Properties Of Neurons Research Articles

Velocity coding in the central brain of bumblebees

Moving animals experience wide-field optic flow due to the displacement of the retinal image during motion. These cues provide information about self-motion and are important for flight control and stabilization, and for more complex tasks like path integration. While in honeybees and bumblebees the use of wide-field optic flow in behavioral tasks is well investigated, little is known about the underlying neuronal processing of these cues. Furthermore, there is a discrepancy between the temporal frequency tuning observed in most motion-sensitive neurons described so far from the optic lobe of insects and the velocity tuning that has been shown for many behaviors. Here we investigated response properties of motion-sensitive neurons in the central brain of bumblebees. Extracellular recordings allowed us to present a large number of stimuli to probe the spatiotemporal tuning of these neurons. We presented moving gratings that simulated either front-to-back or back-to-front optic flow and found three response types. Direction-selective responses of one of the groups matched those of TN-neurons, which provide optic flow information to the central complex, while the other groups contained neurons with purely excitatory responses that were either selective or non-selective for stimulus direction. Most recorded units showed velocity-coding properties at lower angular velocities, but showed spatial frequency dependent responses at higher velocities. Based on behavioral data, neuronal modelling work has previously predicted the existence of non direction-selective neurons with such properties. Our data now provides physiological evidence for these neurons and shows that neurons with TN-like properties exhibit a similar velocity-dependent coding.

Transplantation of human neural stem cells repairs neural circuits and restores neurological function in the stroke-injured brain

Exogenous neural stem cell (NSC) transplantation has become one of the most promising treatment methods for chronic stroke. Recent studies have shown that most ischemia-reperfusion model rats recover spontaneously after injury, which limits the ability to observe long-term behavioral recovery. Here, we used a severe stroke rat model with 150 minutes of ischemia, which produced severe behavioral deficiencies that persisted at 12 weeks, to study the therapeutic effect of neural stem cells on neural restoration in chronic stroke. Our study showed that stroke model rats treated with human neural stem cells had long-term sustained recovery of motor function, reduced infarction volume, long-term human neural stem cell survival, and improved local inflammatory environment and angiogenesis. We also demonstrated that transplanted human neural stem cells differentiated into mature neurons in vivo, formed stable functional synaptic connections with host neurons, and exhibited the electrophysiological properties of functional mature neurons, indicating that they replaced the damaged host neurons. The findings showed that human fetal-derived neural stem cells had long-term effects for neurological recovery in a model of severe stroke, which suggests that human neural stem cells-based therapy may be effective for repairing damaged neural circuits in stroke patients.

CNSC-22. GLIOBLASTOMA CELLS EXHIBIT NEURON-LIKE EXCITABILITY IN BOTH ACUTE AND ORGANOTYPIC HUMAN BRAIN SLICES

Abstract Glioblastomas (GBM) are known for their significant intratumor heterogeneity, featuring a variety of plastic cell types that make effective treatment challenging. Recent studies have shown that neuronal-progenitor-like transcriptomic cell states at the leading edge of the tumor receive synaptic input from nearby neurons, which drives disease proliferation. However, the excitability of GBM cells remains controversial, with observations ranging from non-excitable to neuron-like excitability, complicating our understanding of their pathophysiology. In this study, we developed a novel experimental approach to study glioblastoma cells in both acute and cultured brain slices infiltrated with cancer from GBM patients. Using an adeno-associated virus for gene delivery, we selectively expressed fluorescent proteins in specific cell types. Fluorescence-guided whole-cell patch-clamp recordings were then used to analyze the electrophysiological properties of GBM cells and neurons within the tumor microenvironment. Our findings show that GBM cells have distinct electrophysiological properties, including a depolarized resting membrane potential (-31.95 ± 2.18 mV, n = 55 cells) and high membrane resistance (1.27 ± 0.19 GΩ, n = 62 cells). Approximately 56% of GBM cells at the tumor’s leading edge exhibited neuron-like excitability, generating aberrant action potentials (aAPs) upon depolarization. Collectively, our study provides direct evidence of the intrinsic excitability of GBM cells and a detailed characterization of their electrophysiological properties in the cancer-infiltrated human cortex. These insights shed new light on tumor heterogeneity and cell-cell interactions in glioblastoma, potentially guiding the development of targeted therapies.

