tested the hypothesis that quadriceps muscle stimulation could be used to examine heteronymous inhibition more selectively when compared to femoral nerve stimulation by comparing the effects of nerve and muscle stimulation onto ongoing soleus EMG held at 20% of maximal effort. Motor threshold and two higher femoral nerve and quadriceps stimulus intensities matched by twitch evoked torque magnitudes were examined. We found that significantly fewer participants exhibited excitation during quadriceps muscle stimulation when compared to nerve stimulation (14-29% vs. 64-71% of participants across stimulation intensities) and the magnitude of heteronymous excitation from muscle stimulation, when present, was much reduced compared to nerve stimulation. Muscle and nerve stimulation resulted in heteronymous inhibition that significantly increased with increasing stimulation evoked torque magnitudes. This study provides novel evidence that muscle stimulation may be used to more selectively examine inhibitory heteronymous feedback between muscles in the human lower limb when compared to nerve stimulation.

Generative Models of Brain Dynamics

This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.

Diversified physiological sensory input connectivity questions the existence of distinct classes of spinal interneurons

The spinal cord is engaged in all forms of motor performance but its functions are far from understood. Because network connectivity defines function, we explored the connectivity of muscular, tendon, and tactile sensory inputs among a wide population of spinal interneurons in the lower cervical segments. Using low noise intracellular whole cell recordings in the decerebrated, non-anesthetized cat in vivo, we could define mono-, di-, and trisynaptic inputs as well as the weights of each input. Whereas each neuron had a highly specific input, and each indirect input could moreover be explained by inputs in other recorded neurons, we unexpectedly also found the input connectivity of the spinal interneuron population to form a continuum. Our data hence contrasts with the currently widespread notion of distinct classes of interneurons. We argue that this suggested diversified physiological connectivity, which likely requires a major component of circuitry learning, implies a more flexible functionality.

Emergence of Direction-Selective Retinal Cell Types in Task-Optimized Deep Learning Models

Convolutional neural networks (CNNs), a class of deep learning models, have experienced recent success in modeling sensory cortices and retinal circuits through optimizing performance on machine learning tasks, otherwise known as task optimization. Previous research has shown task-optimized CNNs to be capable of providing explanations as to why the retina efficiently encodes natural stimuli and how certain retinal cell types are involved in efficient encoding. In our work, we sought to use task-optimized CNNs as a means of explaining computational mechanisms responsible for motion-selective retinal circuits. We designed a biologically constrained CNN and optimized its performance on a motion-classification task. We drew inspiration from psychophysics, deep learning, and systems neuroscience literature to develop a toolbox of methods to reverse engineer the computational mechanisms learned in our model. Through reverse engineering our model, we proposed a computational mechanism in which direction-selective ganglion cells and starburst amacrine cells, both experimentally observed retinal cell types, emerge in our model to discriminate among moving stimuli. This emergence suggests that direction-selective circuits in the retina are ecologically designed to robustly discriminate among moving stimuli. Our results and methods also provide a framework for how to build more interpretable deep learning models and how to understand them.

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.

Untangling Peripheral Sympathetic Neurocircuits

The sympathetic nervous system plays a critical role in regulating many autonomic functions, including cardiac rhythm. The postganglionic neurons in the sympathetic chain ganglia are essential components that relay sympathetic signals to target tissues and disruption of their activity leads to poor health outcomes. Despite this importance, the neurocircuitry within sympathetic ganglia is poorly understood. Canonically, postganglionic sympathetic neurons are thought to simply be activated by monosynaptic inputs from preganglionic cholinergic neurons of the intermediolateral cell columns of the spinal cord. Early electrophysiological studies of sympathetic ganglia where the peripheral nerve trunks were electrically stimulated identified excitatory cholinergic synaptic events in addition to retrograde action potentials, leading some to speculate that excitatory collateral projections are present. However, this seemed unlikely since sympathetic postganglionic neurons were known to synthesize and release norepinephrine and expression of dual neurochemical phenotypes had not been well recognized. In vitro studies clearly established the capacity of cultured sympathetic neurons to express and release acetylcholine and norepinephrine throughout development and even in pathophysiological conditions. Given this insight, we believe that the canonical view of ganglionic transmission needs to be reevaluated and may provide a mechanistic understanding of autonomic imbalance in disease. Further studies likely will require genetic models manipulating neurochemical phenotypes within sympathetic ganglia to resolve the function of cholinergic collateral projections between postganglionic neurons. In this perspective article, we will discuss the evidence for collateral projections in sympathetic ganglia, determine if current laboratory techniques could address these questions, and discuss potential obstacles and caveats.

