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bioRxiv2021

Posterior integration and thalamo-frontotemporal broadcasting are impaired in disorders of consciousness

The study of the brains static and dynamical activity is opening a valuable source of assistance for the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in-silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting-state fMRI. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to glucose metabolism as measured with [18F]FDG-PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results seed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior. HighlightsO_LIPropagation of neural events and network responses are disrupted in patients with DoC. C_LIO_LILoss of consciousness is related to the malfunctioning of two neural circuits. C_LIO_LIPosterior cortical regions lack to integrate information in altered consciousness. C_LIO_LIBreakdown of information broadcasting of subcortical cortical areas in DoC. C_LIO_LILoss of network responses in DoC patients is related to glucose metabolism. C_LI

neuroscience↗

bioRxiv2025

Self-supervised image restoration in coherent X-rayneuronal microscopy

Coherent X-ray microscopy is emerging as a transformative technology for neuronal imaging, with the potential to offer a scalable solution for reconstruction of neural circuits in millimeter sized tissue volumes. Specifically, X-ray holographic nanoto-mography (XNH) brings together outstanding capabilities in terms of contrast, spatial resolution and data acquisition speed. While recent XNH developments already enabled generating valuable datasets for neuro-sciences, a major challenge for reconstruction of neural circuits remained overcoming resolving power limits to distinguish smaller neurites and synapses in the reconstructed volumes. Here we present a self-supervised image restoration approach that simultaneously improves spatial resolution, contrast, and data acquisition speed. This enables revealing synapses with XNH, marking a major milestone in the quest for generating connectomes of full mammalian brains. We demonstrate that this method is effective for various types of neuronal tissues and acquisition schemes. We propose a scalable implementation compatible with multi-terabyte image volumes. Altogether, this work brings large-scale X-ray nanotomography to a new precision level.

neuroscience↗

bioRxiv2020

Drug self-administration in head-restrained mice for simultaneous multiphoton imaging

Multiphoton microscopy is one of several new technologies providing unprecedented insight into the activity dynamics and function of neural circuits. Unfortunately, many of these technologies require experimentation in head-restrained animals, greatly limiting the behavioral repertoire that can be studied with each approach. This issue is especially evident in drug addiction research, as no laboratories have coupled multiphoton microscopy with simultaneous intravenous drug self-administration, the gold standard of behavioral paradigms for investigating the neural mechanisms of drug addiction. Such experiments would be transformative for addiction research as one could measure or perturb an array of behavior and drug-related adaptations in precisely defined neural circuit elements over time, including but not limited to dendritic spine plasticity, neurotransmitter release, and neuronal activity. Here, we describe a new experimental assay wherein mice self-administer drugs of abuse while head-restrained, allowing for simultaneous multiphoton imaging. We demonstrate that this approach enables longitudinal tracking of activity in single neurons from the onset of drug use to relapse. The assay can be easily replicated by interested labs for relatively little cost with readily available materials and can provide unprecedented insight into the neural underpinnings of substance use disorder.

neuroscience↗

bioRxiv2020

Genomic stability of Self-inactivating Rabies

Transsynaptic viral vectors provide means to gain genetic access to neurons based on synaptic connectivity and are essential tools for the dissection of neural circuit function. Among them, the retrograde monosynaptic {Delta}G-Rabies has been widely used in neuroscience research. A recently developed engineered version of the {Delta}G-Rabies, the non-toxic self-inactivating (SiR) virus, represents the first tool for open-ended genetic manipulation of neural circuits. However, the high mutational rate of the rabies virus poses a risk that mutations targeting the key genetic regulatory element in the SiR genome could emerge and revert it to a canonical {Delta}G-Rabies. Such revertant mutations have recently been identified in a SiR batch. To address the origin, incidence and relevance of these mutations, we investigated the genomic stability of SiR in vitro and in vivo. We found that "revertant" mutations are rare and accumulate only when SiR is extensively amplified in vitro, particularly in suboptimal production cell lines that have insufficient levels of TEV protease activity. Moreover, we confirmed that SiR-CRE, unlike canonical {Delta}G-Rab-CRE or revertant-SiR-CRE, is non-toxic and that revertant mutations do not emerge in vivo during long-term experiments. HighlightsO_LIRevertant mutations are rare and do not accumulate when SiR is produced in high-TEVp expressing production cell lines C_LIO_LISiR is non-toxic in vivo C_LIO_LIRevertant SiR mutations do not accumulate during in vivo experiments C_LI