Adverse Effects of Aβ1-42 Oligomers: Impaired Contextual Memory and Altered Intrinsic Properties of CA1 Pyramidal Neurons

Aβ1-42 (amyloid beta) oligomers, the major neurotoxic culprits in Alzheimer’s disease, initiate early pathophysiological events, including neuronal hyperactivity, that underlie aberrant network activity and cognitive impairment. Although several synaptotoxic effects have been extensively studied, neuronal hyperexcitability, which may also contribute to cognitive deficits, is not fully understood. Here, we found several adverse effects of in vivo injection of Aβ1-42 oligomers on contextual memory and intrinsic properties of CA1 pyramidal neurons. Male rats underwent behavioral and electrophysiological studies 1 week after microinjections into the dorsal CA1 region, followed by histological analysis. After 1 week, Aβ1-42 oligomers impaired contextual learning without affecting basic physiological functions and triggered training-induced neuronal excitability. Furthermore, riluzole, a persistent sodium current (INaP) blocker, dose-dependently reduced Aβ1-42 oligomer-induced hyperexcitability. Congo red staining, which detects insoluble amyloid deposits, further identified labeling of CA1 pyramidal neurons while immunohistochemistry with lecanemab, which detects soluble Aβ oligomers, revealed immunoreactivity of both pyramidal and non-pyramidal cells in the target area. Therefore, our study suggests that a single injection of Aβ1-42 oligomers resulted in contextual memory deficits along with concomitant neuronal hyperexcitability and amyloid deposition in the CA1 region after 1 week.

Obesogenic Cafeteria Diet Induces Dynamic Changes in Gut Microbiota, Reduces Myenteric Neuron Excitability, and Impairs Gut Contraction in Mice.

The cafeteria diet (CAF) is a superior diet model in animal experiments compared to the conventional high-fat diet (HFD), effectively inducing obesity, metabolic disturbances and multi-organ damage. Nevertheless, its impact on gut microbiota composition during the progression of obesity, along with its repercussions on the enteric nervous system (ENS) and gastrointestinal motility has not been completely elucidated. To gain more insight into the effects of CAF diet in the gut, C57BL/6 mice were fed with CAF or standard diet for 2 or 8 weeks. CAF-fed mice experienced weight gain, disturbed glucose metabolism, dysregulated expression of colonic IL-6, IL-22, TNFα and TPH1, and altered colon morphology, starting at week 2. Fecal DNA was isolated and gut microbiota composition was monitored by sequencing the V3-V4 16S rRNA region. Sequence analysis revealed that Clostridia and Proteobacteria were specific biomarkers associated with CAF-feeding at week 2, while Bacteroides, Actinobacteria were prominent at week 8. Additionally, the impact of CAF diet on ENS was investigated (week 8), where HuC/D+ neurons were measured and counted, and their biophysical properties were evaluated by patch-clamp. Gut contractility was tested in whole-mount preparations. Myenteric neurons in CAF-fed mice exhibited reduced body size, incremented cell density and decreased excitability. The amplitude and frequency of the rhythmic spontaneous contractions in colon and ileum were affected by the CAF diet. Our findings demonstrate, for the first time, that CAF diet gradually changes the gut microbiota and promotes low-grade inflammation, impacting the functional properties of myenteric neurons and gut contractility in mice.