Neurons learn by predicting future activity

Understanding how the brain learns may lead to machines with human-like intellectual capacities. It was previously proposed that the brain may operate on the principle of predictive coding. However, it is still not well understood how a predictive system could be implemented in the brain. Here we demonstrate that the ability of a single neuron to predict its future activity may provide an effective learning mechanism. Interestingly, this predictive learning rule can be derived from a metabolic principle, where neurons need to minimize their own synaptic activity (cost), while maximizing their impact on local blood supply by recruiting other neurons. We show how this mathematically derived learning rule can provide a theoretical connection between diverse types of brain-inspired algorithms, thus, offering a step toward development of a general theory of neuronal learning. We tested this predictive learning rule in neural network simulations and in data recorded from awake animals. Our results also suggest that spontaneous brain activity provides “training data” for neurons to learn to predict cortical dynamics. Thus, the ability of a single neuron to minimize surprise: i.e. the difference between actual and expected activity, could be an important missing element to understand computation in the brain.

Control of mammalian locomotion by ventral spinocerebellar tract neurons

Locomotion is a complex behavior required for animal survival. Vertebrate locomotion depends on spinal interneurons termed the central pattern generator (CPG), which generates activity responsible for the alternation of flexor and extensor muscles and the left and right side of the body. It is unknown whether multiple or a single neuronal type is responsible for the control of mammalian locomotion. Here, we show that ventral spinocerebellar tract neurons (VSCTs) drive generation and maintenance of locomotor behavior in neonatal and adult mice. Using mouse genetics, physiological, anatomical, and behavioral assays, we demonstrate that VSCTs exhibit rhythmogenic properties and neuronal circuit connectivity consistent with their essential role in the locomotor CPG. Importantly, optogenetic activation and chemogenetic silencing reveals that VSCTs are necessary and sufficient for locomotion. These findings identify VSCTs as critical components for mammalian locomotion and provide a paradigm shift in our understanding of neural control of complex behaviors.

Synaptic Projections of Motoneurons Within the Spinal Cord

Motoneurons have long been considered as the final common pathway of the nervous system, transmitting the neural impulses that are transduced into action.While many studies have focussed on the inputs that motoneurons receive from local circuits within the spinal cord and from other parts of the CNS, relatively few have investigated the targets of local axonal projections from motoneurons themselves, with the notable exception of those contacting Renshaw cells or other motoneurons.Recent research has not only characterised the detailed features of the excitatory connections between motoneurons and Renshaw cells but has also established that Renshaw cells are not the only target of motoneurons axons within the spinal cord. Motoneurons also form synaptic contacts with other motoneurons as well as with a subset of ventrally located V3 interneurons. These findings indicate that motoneurons cannot be simply viewed as the last relay station delivering the command drive to muscles, but perform an active role in the generation and modulation of motor patterns.

Genetic targeting of adult Renshaw cells using a Calbindin 1 destabilized Cre allele for intersection with Parvalbumin or Engrailed1

Renshaw cells (RCs) are one of the most studied spinal interneurons; however, their roles in motor control remain enigmatic in part due to the lack of experimental models to interfere with RC function, specifically in adults. To overcome this limitation, we leveraged the distinct temporal regulation of Calbindin (Calb1) expression in RCs to create genetic models for timed RC manipulation. We used a Calb1 allele expressing a destabilized Cre (dgCre) theoretically active only upon trimethoprim (TMP) administration. TMP timing and dose influenced RC targeting efficiency, which was highest within the first three postnatal weeks, but specificity was low with many other spinal neurons also targeted. In addition, dgCre showed TMP-independent activity resulting in spontaneous recombination events that accumulated with age. Combining Calb1-dgCre with Parvalbumin (Pvalb) or Engrailed1 (En1) Flpo alleles in dual conditional systems increased cellular and timing specificity. Under optimal conditions, Calb1-dgCre/Pvalb-Flpo mice targeted 90% of RCs and few dorsal horn neurons; Calb1-dgCre/En1-Flpo mice showed higher specificity, but only a maximum of 70% of RCs targeted. Both models targeted neurons throughout the brain. Restricted spinal expression was obtained by injecting intraspinally AAVs carrying dual conditional genes. These results describe the first models to genetically target RCs bypassing development.

