Spiking Activity Of Neurons Research Articles

High-gamma mirror activity patterns in the human brain during reach-to-grasp movement observation, retention, and execution-An MEG study.

While the existence of a human mirror neuron system is evident, the involved brain areas and their exact functional roles remain under scientific debate. A number of functionally different mirror neuron types, neurons that selectively respond to specific grasp phases and types for example, have been reported with single cell recordings in monkeys. In humans, spatially limited, intracranially recorded electrophysiological signals in the high-gamma (HG) range have been used to investigate the human mirror system, as they are associated with spiking activity in single neurons. Our goal here is to complement previous intracranial HG studies by using magnetoencephalography to record HG activity simultaneously from the whole head. Participants performed a natural reach-to-grasp movement observation and delayed imitation task with different everyday objects and grasp types. This allowed us to characterize the spatial organization of cortical areas that show HG-activation modulation during movement observation (mirroring), retention (mnemonic mirroring), and execution (motor control). Our results show mirroring related HG modulation patterns over bilateral occipito-parietal as well as sensorimotor areas. In addition, we found mnemonic mirroring related HG modulation over contra-lateral fronto-temporal areas. These results provide a foundation for further human mirror system research as well as possible target areas for brain-computer interface and neurorehabilitation approaches.

Lactate is an energy substrate for rodent cortical neurons and enhances their firing activity.

Glucose is the mandatory fuel for the brain, yet the relative contribution of glucose and lactate for neuronal energy metabolism is unclear. We found that increased lactate, but not glucose concentration, enhances the spiking activity of neurons of the cerebral cortex. Enhanced spiking was dependent on ATP-sensitive potassium (KATP) channels formed with KCNJ11 and ABCC8 subunits, which we show are functionally expressed in most neocortical neuronal types. We also demonstrate the ability of cortical neurons to take-up and metabolize lactate. We further reveal that ATP is produced by cortical neurons largely via oxidative phosphorylation and only modestly by glycolysis. Our data demonstrate that in active neurons, lactate is preferred to glucose as an energy substrate, and that lactate metabolism shapes neuronal activity in the neocortex through KATP channels. Our results highlight the importance of metabolic crosstalk between neurons and astrocytes for brain function.

NCMP-07. IDH MUTANT GLIOMAS PROMOTE EPILEPTOGENESIS VIA D-2-HYDROXYGLUTARATE DEPENDENT MTOR HYPER-ACTIVATION

Abstract Uncontrolled seizures in patients with low grade gliomas have a significant impact on quality of life and morbidity, yet the mechanisms through which these tumors cause seizures remain unknown. Albeit there are multiple features that contribute to tumor related epileptogenesis, IDH mutations are determined to be an independent factor, although the pathogenesis remains poorly understood. Here, we hypothesize that the active metabolite D-2-hydroxyglutarate (D-2-HG) produced by the IDH-mutant enzyme leads to metabolic disruptions in surrounding cortical neurons that consequently promote seizures. We use a complementary study of in vitro cortical cultures and electrographically sorted human cortical tissue from patients (n=5) with IDH-mutant gliomas to test this hypothesis. We demonstrate that D-2-HG leads to increased neuronal spiking activity (p< 0.0001) and promotes a distinct metabolic profile in surrounding neurons and upregulation of mTOR signaling (p< 0.0001), which is consistent in human epileptic cortex compared to peritumoral nonepileptic cortex. Furthermore, increases in neuronal activity are induced by mTOR activation and reversed with mTOR inhibition. Together, our data suggest that metabolic disruptions and mTOR signaling upregulation in the surrounding cortex due to D-2-HG may be a driving event for epileptogenesis in patients with IDH-mutant low grade gliomas.