Optimized primary dorsal root ganglion cell culture protocol for reliable K+ current patch-clamp recordings

DRG primary neuron cultures, derived from rodents, closely mimic properties of sensory neurons in vivo and are highly useful for studying pain and neurological disorders. These cultures are pivotal in patch clamp electrophysiology for sensory neuron properties analysis. A detailed, replicable protocol in scientific research ensures experiment accuracy and reproducibility. This paper provides comprehensive details for replicating the protocol and achieving consistent results in primary DRG cell culture as used for patch clamp recordings. We outlined a comprehensive protocol for establishing primary DRG cell culture, optimized for improved gigaseal formation in whole-cell patch-clamp recordings. Additionally, we conducted a simulation study focused on recording macroscopic K+ channels. The findings established an optimized novel protocol that works reliably for whole-cell patch clamp recordings and data analysis using primary DRG cells prepared as described in this publication. The details for the protocol in the literature are dispersed across various publications, making it challenging to find a comprehensive summary in one source. This study confirms, for the first time, the efficacy of using fewer protocol steps, which reduces stress and variability in obtaining suitable cells for patch clamp recordings compared to existing methods in the literature. Given the challenges posed by the dissociation process of primary DRG cells and the importance of comprehensive method documentation in the literature, the protocol presented provides improved and consistent applications of primary DRG cell culture in patch clamp recordings

Firing dynamics and coupling synchronization of memristive EMR-based Chaivlo neuron utilizing equivalent energy approach.

Firing dynamics and its energy property of neuron are crucial for exploring the mechanism of intricate information processing within the nervous system. However, the energy analysis of discrete neuron is significantly lacking in comparison to the vast literature and mature theory available on continuous neuron, thereby necessitating a focused effort in this underexplored realm. In this paper, we introduce a Chaivlo neuron map by employing a flux-controlled memristor to simulate electromagnetic radiation (EMR), and a detailed analysis of its firing dynamics is conducted based on an equivalent Hamiltonian energy approach. Our observations reveal that a range of energy-based firing behaviors, such as spike firing, coexistence firing, mixed-mode firing, and chaotic bursting firing, can be induced by EMR and injected current. To delve deeper into the synchronous firing dynamics, we establish a Chaivlo network by electrically coupling two memristive EMR-based Chaivlo neurons. Subsequently, we experimentally evaluate the synchronization behavior of this network by quantifying both the synchronization factor and the average difference of equivalent Hamiltonian energy. Our findings conclusively demonstrate that both EMR and coupling strength positively contribute to the network's synchronization ability.

In Vivo Expression of an SCA27A-linked FGF14 Mutation Results in Haploinsufficiency and Impaired Firing of Cerebellar Purkinje Neurons.

Autosomal dominant mutations in FGF14 , which encodes intracellular fibroblast growth factor 14 (iFGF14), underlie spinocerebellar ataxia type 27A (SCA27A), a devastating multisystem disorder resulting in progressive deficits in motor coordination and cognitive function. Mice lacking iFGF14 ( Fgf14 -/- ) exhibit similar phenotypes, which have been linked to iFGF14-mediated modulation of the voltage-gated sodium (Nav) channels that control the high frequency repetitive firing of Purkinje neurons, the main output neurons of the cerebellar cortex. To investigate the pathophysiological mechanisms underlying SCA27A, we developed a targeted knock-in strategy to introduce the first point mutation identified in FGF14 into the mouse Fgf14 locus ( Fgf14 F145S ), we determined the impact of in vivo expression of the mutant Fgf14 F145S allele on the motor performance of adult animals and on the firing properties of mature Purkinje neurons in acute cerebellar slices. Electrophysiological experiments revealed that repetitive firing rates are attenuated in adult Fgf14 F145S/+ cerebellar Purkinje neurons, attributed to a hyperpolarizing shift in the voltage-dependence of steady-state inactivation of Nav channels. More severe effects on firing properties and Nav channel inactivation were observed in homozygous Fgf14 F145S/F145S Purkinje neurons. Interestingly, the electrophysiological phenotypes identified in adult Fgf14 F145S/+ and Fgf14 F145S/F145S cerebellar Purkinje neurons mirror those observed in heterozygous Fgf14 +/- and homozygous Fgf14 -/- Purkinje neurons, respectively, suggesting that the mutation results in the loss of the iFGF14 protein. Western blot analysis of lysates from adult heterozygous Fgf14 F145S/+ and homozygous Fgf14 F145S/F145S animals revealed reduced or undetectable, respectively, iFGF14 expression, supporting the hypothesis that the mutant allele results in loss of the iFGF14 protein and that haploinsufficiency underlies SCA27A neurological phenotypes.