Circuits and Synapses: Hypothesis, Observation, Controversy and Serendipity - An Opinion Piece

More than a century of dedicated research has resulted in what we now know, and what we think we know, about synapses and neural circuits. This piece asks to what extent some of the major advances - both theoretical and practical - have resulted from carefully considered theory, or experimental design: endeavors that aim to address a question, or to refute an existing hypothesis. It also, however, addresses the important part that serendipity and chance have played. There are cases where hypothesis driven research has resulted in important progress. There are also examples where a hypothesis, a model, or even an experimental approach - particularly one that seems to provide welcome simplification - has become so popular that it becomes dogma and stifles advance in other directions. The nervous system rejoices in complexity, which should neither be ignored, nor run from. The emergence of testable "rules" that can simplify our understanding of neuronal circuits has required the collection of large amounts of data that were difficult to obtain. And although those collecting these data have been criticized for not advancing hypotheses while they were "collecting butterflies," the beauty of the butterflies always enticed us toward further exploration.

Role of DSCAM in the Development of Neural Control of Movement and Locomotion

Locomotion results in an alternance of flexor and extensor muscles between left and right limbs generated by motoneurons that are controlled by the spinal interneuronal circuit. This spinal locomotor circuit is modulated by sensory afferents, which relay proprioceptive and cutaneous inputs that inform the spatial position of limbs in space and potential contacts with our environment respectively, but also by supraspinal descending commands of the brain that allow us to navigate in complex environments, avoid obstacles, chase prey, or flee predators. Although signaling pathways are important in the establishment and maintenance of motor circuits, the role of DSCAM, a cell adherence molecule associated with Down syndrome, has only recently been investigated in the context of motor control and locomotion in the rodent. DSCAM is known to be involved in lamination and delamination, synaptic targeting, axonal guidance, dendritic and cell tiling, axonal fasciculation and branching, programmed cell death, and synaptogenesis, all of which can impact the establishment of motor circuits during development, but also their maintenance through adulthood. We discuss herein how DSCAM is important for proper motor coordination, especially for breathing and locomotion.

Diverse identities and sites of action of cochlear neurotransmitters

Accurate encoding of acoustic stimuli requires temporally precise responses to sound integrated with cellular mechanisms that encode the complexity of stimuli over varying timescales and orders of magnitude of intensity. Sound in mammals is initially encoded in the cochlea, the peripheral hearing organ, which contains functionally specialized cells (including hair cells, afferent and efferent neurons, and a multitude of supporting cells) to allow faithful acoustic perception. To accomplish the demanding physiological requirements of hearing, the cochlea has developed synaptic arrangements that operate over different timescales, with varied strengths, and with the ability to adjust function in dynamic hearing conditions. Multiple neurotransmitters interact to support the precision and complexity of hearing. Here, we review the location of release, action, and function of neurotransmitters in the mammalian cochlea with an emphasis on recent work describing the complexity of signaling.

Gap junction-mediated glycinergic inhibition ensures precise temporal patterning in vocal behavior

Precise neuronal firing is especially important for behaviors highly dependent on the correct sequencing and timing of muscle activity patterns, such as acoustic signaling. Acoustic signaling is an important communication modality for vertebrates, including many teleost fishes. Toadfishes are well known to exhibit high temporal fidelity in synchronous motoneuron firing within a hindbrain network directly determining the temporal structure of natural calls. Here, we investigated how these motoneurons maintain synchronous activation. We show that pronounced temporal precision in population-level motoneuronal firing depends on gap junction-mediated, glycinergic inhibition that generates a period of reduced probability of motoneuron activation. Super-resolution microscopy confirms glycinergic release sites formed by a subset of adjacent premotoneurons contacting motoneuron somata and dendrites. In aggregate, the evidence supports the hypothesis that gap junction-mediated, glycinergic inhibition provides a timing mechanism for achieving synchrony and temporal precision in the millisecond range for rapid modulation of acoustic waveforms.

Spinal Inhibitory Interneurons: Gatekeepers of Sensorimotor Pathways

The ability to sense and move within an environment are complex functions necessary for the survival of nearly all species. The spinal cord is both the initial entry site for peripheral information and the final output site for motor response, placing spinal circuits as paramount in mediating sensory responses and coordinating movement. This is partly accomplished through the activation of complex spinal microcircuits that gate afferent signals to filter extraneous stimuli from various sensory modalities and determine which signals are transmitted to higher order structures in the CNS and to spinal motor pathways. A mechanistic understanding of how inhibitory interneurons are organized and employed within the spinal cord will provide potential access points for therapeutics targeting inhibitory deficits underlying various pathologies including sensory and movement disorders. Recent studies using transgenic manipulations, neurochemical profiling, and single-cell transcriptomics have identified distinct populations of inhibitory interneurons which express an array of genetic and/or neurochemical markers that constitute functional microcircuits. In this review, we provide an overview of identified neural components that make up inhibitory microcircuits within the dorsal and ventral spinal cord and highlight the importance of inhibitory control of sensorimotor pathways at the spinal level.