neuroscience↗

bioRxiv2026

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Extensive studies suggest the brain performs Bayesian inference to infer the latent world states. It is a fundamental neuroscience question that how canonical recurrent neural circuits in the brain implement Bayesian inference. Many existing theoretical studies focused on how the recurrent circuits compute the posterior, while largely overlooking how the circuits compute the model evidence (normalization constant in Bayes' theorem), a key quantity that measures how well a model explains the observed data.Thus, it remains largely unknown about how the recurrent circuits compute the model evidence. The present study performs rigorous theoretical analyses of the continuous attractor networks, a canonical recurrent circuit model, and reveals that the nonlinear circuit dynamics can simultaneously compute the posterior and model evidence in first two dominant subspaces within the circuit dynamics. Specifically, the circuit dynamics in the stimulus feature subspace implements the Langevin posterior sampling, and the circuit dynamics in the subspace of total neuronal activity computes the model evidence in a way analogous to the evidence lower bound in stochastic variational inference. We further extend the circuit model to compute the model evidence of multiple inputs, and simulations validate the computation in the network. Our work for the first time reveals the concurrent model evidence and posterior sampling in subspaces in continuous attractor networks, significantly deepen our understanding of the computational algorithms adopted by the neural circuits.

neuroscience↗

bioRxiv2025

Nanotopographic Control of Actin Waves and Growth Cone Navigation in Developing Neurons

The development of axons and dendrites (neurites) in a neural circuit relies on the dynamic interplay of cytoskeletal components, especially actin, and the integration of diverse environmental cues. Building on prior findings that actin dynamics can serve as a primary sensor of physical guidance cues, this work investigates the role of nanotopography in modulating and guiding actin waves and neurite-tip dynamics during early neural circuit development. Although actin dynamics is well known to contribute to pathfinding in wide axonal tips, typically referred to as growth cones, we also observe dynamic actin remodeling throughout neurites and at other, narrower, neurite tips. We find that actin-wave speeds do not change significantly in the first two weeks of neurite development on flat substrates, but decrease over the same period in neurites on nanoridges. The ability of nanoridges to guide actin waves and the neurite-tip direction also decreases as neurites mature, both for narrow tips and wide growth cones. This change in responsiveness to physical guidance cues with neuronal maturation may impact the regenerative capacity of developing neural cells that are inserted into mature brains.

biophysics↗

bioRxiv2025

Cholecystokinin input from the anterior cingulate cortex to the lateral periaqueductal gray mediates nocebo pain behavior in mice

The nocebo effect, the evil twin of the better-known placebo effect, in which anticipation of harm worsens pain and other symptoms, is increasingly thought to be responsible for poor clinical outcomes. In humans, nocebo hyperalgesia (i.e., increased pain sensitivity) is blocked by proglumide, a cholecystokinin (CCK) receptor antagonist. Yet, the neural circuitry underlying nocebo hyperalgesia remains unidentified, largely due to a lack of appropriate animal models. Independently, our two laboratories developed unique animal models of CCK-dependent nocebo hyperalgesia in which the expectation of pain was elicited by environmental or social cues. We find that both nocebo paradigms share a neural circuit involving CCK release from neurons projecting from the anterior cingulate cortex to the lateral periaqueductal gray. This previously unrecognized pathway could represent a promising target for therapeutic interventions in pain-related disorders. One-Sentence SummaryPain expectations, whether environmentally conditioned or socially transmitted, are mediated by a shared neural circuit involving cholecystokinin (CCK) projections from the anterior cingulate cortex (ACC) to the lateral periaqueductal gray (PAG).

neuroscience↗

bioRxiv2021

A General LSTM-based Deep Learning Method for Estimating Neuronal Models and Inferring Neural Circuitry