Development of a neural network with a layer of trainable activation functions for the second stage of the ensemble classifier with stacking

One of the promising directions for improving the quality of object recognition in images and parallelizing calculations is the use of ensemble classifiers with stacking. A neural network at the second level makes it possible to achieve the resulting quality of classification, which is significantly higher than each of the networks of the first level separately. The classification quality of the entire ensemble classifier with stacking depends on the efficiency of the neural networks at the first stage, their number, and the quality of the classification of the neural network of the second stage. This paper proposes a neural network architecture for the second stage of the ensemble classifier, which combines the approximating properties of traditional neurons and learning activation functions. Gaussian Radial Basis Functions (RBFs) were chosen to implement the learned activation functions, which are summed with the learned weights. The experimental studies showed that when working with the CIFAR-10 data set, the best results are obtained when six RBFs are used. A comparison with the use of multilayer perceptron (MLP) in the second stage showed a reduction in classification errors by 0.45–1.9 % depending on the number of neural networks in the first stage. At the same time, the proposed neural network architecture for the second degree had 1.69–3.7 times less learning coefficients than MLP. This result is explained by the fact that the use of an output layer with ordinary neurons allowed us not to enter into the architecture many learning activation functions for each output signal of the first stage, but to limit ourselves to only one. Since the results were obtained on the CIFAR-10 universal data set, a similar effect could be obtained on a large number of similar practical data sets.

Prenatal ethanol exposure impairs hippocampal plasticity and cognition in adolescent mice

BackgroundPrenatal alcohol exposure (PAE) induces a wide range of neurodevelopmental disabilities that are grouped under the term ‘fetal alcohol spectrum disorders’ (FASD). The effects of PAE on brain development are dependent on complex neurochemical events, including modification of AMPA receptors (AMPARs). We have recently found that chronic ethanol (EtOH) exposure decreases AMPA-mediated neurotransmission and expression through the overexpression of the specific microRNA (miR)137 and 501-3p, which target GluA1 AMPA subunit, in the developing hippocampus in vitro. Here, we explored how PAE mice may alter AMPAergic synapses in the hippocampus, and its effects on behavior. MethodsTo model PAE, we exposed C57Bl/6 pregnant mice to 10 % EtOH during during the first 10 days of gestation (GD 0–10; equivalent to the first trimester of pregnancy in humans). AMPA subunits postsynaptic expression in the hippocampus, electrical properties of CA1 neurons, memory recognition, and locomotor functions were then analyzed in adolescent PAE-exposed offspring. ResultsPAE adolescent mice showed dysregulation of AMPAergic neurotransmission, and increased miR 501-3p expression, associated with a significant reduction of spontaneous AMPA currents and intrinsic somatic excitability. In addition, PAE reduced the phosphorylation of AMPAR-containing GluA1 subunit, despite an increase in its total levels. Of note, the total levels of GluA2 and GluA3 AMPA receptors were enhanced as well. Consistently, at behavioral level, PAE reduced object recognition without altering locomotor activity. ConclusionsOur study shows that PAE leads to dysfunctional formation of AMPAergic synapses that could be responsible for neurobehavioral impairments, contributing to the understanding of the pathogenesis of FASD.