Projection Neuron Axon Collaterals in the Dorsal Horn: Placing a New Player in Spinal Cord Pain Processing

The pain experience depends on the relay of nociceptive signals from the spinal cord dorsal horn to higher brain centers. This function is ultimately achieved by the output of a small population of highly specialized neurons called projection neurons (PNs). Like output neurons in other central nervous system (CNS) regions, PNs are invested with a substantial axon collateral system that ramifies extensively within local circuits. These axon collaterals are widely distributed within and between spinal cord segments. Anatomical data on PN axon collaterals have existed since the time of Cajal, however, their function in spinal pain signaling remains unclear and is absent from current models of spinal pain processing. Despite these omissions, some insight on the potential role of PN axon collaterals can be drawn from axon collateral systems of principal or output neurons in other CNS regions, such as the hippocampus, amygdala, olfactory cortex, and ventral horn of the spinal cord. The connectivity and actions of axon collaterals in these systems have been well-defined and used to confirm crucial roles in memory, fear, olfaction, and movement control, respectively. We review this information here and propose a framework for characterizing PN axon collateral function in the dorsal horn. We highlight that experimental approaches traditionally used to delineate axon collateral function in other CNS regions are not easily applied to PNs because of their scarcity relative to spinal interneurons (INs), and the lack of cellular organization in the dorsal horn. Finally, we emphasize how the rapid development of techniques such as viral expression of optogenetic or chemogenetic probes can overcome these challenges and allow characterization of PN axon collateral function. Obtaining detailed information of this type is a necessary first step for incorporation of PN collateral system function into models of spinal sensory processing.

Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

Robust parallel decision-making in neural circuits with nonlinear inhibition

Animals frequently need to choose the best alternative from a set of possibilities, whether it is which direction to swim in or which food source to favor. How long should a network of neurons take to choose the best of N options? Theoretical results suggest that the optimal time grows as log(N), if the values of each option are imperfectly perceived. However, standard self-terminating neural network models of decision-making cannot achieve this optimal behavior. We show how using certain additional nonlinear response properties in neurons, which are ignored in standard models, results in a decision-making architecture that both achieves the optimal scaling of decision time and accounts for multiple experimentally observed features of neural decision-making.

An elemental computation in the brain is to identify the best in a set of options and report its value. It is required for inference, decision-making, optimization, action selection, consensus, and foraging. Neural computing is considered powerful because of its parallelism; however, it is unclear whether neurons can perform this max-finding operation in a way that improves upon the prohibitively slow optimal serial max-finding computation (which takes ∼N⁡log(N) time for N noisy candidate options) by a factor of N, the benchmark for parallel computation. Biologically plausible architectures for this task are winner-take-all (WTA) networks, where individual neurons inhibit each other so only those with the largest input remain active. We show that conventional WTA networks fail the parallelism benchmark and, worse, in the presence of noise, altogether fail to produce a winner when N is large. We introduce the nWTA network, in which neurons are equipped with a second nonlinearity that prevents weakly active neurons from contributing inhibition. Without parameter fine-tuning or rescaling as N varies, the nWTA network achieves the parallelism benchmark. The network reproduces experimentally observed phenomena like Hick’s law without needing an additional readout stage or adaptive N-dependent thresholds. Our work bridges scales by linking cellular nonlinearities to circuit-level decision-making, establishes that distributed computation saturating the parallelism benchmark is possible in networks of noisy, finite-memory neurons, and shows that Hick’s law may be a symptom of near-optimal parallel decision-making with noisy input.

Properties of Renshaw-like cells excited by recurrent collaterals of pudendal motoneurons in the cat

Although anatomical studies have indicated pudendal motoneurons to give off recurrent collaterals, they are not considered to make synapses onto interneurons, such as Renshaw cells, and rather terminate their own signals. No study till date has examined interneurons being driven by recurrent collaterals of pudendal motoneurons. Here, we aimed to investigate the existence of Renshaw cells driven by pudendal motoneurons along with the recurrent inhibition of the latter. Extracellular recordings were obtained from the ventral horn of the sacral spinal cord of anesthetized cats. Dorsal roots were sectioned, and motor axons were electrically stimulated. Renshaw-like cells driven by recurrent collaterals, with high-frequency firings at short latency discharge, were observed around Onuf's nucleus. However, the recurrent inhibitory post-synaptic potentials were not recorded by intracellular recordings from the pudendal motoneurons. In summary, we found Renshaw-like cells driven by pudendal motoneurons, but we could not identify the synaptic connection of these neurons.