Computational neural models are essential tools for neuroscientists to study the functional roles of single neurons or neural circuits. With the recent advances in experimental techniques, there is a growing demand to build up neural models at single neuron or large-scale circuit levels. A long-standing challenge to build up such models lies in tuning the free parameters of the models to closely reproduce experimental recordings. There are many advanced machine-learning-based methods developed recently for parameter tuning, but many of them are task-specific or requires onerous manual interference. There lacks a general and fully-automated method since now. Here, we present a Long Short-Term Memory (LSTM)-based deep learning method, General Neural Estimator (GNE), to fully automate the parameter tuning procedure, which can be directly applied to both single neuronal models and large-scale neural circuits. We made comprehensive comparisons with many advanced methods, and GNE showed outstanding performance on both synthesized data and experimental data. Finally, we proposed a roadmap centered on GNE to help guide neuroscientists to computationally reconstruct single neurons and neural circuits, which might inspire future brain reconstruction techniques and corresponding experimental design. The code of our work will be publicly available upon acceptance of this paper.

neuroscience↗

bioRxiv2024

Neuronal representation of the decisional reference point in monkeys

The decisional reference point serves as a hidden benchmark for evaluating options in decision-making. Despite extensive behavioral evidence for the existence of the reference point, its neural instantiation remains unclear. To identify reference point encoding at both the single-neuron and population levels, we analyzed neural activity from macaques performing a wealth accumulation task that dissociated objective reward values from the reference point. Across six frontal regions, we found that reference-related signals were broadly distributed at the single-neuron level. However, at the population level, only the ventral bank of the anterior cingulate cortex (vbACC) encoded the reference point significantly. In contrast, the dorsal bank of ACC and dorsolateral prefrontal cortex showed population-level encoding of reference-dependent subjective value signals. The temporal sequence of these signals and their known anatomical connectivity hints at a dedicated neural circuit for reference-dependence, with the vbACC potentially serving as a global source of reference point signal modulating activity in downstream value-encoding regions. SIGNIFICANCEAll experiences are evaluated relative to an internal benchmark called the reference point. Despite its central role in all modern theories of decision-making, its neural basis has remained unclear. In this study, using dense single-neuron recordings from macaque frontal cortex, we found that only the ventral bank of the anterior cingulate cortex encoded the reference point consistently at the population level. We also found population-level reference-dependent value signals representing anticipated and earned rewards in the dorsal bank of the anterior cingulate cortex and the dorsolateral prefrontal cortex, respectively. Both were inversely modulated by the reference point as predicted by theory. These findings together with known anatomical connectivity support the existence of a dedicated neural circuit for reference-dependent valuation.

neuroscience↗

bioRxiv2024

A Brain Reward Circuit Inhibited By Next-Generation Weight Loss Drugs

Glucagon-like peptide-1 receptor agonists (GLP1RAs) effectively reduce body weight and improve metabolic outcomes, yet established peptide-based therapies require injections and complex manufacturing. Small-molecule GLP1RAs promise oral bioavailability and scalable manufacturing, but their selective binding to human versus rodent receptors has limited mechanistic studies. Here, we developed humanized GLP1R mouse models to investigate how small-molecule GLP1RAs influence feeding behavior. This approach revealed that these compounds regulate both homeostatic and hedonic feeding through parallel neural circuits. Beyond engaging canonical hypothalamic and hindbrain networks that control metabolic homeostasis, GLP1RAs recruit a discrete population of Glp1r-expressing neurons in the central amygdala, which selectively suppress the consumption of palatable foods by reducing dopamine release in the nucleus accumbens. Stimulating these central amygdalar neurons curtail hedonic feeding, whereas targeted deletion of the receptor in this cell population specifically diminishes the anorectic efficacy of GLP1RAs for reward-driven intake. These findings reveal a dedicated neural circuit through which small molecule GLP1RAs modulate reward processing, suggesting broad therapeutic potential in conditions of dysregulated dopamine signaling including substance use disorder and binge eating.

neuroscience↗

bioRxiv2025

An orthogonal TRAP enables intersectional genetic access to activated neurons in the mouse brain