Spike-based rotation detection using a self-powered triboelectric sensor with mimicry of a vestibular system

The biological vestibular system in semicircular canals detects head rotation with high energy efficiency through its unique spike-based processing. We propose a self-powered vestibular sensory neuron inspired by the biological vestibular system and utilizing a triboelectric nanogenerator (TENG) as a rotation sensor and a neuron transistor (neuristor) as a spiking neuron. This artificial vestibular neuron simultaneously detects the angular velocity and encodes it into spikes. When the angular velocity that is applied to the TENG increases, the spiking frequency increases, and this is a desired property of the vestibular neuron. Therefore, the vestibular neuron can serve as an input neuron in a spiking neural network (SNN). We also demonstrate a fully hardware-based neuromorphic system that is attractive for its improved energy efficiency to recognize rotation axes. Compared to a vestibular system based on conventional von-Neumann computing, that based on the proposed neuromorphic computing can effectively reduce power consumption by adopting spike transmission and employing the self-powered sensor. The proposed system is promising for applications related to rotation that demands high energy-efficiency.

Synchrony dynamics underlie irregular neocortical spiking.

Cortical neurons are characterized by their variable spiking patterns. We challenge prevalent theories for the origin of spiking variability. We examine the specific hypothesis that cortical synchrony drives spiking variability in vivo . Using dynamic clamp, we demonstrate that intrinsic neuronal properties do not contribute substantially to spiking variability, but rather spiking variability emerges from weakly synchronous network drive. With large-scale electrophysiology we quantify the degree of synchrony and its time scale in cortical networks in vivo . We demonstrate that physiological levels of synchrony are sufficient to generate irregular responses found in vivo . Further, this synchrony shifts over timescales ranging from 25 to 200 ms, depending on the presence of external sensory input. Such shifts occur when the network moves from spontaneous to driven modes, leading naturally to a decline in response variability as observed across cortical areas. Finally, while individual neurons exhibit reliable responses to physiological drive, different neurons respond in a distinct fashion according to their intrinsic properties, contributing to stable synchrony across the neural network.

Alcohol consumption confers lasting impacts on prefrontal cortical neuron intrinsic excitability and spontaneous neurotransmitter signaling in the aging brain in mice

Both alcohol use disorder (AUD) and cognitive decline include disruption in the balance of excitation and inhibition in the cortex, but the potential role of alcohol use on excitation and inhibition on the aging brain is unclear. We examined the effect of moderate voluntary binge alcohol consumption on the aged, pre-disease neuronal environment by measuring intrinsic excitability and spontaneous neurotransmission on prefrontal cortical pyramidal (excitatory, glutamatergic) and non-pyramidal (inhibitory, GABAergic) neurons following a prolonged period of abstinence from alcohol in mice. Results highlight that binge alcohol consumption has lasting impacts on the electrophysiological properties of prefrontal cortical neurons. A profound increase in excitatory events onto layer 2/3 non-pyramidal neurons following alcohol consumption was seen, along with altered intrinsic excitability of pyramidal neurons, which could have a range of effects on cognitive disorder progression, such as Alzheimer’s Disease, in humans. These results indicate that moderate voluntary alcohol influences the pre-disease environment in aging and highlight the need for further mechanistic investigation into this risk factor.

Sparse-Coding Variational Autoencoders.

The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The original sparse coding model suffered from two key limitations, however: (1) computing the neural response to an image patch required minimizing a nonlinear objective function via recurrent dynamics and (2) fitting relied on approximate inference methods that ignored uncertainty. Although subsequent work has developed several methods to overcome these obstacles, we propose a novel solution inspired by the variational autoencoder (VAE) framework. We introduce the sparse coding variational autoencoder (SVAE), which augments the sparse coding model with a probabilistic recognition model parameterized by a deep neural network. This recognition model provides a neurally plausible feedforward implementation for the mapping from image patches to neural activities and enables a principled method for fitting the sparse coding model to data via maximization of the evidence lower bound (ELBO). The SVAE differs from standard VAEs in three key respects: the latent representation is overcomplete (there are more latent dimensions than image pixels), the prior is sparse or heavy-tailed instead of gaussian, and the decoder network is a linear projection instead of a deep network. We fit the SVAE to natural image data under different assumed prior distributions and show that it obtains higher test performance than previous fitting methods. Finally, we examine the response properties of the recognition network and show that it captures important nonlinear properties of neurons in the early visual pathway.