Exciting Complexity: The Role of Motor Circuit Elements in ALS Pathophysiology

Amyotrophic lateral sclerosis (ALS) is a fatal disease, characterized by the degeneration of both upper and lower motor neurons. Despite decades of research, we still to date lack a cure or disease modifying treatment, emphasizing the need for a much-improved insight into disease mechanisms and cell type vulnerability. Altered neuronal excitability is a common phenomenon reported in ALS patients, as well as in animal models of the disease, but the cellular and circuit processes involved, as well as the causal relevance of those observations to molecular alterations and final cell death, remain poorly understood. Here, we review evidence from clinical studies, cell type-specific electrophysiology, genetic manipulations and molecular characterizations in animal models and culture experiments, which argue for a causal involvement of complex alterations of structure, function and connectivity of different neuronal subtypes within the cortical and spinal cord motor circuitries. We also summarize the current knowledge regarding the detrimental role of astrocytes and reassess the frequently proposed hypothesis of glutamate-mediated excitotoxicity with respect to changes in neuronal excitability. Together, these findings suggest multifaceted cell type-, brain area- and disease stage- specific disturbances of the excitation/inhibition balance as a cardinal aspect of ALS pathophysiology.

Motoneuronal Spinal Circuits in Degenerative Motoneuron Disease

The most evident phenotype of degenerative motoneuron disease is the loss of motor function which accompanies motoneuron death. In both amyotrophic lateral sclerosis (ALS) and spinal muscular atrophy (SMA), it is now clear that dysfunction is not restricted to motoneurons but is manifest in the spinal circuits in which motoneurons are embedded. As mounting evidence shows that motoneurons possess more elaborate and extensive connections within the spinal cord than previously realized, it is necessary to consider the role of this circuitry and its dysfunction in the disease process. In this review article, we ask if the selective vulnerability of the different motoneuron types and the relative disease resistance of distinct motoneuron groups can be understood in terms of their intraspinal connections.

Multiple Local Synaptic Modifications at Specific Sensorimotor Connections after Learning Are Associated with Behavioral Adaptations That Are Components of a Global Response Change

Learning causes local changes in synaptic connectivity and coordinated, global changes affecting many aspects of behavior. How do local synaptic changes produce global behavioral changes? In the hermaphroditic mollusc Aplysia, after learning that food is inedible, memory is expressed as bias to reject a food and to reduce responses to that food. We now show that memory is also expressed as an increased bias to reject even a nonfood object. The increased bias to rejection is partially explained by changes in synaptic connections from primary mechanoafferents to five follower neurons with well defined roles in producing different feeding behaviors. Previously, these mechanoafferents had been shown to play a role in memory consolidation. Connectivity changes differed for each follower neuron: the probability that cells were connected changed; excitation changed to inhibition and vice versa; and connection amplitude changed. Thus, multiple neural changes at different sites underlie specific aspects of a coordinated behavioral change. Changes in the connectivity between mechanoafferents and their followers cannot account for all of the behavioral changes expressed after learning, indicating that additional synaptic sites are also changed. Access to the circuit controlling feeding can help determine the logic and cellular mechanisms by which multiple local synaptic changes produce an integrated, global change in behavior.SIGNIFICANCE STATEMENT How do local changes in synapses affect global behavior? Studies on invertebrate preparations usually examine synaptic changes at specific neural sites, producing a specific behavioral change. However, memory may be expressed by multiple behavioral changes. We report that a change in behavior after learning in Aplysia is accomplished, in part, by regulating connections between mechanoafferents and their synaptic followers. For some followers, the connection probabilities change; for others, the connection signs are reversed; in others, the connection strength is modified. Thus, learning produces changes in connectivity at multiple sites, via multiple synaptic mechanisms that are consistent with the observed behavioral change.