Spatial representability of neuronal activity

A common approach to interpreting spiking activity is based on identifying the firing fields—regions in physical or configuration spaces that elicit responses of neurons. Common examples include hippocampal place cells that fire at preferred locations in the navigated environment, head direction cells that fire at preferred orientations of the animal’s head, view cells that respond to preferred spots in the visual field, etc. In all these cases, firing fields were discovered empirically, by trial and error. We argue that the existence and a number of properties of the firing fields can be established theoretically, through topological analyses of the neuronal spiking activity. In particular, we use Leray criterion powered by persistent homology theory, Eckhoff conditions and Region Connection Calculus to verify consistency of neuronal responses with a single coherent representation of space.

Collapse of complexity of brain and body activity due to excessive inhibition and MeCP2 disruption

Complex body movements require complex dynamics and coordination among neurons in motor cortex. Conversely, a long-standing theoretical notion supposes that if many neurons in motor cortex become excessively synchronized, they may lack the necessary complexity for healthy motor coding. However, direct experimental support for this idea is rare and underlying mechanisms are unclear. Here we recorded three-dimensional body movements and spiking activity of many single neurons in motor cortex of rats with enhanced synaptic inhibition and a transgenic rat model of Rett syndrome (RTT). For both cases, we found a collapse of complexity in the motor system. Reduced complexity was apparent in lower-dimensional, stereotyped brain-body interactions, neural synchrony, and simpler behavior. Our results demonstrate how imbalanced inhibition can cause excessive synchrony among movement-related neurons and, consequently, a stereotyped motor code. Excessive inhibition and synchrony may underlie abnormal motor function in RTT.

Working Memory: From Neural Activity to the Sentient Mind.

Working memory (WM) is the ability to maintain and manipulate information in the conscious mind over a timescale of seconds. This ability is thought to be maintained through the persistent discharges of neurons in a network of brain areas centered on the prefrontal cortex, as evidenced by neurophysiological recordings in nonhuman primates, though both the localization and the neural basis of WM has been a matter of debate in recent years. Neural correlates of WM are evident in species other than primates, including rodents and corvids. A specialized network of excitatory and inhibitory neurons, aided by neuromodulatory influences of dopamine, is critical for the maintenance of neuronal activity. Limitations in WM capacity and duration, as well as its enhancement during development, can be attributed to properties of neural activity and circuits. Changes in these factors can be observed through training-induced improvements and in pathological impairments. WM thus provides a prototypical cognitive function whose properties can be tied to the spiking activity of brain neurons. © 2021 American Physiological Society. Compr Physiol 11:1-41, 2021.

Trial-to-Trial Variability of Spiking Delay Activity in Prefrontal Cortex Constrains Burst-Coding Models of Working Memory.

A hallmark neuronal correlate of working memory (WM) is stimulus-selective spiking activity of neurons in PFC during mnemonic delays. These observations have motivated an influential computational modeling framework in which WM is supported by persistent activity. Recently, this framework has been challenged by arguments that observed persistent activity may be an artifact of trial-averaging, which potentially masks high variability of delay activity at the single-trial level. In an alternative scenario, WM delay activity could be encoded in bursts of selective neuronal firing which occur intermittently across trials. However, this alternative proposal has not been tested on single-neuron spike-train data. Here, we developed a framework for addressing this issue by characterizing the trial-to-trial variability of neuronal spiking quantified by Fano factor (FF). By building a doubly stochastic Poisson spiking model, we first demonstrated that the burst-coding proposal implies a significant increase in FF positively correlated with firing rate, and thus loss of stability across trials during the delay. Simulation of spiking cortical circuit WM models further confirmed that FF is a sensitive measure that can well dissociate distinct WM mechanisms. We then tested these predictions on datasets of single-neuron recordings from macaque PFC during three WM tasks. In sharp contrast to the burst-coding model predictions, we only found a small fraction of neurons showing increased WM-dependent burstiness, and stability across trials during delay was strengthened in empirical data. Therefore, reduced trial-to-trial variability during delay provides strong constraints on the contribution of single-neuron intermittent bursting to WM maintenance.SIGNIFICANCE STATEMENT There are diverging classes of theoretical models explaining how information is maintained in working memory by cortical circuits. In an influential model class, neurons exhibit persistent elevated memorandum-selective firing, whereas a recently developed class of burst-coding models suggests that persistent activity is an artifact of trial-averaging, and spiking is sparse in each single trial, subserved by brief intermittent bursts. However, this alternative picture has not been characterized or tested on empirical spike-train data. Here we combine mathematical analysis, computational model simulation, and experimental data analysis to test empirically these two classes of models and show that the trial-to-trial variability of empirical spike trains is not consistent with burst coding. These findings provide constraints for theoretical models of working memory.

Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation.

The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85–90% of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE, which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.

Dynamic Spatiotemporal Pattern Recognition With Recurrent Spiking Neural Network.

Our real-time actions in everyday life reflect a range of spatiotemporal dynamic brain activity patterns, the consequence of neuronal computation with spikes in the brain. Most existing models with spiking neurons aim at solving static pattern recognition tasks such as image classification. Compared with static features, spatiotemporal patterns are more complex due to their dynamics in both space and time domains. Spatiotemporal pattern recognition based on learning algorithms with spiking neurons therefore remains challenging. We propose an end-to-end recurrent spiking neural network model trained with an algorithm based on spike latency and temporal difference backpropagation. Our model is a cascaded network with three layers of spiking neurons where the input and output layers are the encoder and decoder, respectively. In the hidden layer, the recurrently connected neurons with transmission delays carry out high-dimensional computation to incorporate the spatiotemporal dynamics of the inputs. The test results based on the data sets of spiking activities of the retinal neurons show that the proposed framework can recognize dynamic spatiotemporal patterns much better than using spike counts. Moreover, for 3D trajectories of a human action data set, the proposed framework achieves a test accuracy of 83.6% on average. Rapid recognition is achieved through the learning methodology-based on spike latency and the decoding process using the first spike of the output neurons. Taken together, these results highlight a new model to extract information from activity patterns of neural computation in the brain and provide a novel approach for spike-based neuromorphic computing.

PCDH19 interplay with GABA(A) receptors: a window to DEE9 pathogenetic mechanisms.

PCDH19 interplay with GABA(A) receptors: a window to DEE9 pathogenetic mechanisms.

MiR-29a and the PTEN–GSK3β axis are involved in aluminum-induced damage to primary hippocampal neuronal networks

We previously reported that aluminum (Al) can cause a range of neurotoxic injuries including progressive irreversible synaptic structural damage and synaptic dysfunction, and eventually neuronal deaths. Mechanism of Al-induced electrophysiological and neuronal connectivity changes in neurons may indicate damage to the neuronal network. Here, mouse primary hippocampal neurons were cultured on micro-electrode array (MEA)- and high-content analysis (HCA)-related plates, showing that Al exposure significantly inhibited hippocampal neuronal electrical spike activity and neurite outgrowth characterized by a reduction in neurite branching and a decrease in the average total neurite length in relation to both Al dose and time of incubation. In recent years, miR-29a/ phosphatase and tensin homolog (PTEN) have been found to play pivotal roles in the morphogenesis of neurons, it has been confirmed in vitro and in vivo that the PTEN–Glycogen synthase kinase-3β (GSK-3β) axis regulates neurite outgrowth. The present study demonstrated that increases in Al exposure and dose gradually reduce miR-29a expression. Up-regulation of miR-29a in the hippocampal neurons by lentivirus transfection reversed the decrease in electrical spike activity and the reduction in both neurite branching and length induced by Al. Moreover, miR-29a suppressed the expression of PTEN and increased the level of phosphorylated Protein Kinase B (p-AKT) and p-GSK-3β which were inhibited by the Al treatment. This suggests that miR-29a is critically involved in the functional and structural neuronal damage induced by Al and is a potential target for Al neurotoxicity. Moreover, the reduction of neurite length and branching induced by Al exposure was regulated by miR-29a and its target neuronal PTEN–GSK3β signaling pathway, which also represents a possible mechanism of Al-induced the inhibition of the electrical activity. Collectively, Al-induced damage to the neuronal network occurred through miR-29a-mediated alterations of the PTEN–GSK3β signaling pathway.