Hidden in the white matter: Current views on interstitial white matter neurons.

The mammalian brain comprises two structurally and functionally distinct compartments: the gray matter (GM) and the white matter (WM). In humans, the WM constitutes approximately half of the brain volume, yet it remains significantly less investigated than the GM. The major cellular elements of the WM are neuronal axons and glial cells. However, the WM also contains cell bodies of the interstitial neurons, estimated to number 10 to 28 million in the adult bat brain, 67 million in Lar gibbon brain, and 450 to 670 million in the adult human brain, representing as much as 1.3%, 2.25%, and 3.5% of all neurons in the cerebral cortex, respectively. Many studies investigated the interstitial WM neurons (IWMNs) using immunohistochemistry, and some information is available regarding their electrophysiological properties. However, the functional role of IWMNs in physiologic and pathologic conditions largely remains unknown. This review aims to provide a concise update regarding the distribution and properties of interstitial WM neurons, highlight possible functions of these cells as debated in the literature, and speculate about other possible functions of the IWMNs and their interactions with glial cells. We hope that our review will inspire new research on IWMNs, which represent an intriguing cell population in the brain.

Brain–machine convergent evolution: Why finding parallels between brain and artificial systems is informative

Central nervous system neurons manifest a rich diversity of selectivity profiles—whose precise role is still poorly understood. Following the striking success of artificial networks, a major debate has emerged concerning their usefulness in explaining neuronal properties. Here we propose that finding parallels between artificial and neuronal networks is informative precisely because these systems are so different from each other. Our argument is based on an extension of the concept of convergent evolution—well established in biology—to the domain of artificial systems. Applying this concept to different areas and levels of the cortical hierarchy can be a powerful tool for elucidating the functional role of well-known cortical selectivities. Importantly, we further demonstrate that such parallels can uncover novel functionalities by showing that grid cells in the entorhinal cortex can be modeled to function as a set of basis functions in a lossy representation such as the well-known JPEG compression. Thus, contrary to common intuition, here we illustrate that finding parallels with artificial systems provides novel and informative insights, particularly in those cases that are far removed from realistic brain biology.

Regulatory role of electroacupuncture on satellite glial cell activity in the colon and dorsal root ganglion of rats with irritable bowel syndrome.

To investigate the role of satellite glial cells in irritable bowel syndrome (IBS) and the effect of electroacupuncture (EA) at the Tianshu (ST25) and Shangjuxu (ST37) combination. A model for visceral hypersensitivity in IBS was induced through colorectal distension (CRD) stimulation. Clean-grade male Sprague-Dawley (SD) rats were randomly divided into four groups: a normal group (NG), a model group (MG), an electroacupuncture group (EA), and a glial cell inhibitor group (FCA). Bilateral EA (2/100 Hz, 1 mA, 30 min) was administered at the Tianshu (ST25) and Shangjuxu (ST37) in week 6. Abdominal withdrawal reflex (AWR) scores were used to assess the behavioral response associated with visceral hyperalgesia, while hematoxylin-eosin staining was employed to evaluate pathological changes in the colon. The protein and mRNA levels of glial fibrillary acidic protein (GFAP) in the colon and colon-related dorsal root ganglion (DRG) were analyzed using immun-ofluorescence, immun-ohistochemistry, Western blotting, real-time polymerase chain reaction. The impact of EA on electrophysiological properties of colon-related DRG neurons was observed through whole-cell patch clamp analysis. EA significantly reduced the visceral pain behavior scores in rats with IBS in response to graded (20, 40, 60, 80 mm Hg) CRD stimulation. Additionally, EA downregulated the protein and mRNA expression levels of GFAP in the colon and colon-related DRG of rats with IBS. EA also regulated the resting membrane potential, rheobase and action potential of colon-related DRG neurons in rats with IBS. EA can regulate the excitatory properties of colon-related DRG neurons by downregulating the protein and mRNA expression of GFAP in the colon and colon-related DRG, indicating a potential neurobiological mechanism by which EA relieves visceral hypersensitivity in rats with IBS.