Decentralized control of insect walking: A simple neural network explains a wide range of behavioral and neurophysiological results

Controlling the six legs of an insect walking in an unpredictable environment is a challenging task, as many degrees of freedom have to be coordinated. Solutions proposed to deal with this task are usually based on the highly influential concept that (sensory-modulated) central pattern generators (CPG) are required to control the rhythmic movements of walking legs. Here, we investigate a different view. To this end, we introduce a sensor based controller operating on artificial neurons, being applied to a (simulated) insectoid robot required to exploit the "loop through the world" allowing for simplification of neural computation. We show that such a decentralized solution leads to adaptive behavior when facing uncertain environments which we demonstrate for a broad range of behaviors never dealt with in a single system by earlier approaches. This includes the ability to produce footfall patterns such as velocity dependent "tripod", "tetrapod", "pentapod" as well as various stable intermediate patterns as observed in stick insects and in Drosophila. These patterns are found to be stable against disturbances and when starting from various leg configurations. Our neuronal architecture easily allows for starting or interrupting a walk, all being difficult for CPG controlled solutions. Furthermore, negotiation of curves and walking on a treadmill with various treatments of individual legs is possible as well as backward walking and performing short steps. This approach can as well account for the neurophysiological results usually interpreted to support the idea that CPGs form the basis of walking, although our approach is not relying on explicit CPG-like structures. Application of CPGs may however be required for very fast walking. Our neuronal structure allows to pinpoint specific neurons known from various insect studies. Interestingly, specific common properties observed in both insects and crustaceans suggest a significance of our controller beyond the realm of insects.

Octopamine neuron dependent aggression requires dVGLUT from dual-transmitting neurons

Neuromodulators such as monoamines are often expressed in neurons that also release at least one fast-acting neurotransmitter. The release of a combination of transmitters provides both "classical" and "modulatory" signals that could produce diverse and/or complementary effects in associated circuits. Here, we establish that the majority of Drosophila octopamine (OA) neurons are also glutamatergic and identify the individual contributions of each neurotransmitter on sex-specific behaviors. Males without OA display low levels of aggression and high levels of inter-male courtship. Males deficient for dVGLUT solely in OA-glutamate neurons (OGNs) also exhibit a reduction in aggression, but without a concurrent increase in inter-male courtship. Within OGNs, a portion of VMAT and dVGLUT puncta differ in localization suggesting spatial differences in OA signaling. Our findings establish a previously undetermined role for dVGLUT in OA neurons and suggests that glutamate uncouples aggression from OA-dependent courtship-related behavior. These results indicate that dual neurotransmission can increase the efficacy of individual neurotransmitters while maintaining unique functions within a multi-functional social behavior neuronal network.

Recovery cycles of posterior root-muscle reflexes evoked by transcutaneous spinal cord stimulation and of the H reflex in individuals with intact and injured spinal cord

Posterior root-muscle (PRM) reflexes are short-latency spinal reflexes evoked by epidural or transcutaneous spinal cord stimulation (SCS) in clinical and physiological studies. PRM reflexes share key physiological characteristics with the H reflex elicited by electrical stimulation of large-diameter muscle spindle afferents in the tibial nerve. Here, we compared the H reflex and the PRM reflex of soleus in response to transcutaneous stimulation by studying their recovery cycles in ten neurologically intact volunteers and ten individuals with traumatic, chronic spinal cord injury (SCI). The recovery cycles of the reflexes, i.e., the time course of their excitability changes, were assessed by paired pulses with conditioning-test intervals of 20–5000 ms. Between the subject groups, no statistical difference was found for the recovery cycles of the H reflexes, yet those of the PRM reflexes differed significantly, with a striking suppression in the intact group. When comparing the reflex types, they did not differ in the SCI group, while the PRM reflexes were more strongly depressed in the intact group for durations characteristic for presynaptic inhibition. These differences may arise from the concomitant stimulation of several posterior roots containing afferent fibers of various lower extremity nerves by transcutaneous SCS, producing multi-source heteronymous presynaptic inhibition, and the collective dysfunction of inhibitory mechanisms after SCI contributing to spasticity. PRM-reflex recovery cycles additionally obtained for bilateral rectus femoris, biceps femoris, tibialis anterior, and soleus all demonstrated a stronger suppression in the intact group. Within both subject groups, the thigh muscles showed a stronger recovery than the lower leg muscles, which may reflect a characteristic difference in motor control of diverse muscles. Based on the substantial difference between intact and SCI individuals, PRM-reflex depression tested with paired pulses could become a sensitive measure for spasticity and motor recovery.