Self-Organized Criticality in the Brain

Self-organized criticality (SOC) refers to the ability of complex systems to evolve toward a second-order phase transition at which interactions between system components lead to scale-invariant events that are beneficial for system performance. For the last two decades, considerable experimental evidence has accumulated that the mammalian cortex with its diversity in cell types, interconnectivity, and plasticity might exhibit SOC. Here, we review the experimental findings of isolated, layered cortex preparations to self-organize toward four dynamical motifs presently identified in the intact cortex in vivo: up-states, oscillations, neuronal avalanches, and coherence potentials. During up-states, the synchronization observed for nested theta/gamma oscillations embeds scale-invariant neuronal avalanches, which can be identified by robust power law scaling in avalanche sizes with a slope of −3/2 and a critical branching parameter of 1. This precise dynamical coordination, tracked in the negative transients of the local field potential (nLFP) and spiking activity of pyramidal neurons using two-photon imaging, emerges autonomously in superficial layers of organotypic cortex cultures and acute cortex slices, is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies. A subclass of avalanches, the coherence potentials, exhibits precise maintenance of the time course in propagated local synchrony. Avalanches emerge in superficial layers of the cortex under conditions of strong external drive. The balance of excitation and inhibition (E/I), as well as neuromodulators such as dopamine, establishes powerful control parameters for avalanche dynamics. This rich dynamical repertoire is not observed in dissociated cortex cultures, which lack the differentiation into cortical layers and exhibit a dynamical phenotype expected for a first-order phase transition. The precise interactions between up-states, nested oscillations, and avalanches in superficial layers of the cortex provide compelling evidence for SOC in the brain.

OTME-4. IDH mutated gliomas promote epileptogenesis via D-2-hydroxyglutarate dependent mTOR hyperactivation

Epilepsy in the context of brain tumors provides a great burden in these patients, yet mechanisms underlying this process are poorly understood. It has been demonstrated that isocitrate dehydrogenase (IDH) mutations are an independent factor in epileptogenesis in patients with low grade gliomas. Here, using electrographically sorted human cortical tissue from patients with IDH mutated tumor related epilepsy and in vitro cortical cultures, we explore a metabolic paradigm and its impact on increased neuronal excitability. We hypothesize the IDH mutation promotes epileptogenesis through its neomorphic activity of D-2-hydroxyglutarate (D-2-HG) production in turn interrupts surrounding normal neuronal circuitry potentially through metabolic perturbations. We demonstrate D-2-HG increases neuronal spiking activity, promotes distinct metabolic profiles independent of neuronal spiking activity, as well as increases neuronal mTOR signaling, which is reflected in human peritumoral epileptic cortex. Increased mTOR signaling is sufficient to upregulate neuronal spiking activity and, reciprocally, inhibition of mTOR corrects neuronal activity as well as partially corrects metabolic reprogramming. Our results suggest D-2-HG can lead to mTOR activation within the peritumoral neurons, thereby suggesting an additional possible mechanism of epileptogenesis in patients with IDH mutated low grade gliomas. Ultimately, our results raise the possibility of mTOR inhibition may be a promising treatment of seizures in patients with these tumors.

Noninvasive Neural Interfacing With Wearable Muscle Sensors: Combining Convolutive Blind Source Separation Methods and Deep Learning Techniques for Neural Decoding

Neural interfacing is essential for advancing our fundamental understanding of movement neurophysiology and for developing human-machine interaction systems. This can be achieved at different levels of the central nervous system (CNS) and peripheral nervous system (PNS); however, direct neural interfaces with brain and nerve tissues face important challenges and are currently limited to clinical cases of severe motor impairment. Recent advances in electronics and signal processing for recording and analyzing surface electromyographic (sEMG) signals allow for a radically new way of establishing human interfaces by reverse engineering the neural information embedded in the electrical activity of skeletal muscles. This approach provides a window into the spiking activity of motor neurons in the spinal cord. In this article, we present a brief overview of neural interfaces and discuss the properties of multichannel sEMG in comparison to other CNS and PNS recording modalities. We then describe signal processing approaches for neural interfacing from sEMG, with a focus on recent breakthroughs in convolutive blind source separation (BSS) methods and deep learning techniques. When combined, these approaches establish unique noninvasive human-machine interfaces for neurotechnologies, with applications in medical devices and large-scale consumer electronics.