Decomposition-Estimation-Reconstruction: An Automatic and Accurate Neuron Extraction Paradigm.

The extraction of spatiotemporal neuron activity from calcium imaging videos plays a crucial role in unraveling the coding properties of neurons. While existing neuron extraction approaches have shown promising results, disturbing and scattering background and unused depth still impede their performance. To address these limitations, we develop an automatic and accurate neuron extraction paradigm, dubbed as decomposition-estimation-reconstruction (DER), consisting of D-procedure, E-procedure, and R-procedure. Specifically, the D-procedure first decomposes the raw data into a low-rank background and a sparse neuron signal, and regularizes L0 -norm priors of intensity and gradient of the neuron signal to suppress blurring and artifact effects. Then, the E-procedure estimates the depth-dependent transmission of the neuron signal based on its bright and dark channel priors. The R-procedure finally integrates the depth estimation of the neuron signal as a content-importance weight into a constrained non-negative matrix decomposition framework, which facilitates accurate neuron locations to boost the quality of extracted neurons. These three procedures are coupled in a cascade manner, where the former copes with calcium imaging data to facilitate the subsequent one. Comprehensive experiments on neuron extraction from calcium imaging videos demonstrate the superiority of our DER paradigm in both qualitative results and quantitative assessments over state-of-the-art methods.

Variations of neuronal properties in the region of locus coeruleus of mice

Neurons in the locus coeruleus (LC) have been traditionally viewed as a homogenous population. Recent studies begin to reveal their heterogeneity at multiple levels, ranging from molecular compositions to projection targets. To further uncover variations of neuronal properties in the LC, we took a genetic-based tagging approach to identify these neurons. Our data revealed diverse spike waveforms among neurons in the LC region, including a considerable fraction of narrow-spiking units. While all wide-spiking units possessed the regular waveform polarity (negative-positive deflection), the narrow units can be further divided based on opposing waveform polarities. Under anesthesia, wide units emitted action potential at a higher rate than the narrow units. Under wakefulness, only one subtype of narrow units exhibited fast-spiking phenotype. These neurons also had long latencies to optogenetic stimulation. In-situ hybridization further supported the existence of a small population of putative GABAergic neurons in the LC core. Together, our data reveal characteristic differences among neurons in the LC region, and suggest that a fraction of electrophysiologically-identified narrow-spiking neurons can be fast-spiking interneurons, and their fast-spiking feature is masked by anesthesia.

Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanisms.

Neuronal systems maintain stable functions despite large variability in their physiological components. Ion channel expression, in particular, is highly variable in neurons exhibiting similar electrophysiological phenotypes, which raises questions regarding how specific ion channel subsets reliably shape intrinsic properties of neurons. Here, we use detailed conductance-based modeling to explore how stable neuronal function is achieved despite variability in channel composition among neurons. Using dimensionality reduction, we uncover two principal dimensions in the channel conductance space that capture most of the variance of the observed variability. These two dimensions correspond to two sources of variability that originate from distinct physiologically relevant mechanisms underlying the regulation of neuronal activity, providing quantitative insights into how channel composition is linked to the electrophysiological activity of neurons. These insights allow us to understand and design a model-independent, reliable neuromodulation rule for variable neuronal populations.