Pioneers in CNS inhibition: 2. Charles Sherrington and John Eccles on inhibition in spinal and supraspinal structures

This article reviews the contributions of the English neurophysiologist, Charles Scott Sherrington [1857-1952], and his Australian PhD trainee and collaborator, John Carew Eccles [1903-1997], to the concept of central inhibition in the spinal cord and brain. Both were awarded Nobel Prizes; Sherrington in 1932 for "discoveries regarding the function of neurons," and Eccles in 1963 for "discoveries concerning the ionic mechanisms involved in excitation and inhibition in central portions of the nerve cell membrane." Both spoke about central inhibition at their Nobel Prize Award Ceremonies. The subsequent publications of their talks were entitled "Inhibition as a coordinative factor" and "The ionic mechanism of postsynaptic inhibition", respectively. Sherrington's work on central inhibition spanned 41 years (1893-1934), and for Eccles 49 years (1928-1977). Sherrington first studied central inhibition by observing hind limb muscle responses to electrical (peripheral nerve) and mechanical (muscle) stimulation. He used muscle length and force measurements until the early 1900s and electromyography in the late 1920s. Eccles used these techniques while working with Sherrington, but later employed extracellular microelectrode recording in the spinal cord followed in 1951 by intracellular recording from spinal motoneurons. This considerably advanced our understanding of central inhibition. Sherrington's health was poor during his retirement years but he nonetheless made a small number of largely humanities contributions up to 1951, one year before his death at the age of 94. In contrast, Eccles retained his health and vigor until 3 years before his death and published prolifically on many subjects during his 22 years of official retirement. His last neuroscience article appeared in 1994 when he was 91. Despite poor health he continued thinking about his life-long interest, the mind-brain problem, and was attempting to complete his autobiography in the last years of his life.

Mapping Connectivity Amongst Interneuronal Components of the Locomotor CPG

The basic rhythmic activity characteristic of locomotion in mammals is generated by a neural network, located in the spinal cord, known as the locomotor central pattern generator (CPG). Although a great deal of effort has gone into the study of this neural circuit over the past century, identification and characterization of its component interneurons has proven to be challenging, largely due to their location and distribution. Recent work incorporating a molecular approach has provided a great deal of insight into the genetic identity of interneurons that make up this neural circuit, as well as the specific roles that they play during stepping. Despite this progress we still know relatively little regarding the manner in which these neuronal populations are interconnected. In this article we review the interneuronal populations shown to be involved in locomotor activity, briefly summarize their specific function, and focus on experimental work that provides insight into their synaptic connectivity. Finally, we discuss how recently developed viral approaches can potentially be incorporated to provide further insight into the network structure of this neural circuit.

Balanced cholinergic modulation of spinal locomotor circuits via M2 and M3 muscarinic receptors

Neuromodulation ensures that neural circuits produce output that is flexible whilst remaining within an optimal operational range. The neuromodulator acetylcholine is released during locomotion to regulate spinal motor circuits. However, the range of receptors and downstream mechanisms by which acetylcholine acts have yet to be fully elucidated. We therefore investigated metabotropic acetylcholine receptor-mediated modulation by using isolated spinal cord preparations from neonatal mice in which locomotor-related output can be induced pharmacologically. We report that M2 receptor blockade decreases the frequency and amplitude of locomotor-related activity, whilst reducing its variability. In contrast, M3 receptor blockade destabilizes locomotor-related bursting. Motoneuron recordings from spinal cord slices revealed that activation of M2 receptors induces an outward current, decreases rheobase, reduces the medium afterhyperpolarization, shortens spike duration and decreases synaptic inputs. In contrast, M3 receptor activation elicits an inward current, increases rheobase, extends action potential duration and increases synaptic inputs. Analysis of miniature postsynaptic currents support that M2 and M3 receptors modulate synaptic transmission via different mechanisms. In summary, we demonstrate that M2 and M3 receptors have opposing modulatory actions on locomotor circuit output, likely reflecting contrasting cellular mechanisms of action. Thus, intraspinal cholinergic systems mediate balanced, multimodal control of spinal motor output.

Recurrence Resonance" in Three-Neuron Motifs

Stochastic Resonance (SR) and Coherence Resonance (CR) are non-linear phenomena, in which an optimal amount of noise maximizes an objective function, such as the sensitivity for weak signals in SR, or the coherence of stochastic oscillations in CR. Here, we demonstrate a related phenomenon, which we call "Recurrence Resonance" (RR): noise can also improve the information flux in recurrent neural networks. In particular, we show for the case of three-neuron motifs with ternary connection strengths that the mutual information between successive network states can be maximized by adding a suitable amount of noise to the neuron inputs. This striking result suggests that noise in the brain may not be a problem that needs to be suppressed, but indeed a resource that is dynamically regulated in order to optimize information processing.