The study of neural circuits has been greatly enabled by methods for obtaining genetic access to activated neurons. However, these approaches typically tag neurons based on their response to only a single stimulus, which limits the ability to define precise subpopulations of cells. Here we describe an approach (X-TRAP) in which the activity-dependent expression of Flp recombinase is gated by branaplam, a small molecule that triggers splicing of the X-ON switch. We show that X-TRAP knock-in mice exhibit undetectable Flp recombination in the absence of drug and that branaplam treatment results in robust induction of recombination selectively in neurons that express FOS. Moreover, we show that X-TRAP is orthogonal to the widely-used TRAP system, such that these two approaches can be used in the same animal to label cells with Cre and Flp recombination in response to two different stimuli. We apply this strategy to map neural circuits that control food intake. This approach for intersectional, activity-dependent genetic labeling should enhance our ability to identify the neural correlates of behavior.

neuroscience↗

bioRxiv2023

Male cuticular pheromones stimulate removal of the mating plug and promote re-mating through pC1 neurons in Drosophila females

In birds and insects, the female uptakes sperm for a specific duration post-copulation known as the ejaculate holding period (EHP) before expelling unused sperm and the mating plug through sperm ejection. In this study, we found that Drosophila melanogaster females shortens the EHP when incubated with males or mated females shortly after the first mating. This phenomenon, which we termed male-induced EHP shortening (MIES), requires Or47b+ olfactory and ppk23+ gustatory neurons, activated by 2-methyltetracosane and 7-tricosene, respectively. These odorants raise cAMP levels in pC1 neurons, responsible for processing male courtship cues and regulating female mating receptivity. Elevated cAMP levels in pC1 neurons reduce EHP and reinstate their responsiveness to male courtship cues, promoting re-mating with faster sperm ejection. This study established MIES as a genetically tractable model of sexual plasticity with a conserved neural mechanism. Significance StatementSexual plasticity, the adaptation of reproductive behavior to social changes, was explored in the fruit fly, a genetically tractable model insect. Our findings revealed that inseminated females, encountering another courting male post-mating, shorten the ejaculate holding period (EHP). Specific olfactory and gustatory pathways regulating this phenomenon were identified, converging on the pC1 neurons in the brain-a conserved neural circuit that regulates female mating activity. Odors associated with EHP shortening increased the second messenger cAMP. The transient elevation of cAMP heightened the excitability of pC1 neurons, facilitating the prompt removal of the male ejaculate and subsequent re-mating . This study established a behavioral model of sexual plasticity and provided a framework for understanding the neural circuit processes involved.

neuroscience↗

bioRxiv2025

Synthetic modulation of brain-wide anesthesia-activated neural ensembles using a transgenic mouse system

BackgroundGeneral anesthesia is commonly used to produce unconsciousness across species, though the underlying neural substrates remain poorly understood. Here we test the hypothesis that isoflurane anesthesia produces unconsciousness by targeting discrete cell types and neural circuits distributed brain-wide, rather than by brain-wide non-specific binding or binding exclusively within single brain regions. MethodsWe take advantage of a transgenic mouse system that expresses chemogenetic designer receptors exclusively activated by a designer drug (DREADDs) in an inducible Cre-dependent manner driven by Fos immediate early gene expression. Since Fos peaks with metabolic activity, we use this system to insert DREADDs brain-wide into neurons that are active under isoflurane anesthesia. We then test the effects of chemogenetic manipulation of brain-wide anesthesia-activated neural ensembles on behavior, in the absence of isoflurane. ResultsUsing iDisco+ intact brain clearing and light sheet microscopy, we describe brain-wide expression of the captured neurons revealing sparse, heterogeneous and spatially distributed cells. We quantify dense labeling across mesolimbic pathways that include the amygdala, hypothalamus, thalamus, and hindbrain nuclei, implicating these regions as candidate mediators of the anesthetic state. Chemogenetic manipulation of the brain-wide activated neural ensembles reproduces key components of anesthesia, including immobility and thermal anti-nociception. We observe significantly increased isoflurane sensitivity, though not a complete loss of consciousness as measured by the righting reflex, suggesting alternative mechanisms contribute to unconsciousness that are not captured by specific neural signatures. ConclusionsWe provide causal evidence that isoflurane anesthesia engages discrete, distributed brain-wide circuitry, recapitulating dissociable components of general anesthesia. Significance StatementOver 300 million people worldwide undergo general anesthesia each year, yet the neural circuit mechanisms that produce unconsciousness remain unresolved. This fundamental gap limits the development of safer anesthetic strategies and hinders efforts to understand long-term cognitive consequences. Here we define the intact brain-wide neural signatures of isoflurane anesthesia-induced unconsciousness using a transgenic mouse system. We capture brain-wide neurons that are activated by isoflurane, manipulate those ensembles using chemogenetic tools and characterize their spatial representations at the cellular resolution using light sheet microscopy. Together, our data provides causal evidence that anesthesia engages discrete, distributed brain-wide circuitry, offering a systems-level framework for understanding how unconsciousness is generated.