Spike Activity and Genome Instability in Neurons of the Amygdaloid Complex in Rats of Selected Strains with Contrasting Nervous System Arousability in Normal Conditions and Stress

Spike Activity and Genome Instability in Neurons of the Amygdaloid Complex in Rats of Selected Strains with Contrasting Nervous System Arousability in Normal Conditions and Stress

Simulation of spiking activities neuron models using the Euler method

Simulation of neuron spiking activity models has been carried out using the Euler method. This study aims to simulate spiking activity in a neuron model. The neuron model used is the Hodgkin-Huxley neuron model, Integrate and Fire neuron model, Wilson neuron model, and Izhikevich neuron model. The research was conducted by implementing the mathematical equations of each neuron model used and then recording the membrane potential changes from time to time using the Euler method in MATLAB. The different forms of spiking activity were done by varying the variable’s value in each mathematical equation of a neuron model that describes the processing of action potentials (spikes) influenced by ion channel activity. The results showed that the Integrate and Fire neuron models produce regular spiking (RS), Hodgkin-Huxley neuron models have regular spiking (RS) forms, Wilson neuron models produce regular spiking (RS), fast-spiking (FS), and intrinsic bursting (IB), Izhikevich neuron model produces regular spiking (RS), fast-spiking (FS), intrinsic bursting (IB), chattering neurons (CH), and low threshold spiking (LTS). The complexity of the variables used and the spiking activity generated by each neuron model can provide an overview of computational efficiency and proximity to actual biological neurons.

Transcranial alternating current stimulation entrains alpha oscillations by preferential phase synchronization of fast-spiking cortical neurons to stimulation waveform

Computational modeling and human studies suggest that transcranial alternating current stimulation (tACS) modulates alpha oscillations by entrainment. Yet, a direct examination of how tACS interacts with neuronal spiking activity that gives rise to the alpha oscillation in the thalamo-cortical system has been lacking. Here, we demonstrate how tACS entrains endogenous alpha oscillations in head-fixed awake ferrets. We first show that endogenous alpha oscillations in the posterior parietal cortex drive the primary visual cortex and the higher-order visual thalamus. Spike-field coherence is largest for the alpha frequency band, and presumed fast-spiking inhibitory interneurons exhibit strongest coupling to this oscillation. We then apply alpha-tACS that results in a field strength comparable to what is commonly used in humans (<0.5 mV/mm). Both in these ferret experiments and in a computational model of the thalamo-cortical system, tACS entrains alpha oscillations by following the theoretically predicted Arnold tongue. Intriguingly, the fast-spiking inhibitory interneurons exhibit a stronger entrainment response to tACS in both the ferret experiments and the computational model, likely due to their stronger endogenous coupling to the alpha oscillation. Our findings demonstrate the in vivo mechanism of action for the modulation of the alpha oscillation by tACS.

Spike Activity in the Ventromedial Nucleus of Rat Hypothalamus during Aging.

Spike activity of neurons in the ventromedial nucleus (VMN) of the hypothalamus in adult (6-8 months) and aged (2 years) male rats was studied by the in vivo extracellular method using stereotaxic insertion of microelectrodes. In all animals, firing frequency of most VMN neurons increased in response to glucose administration. However, in aged rats, the mean baseline and glucose-induced spike frequencies of VMN neurons were lower than in adult animals. These results support the hypothesis that aging is associated with a decrease in the functional activity of hypothalamic neurons.

MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity.

Here we present our Python toolbox "MR. Estimator" to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling-the difficulty to observe the whole system in full detail-limits our capability to record. Applications range from epidemic spreading to any system that can be represented by an autoregressive process. In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system's dynamic working point.

Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework.

It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.