Large-Scale Analysis of the Diversity and Complexity of the Adult Spinal Cord Neurotransmitter Typology

The development of nervous system atlases is a fundamental pursuit in neuroscience, since they constitute a fundamental tool to improve our understanding of the nervous system and behavior. As such, neurotransmitter maps are valuable resources to decipher the nervous system organization and functionality. We present here the first comprehensive quantitative map of neurons found in the adult zebrafish spinal cord. Our study overlays detailed information regarding the anatomical positions, sizes, neurotransmitter phenotypes, and the projection patterns of the spinal neurons. We also show that neurotransmitter co-expression is much more extensive than previously assumed, suggesting that spinal networks are more complex than first recognized. As a first direct application, we investigated the neurotransmitter diversity in the putative glutamatergic spinal V2a-interneuron assembly. These studies shed new light on the diverse and complex functions of this important interneuron class in the neuronal interplay governing the precise operation of the central pattern generators.

Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform

Decades of research on neuromotor circuits and systems has provided valuable information on neuronal control of movement. Computational models of several elements of the neuromotor system have been developed at various scales, from sub-cellular to system. While several small models abound, their structured integration is the key to building larger and more biologically realistic models which can predict the behavior of the system in different scenarios. This effort calls for integration of elements across neuroscience and musculoskeletal biomechanics. There is also a need for development of methods and tools for structured integration that yield larger in silico models demonstrating a set of desired system responses. We take a small step in this direction with the NEUROmotor integration and Design (NEUROiD) platform. NEUROiD helps integrate results from motor systems anatomy, physiology, and biomechanics into an integrated neuromotor system model. Simulation and visualization of the model across multiple scales is supported. Standard electrophysiological operations such as slicing, current injection, recording of membrane potential, and local field potential are part of NEUROiD. The platform allows traceability of model parameters to primary literature. We illustrate the power and utility of NEUROiD by building a simple ankle model and its controlling neural circuitry by curating a set of published components. NEUROiD allows researchers to utilize remote high-performance computers for simulation, while controlling the model using a web browser.

Robust computation with rhythmic spike patterns

This work makes 2 contributions. First, we present a neural network model of associative memory that stores and retrieves sparse patterns of complex variables. This network can store analog information as fixed-point attractors in the complex domain; it is governed by an energy function and has increased memory capacity compared to early models. Second, we translate complex attractor networks into spiking networks, where the timing of the spike indicates the phase of a complex number. We show that complex fixed points correspond to stable periodic spike patterns. It is demonstrated that such networks can be constructed with resonate-and-fire or integrate-and-fire neurons with biologically plausible mechanisms and be used for robust computations, such as image retrieval.

Information coding by precise timing of spikes can be faster and more energy efficient than traditional rate coding. However, spike-timing codes are often brittle, which has limited their use in theoretical neuroscience and computing applications. Here, we propose a type of attractor neural network in complex state space and show how it can be leveraged to construct spiking neural networks with robust computational properties through a phase-to-timing mapping. Building on Hebbian neural associative memories, like Hopfield networks, we first propose threshold phasor associative memory (TPAM) networks. Complex phasor patterns whose components can assume continuous-valued phase angles and binary magnitudes can be stored and retrieved as stable fixed points in the network dynamics. TPAM achieves high memory capacity when storing sparse phasor patterns, and we derive the energy function that governs its fixed-point attractor dynamics. Second, we construct 2 spiking neural networks to approximate the complex algebraic computations in TPAM, a reductionist model with resonate-and-fire neurons and a biologically plausible network of integrate-and-fire neurons with synaptic delays and recurrently connected inhibitory interneurons. The fixed points of TPAM correspond to stable periodic states of precisely timed spiking activity that are robust to perturbation. The link established between rhythmic firing patterns and complex attractor dynamics has implications for the interpretation of spike patterns seen in neuroscience and can serve as a framework for computation in emerging neuromorphic devices.

Feedback to the future: motor neuron contributions to central pattern generator function

Motor behaviors depend on neural signals in the brain. Regardless of where in the brain behavior patterns arise, the central nervous system sends projections to motor neurons, which in turn project to and control temporally appropriate muscle contractions; thus, motor neurons are traditionally considered the last relay from the central nervous system to muscles. However, in an array of species and motor systems, an accumulating body of evidence supports a more complex role of motor neurons in pattern generation. These studies suggest that motor neurons not only relay motor patterns to the periphery, but directly contribute to pattern generation by providing feedback to upstream circuitry. In spinal and hindbrain circuits in a variety of animals - including flies, worms, leeches, crustaceans, rodents, birds, fish, amphibians and mammals - studies have indicated a crucial role for motor neuron feedback in maintaining normal behavior patterns dictated by the activity of a central pattern generator. Hence, in this Review, we discuss literature examining the role of motor neuron feedback across many taxa and behaviors, and set out to determine the prevalence of motor neuron participation in motor circuits.