neuroscience↗

bioRxiv2020

Optimal synaptic dynamics for memory maintenance in the presence of noise

Synaptic connections in many brain areas have been found to fluctuate significantly, with substantial turnover and remodelling occurring over hours to days. Remarkably, this flux in connectivity persists in the absence of overt learning or behavioural change. What proportion of these ongoing fluctuations can be attributed to systematic plasticity processes that maintain memories and neural circuit function? We show under general conditions that the optimal magnitude of systematic plasticity is typically less than the magnitude of perturbations due to internal biological noise. Thus, for any given amount of unavoidable noise, 50% or more of total synaptic turnover should be effectively random for optimal memory maintenance. Our analysis does not depend on specific neural circuit architectures or plasticity mechanisms and predicts previously unexplained experimental measurements of the activity-dependent component of ongoing plasticity.

neuroscience↗

bioRxiv2023

Loss of cilia after neurulation impacts brain development and neuronal activity in larval zebrafish

Cilia are slender, hair-like structures extending from cell surfaces and playing essential roles in diverse physiological processes. Within the nervous system, primary cilia contribute to signaling and sensory perception, while motile cilia facilitate cerebrospinal fluid flow. Here, we investigated the impact of ciliary loss on neural circuit development using a zebrafish line displaying ciliogenesis defects. We found that cilia loss after neurulation affects neurogenesis and brain morphology, and lead to altered gene expression profiles. Using whole brain calcium imaging, we measured reduced light-evoked and spontaneous neuronal activity in all brain regions. By shedding light on the intricate role of cilia in neural circuit formation and function in the zebrafish, our work highlights their evolutionary conserved role in the brain and set the stage for future analysis of ciliopathy models.

neuroscience↗

bioRxiv2019

Whole integration of neural connectomics, dynamics and bio-mechanics for identification of behavioral sensorimotor pathways in Caenorhabditis elegans

Computational approaches which emulate in-vivo nervous system are needed to investigate mechanisms of the brain to orchestrate behavior. Such approaches must integrate a series of biophysical models encompassing the nervous system, muscles, biomechanics to allow observing the system in its entirety while supporting incorporations of different model variations. Here we develop modWorm: a modeling framework for the nematode Caenorhabditis elegans using modular integration approach. modWorm allows for construction of a model as an integrated series of configurable, exchangeable modules each describing specific biophysical processes across different modalities (e.g., nervous system, muscles, body). Utilizing modWorm, we propose a base neuro-mechanical model for C. elegans built upon the complete connectome. The model integrates a series of 7 modules: i) intra-cellular dynamics, ii) electrical and iii) chemical extra-cellular neural dynamics, iv) translation of neural activity to muscle calcium dynamics, v) muscle calcium dynamics to muscle forces, vi) muscle forces to body postures and vii) proprioceptive feedback. We validate the base model by in-silico injection of constant currents into sensory and inter-neurons known to be associated with locomotion behaviors and by applying external forces to the body. Applications of in-silico neural stimuli experimentally known to modulate locomotion show that the model can recapitulate natural behavioral responses such as forward and backward locomotion as well as mid-locomotion stimuli induced responses such as avoidance and turns. Furthermore, through in-silico ablation surveys, the model can infer novel neural circuits involved in sensorimotor behaviors. To further dissect mechanisms of locomotion, we utilize modWorm to introduce empirical based variations of intra and extra-cellular dynamics as well as model optimizations on associated parameters to elucidate their effects on simulated locomotion dynamics compared to experimental findings. Our results show that the proposed framework can be utilized to identify neural circuits which control, mediate and generate natural behavior.

neuroscience↗

bioRxiv2018

Computation is concentrated in rich clubs of local cortical neurons

To understand how neural circuits process information, it is essential to identify the relationship between computation and circuit topology. Rich-clubs, highly interconnected sets of neurons, are known to propagate a disproportionate amount of information within cortical circuits. Here, we test the hypothesis that rich-clubs also perform a disproportionate amount of computation. To do so, we recorded the spiking activity of on average [~]300 well-isolated individual neurons from organotypic cortical cultures. We then constructed weighted, directed networks reflecting the effective connectivity between the neurons. For each neuron, we quantified the amount of computation it performed based on its inputs. We found that rich-club neurons compute [~]200% more information than neurons outside of the rich club. Indeed, the amount of computation performed in the rich-club was proportional to the amount information propagation by the same neurons. This suggests that, in these circuits, information propagation drives computation. Comparing the computation-to-propagation ratio inside versus outside of the rich club showed that rich clubs compute at a slightly, though significantly, reduced level ([~]4% lower). In total, our findings indicate that rich club topology in effective cortical circuits supports not only information propagation but also neural computation.\n\nAUTHOR SUMMARYHere we answer the question of whether rich club topology in functional cortical circuits supports neural computation as it has been previously shown to do for information propagation. To do so, we combined network analysis with information theoretic tools to analyze the spiking activity of hundreds of neurons recorded from organotypic cultures of mouse somatosensory cortex. We found that neurons in rich clubs computed significantly more than neurons outside of rich clubs, suggesting that rich-clubs do support computation in cortical circuits. Indeed, the amount of computation that we found in the rich club was proportional to the amount of information they propagate suggesting that, in these circuits, information propagation drives computation.

neuroscience↗

bioRxiv2022

Central visceral commands formatted as the slow rhythms encoded in the temporal structures of the sympathetic firing originated from the neonatal rat spinal cord in vitro

Central sympathetic neural circuits continuously generate efferent commands to sustain rhythmic operations of their peripheral effectors. However, beyond the firing rates, in what configuration an effective visceral command is formatted by the spiking activities was largely unknown. This study used an in vitro splanchnic nerve-spinal cord preparation of the neonatal rats as an experimental model and recorded spontaneous efferent activities from single sympathetic fibers. The patterns of the fiber activities were quantitatively evaluated by a metric, the so-called local variation (Lv). Lv was derived from calculating the relative differences between adjacent interspike intervals (ISIs), and thus, described the spiking patterns from a regular spiking with Lv = 0 to that spiking in bursts with Lv >1. Along the time course, the dynamic components of Lv (dLv) displayed quasiperiodic oscillations. Continuous wavelet analysis showed that dLv oscillations registered a dominant power rhythm at [~]7 mHz. This slow rhythmicity was heterogeneously altered by application of various antagonists that interrupted endogenous neurotransmitter activities mediated by ionotropic glutamate receptors or GABAA receptors in the spinal cord. Thus, the oscillation of dLv manifested itself as a feature of neural network operation. On the assumption that the total power of the dLv oscillations reflects an activity status of neural network operation, the antagonist-induced change of the power fits well with a concomitant ISI change in a negative sigmoid relationship, which explains the heterogeneity of the antagonist-induced firing responses. In conclusion, dLv oscillations reflect a dynamic status of neural network operation. The slow rhythms embedded in the dLv oscillations are likely acting as an information coder and convey effective central visceral commands that can be followed by their downstream effectors. Author summaryIn what format the information is encoded by the neural activity is relatively unknown. This is especially true for autonomic regulation of visceral functions. We seek to determine the format of the central sympathetic commands, by which it can generate an effective driving force to regulate the operation of their peripheral target organs. Because most visceral organs operate with certain rhythms, we anticipate that the central visceral commands are also formatted in rhythms. Two aspects of techniques were employed. One was the in vitro electrophysiological and pharmacological techniques to acquire and to manipulate the neural signals recorded from sympathetic single-fibers. The other was the computational techniques to extract the information embedded in the timing of the spiking behaviors and to examine how these features were altered by pharmacological manipulations. We found a spontaneous change of sympathetic spiking patterns displaying rhythms with a frequency that could match the rhythmic operation of many visceral organs. Thus, the meaningful information carried by the sympathetic neural circuits for the visceral controls is likely to be encoded in the dynamics of their spiking patterns. In summary, a dynamic change of neural spiking patterns could be used as a simple scheme for neural information coding.

neuroscience↗