Spike Potentials Research Articles

Dissociation of tic generation from tic expression during the sleep-wake cycle

SummaryMotor tics, the hallmark of Tourette syndrome (TS), are modulated by different behavioral and environmental factors. A major modulating factor is the sleep-wake cycle in which tics are attenuated to a large extent during sleep. This study demonstrates a similar reduction in tic expression during sleep in an animal model of chronic tic disorders and investigates the underlying neural mechanism. We recorded the neuronal activity during spontaneous sleep-wake cycles throughout continuous GABAA antagonist infusion into the striatum. Analysis of video streams and concurrent kinematic assessments indicated tic reduction during sleep in both frequency and intensity. Extracellular recordings in the striatum revealed a state-dependent dissociation between motor tic expression and their macro-level neural correlates (“LFP spikes”) during the sleep-wake cycle. Local field potential (LFP) spikes, which are highly correlated with tic expression during wakefulness, persisted during tic-free sleep and did not change their properties despite the reduced behavioral expression. Local, micro-level, activity near the infusion site was time-locked to the LFP spikes during wakefulness, but this locking decreased significantly during sleep. These results suggest that whereas LFP spikes encode motor tic generation and feasibility, the behavioral expression of tics requires local striatal neural activity entrained to the LFP spikes, leading to the propagation of the activity to downstream targets and consequently their motor expression. These findings point to a possible mechanism for the modulation of tic expression in patients with TS during sleep and potentially during other behavioral states.

COVID-19 Incidence Following Fan Attendance: A Case Study of the National Football League 2020-2021 Season

Background: The 2020/2021 National Football League (NFL) season was unique. Not just because it took place under incredibly difficult circumstances, but because of the contrasting conditions that led to some games having fans attend and others that did not. The conditions are akin to a natural experiment. The treatment, the football games that included fans, and the control, those games played behind closed doors. Here, we exploit this variation to better understand the effect of mass gatherings during sporting events on local incidence of COVID-19.Methods: We conducted a retrospective quasi-experimental study that included daily COVID-19 case reports from 11 March 2020 to 1 March 2021 for 219 U.S. counties (or parishes); attendance numbers from a total of 269 total NFL games, 117 of which had fans and were assigned to the treatment group, and the remaining unattended 152 games, assigned as controls. Separate daily time-series of COVID-19 cases and case rates (derived from 2019 Census county-level residential population estimates) were generated using three separate smoothing parameters (7-, 14-, and 21-day simple moving averages (SMAs)) for every NFL team and were plotted against the actuals (daily cases/rates) to detect potential spikes (outliers) in incidence levels following games. All approaches were applied to the county in which games took place (N = 30), contiguous counties (N = 189) where fans also likely travelled from, and acombination of both. A series of 7-, 14-, and 21-day windows were set up the day following each of the respective 269 games to identify if any potential spike (outlier) in COVID-19 incidence occurred within the incubation period.Outliers flagged in the period following games were recorded. Poisson exact tests were then used to evaluate for differences in case and rate spike (outlier) incidence between treatment and control groups. Additional testing that made use of the same methodology was also carried out to evaluate the potential influence of five different rates of attendance (dosage effect) on generating spikes in case- and rate-levels of COVID-19.Findings: A total of 72 different tests were conducted to determine if fan attended games led to a significantly greater number of cases and rates of COVID-19 at various levels of spatial aggregation than for games in which no fans were present. The evidence overwhelmingly supports that fan attendance at NFL games led to episodic spikes in the count of COVID-19 cases/rates in the 14-day window in-county (rate ratio 1·36 [95% CI 1·00–1·87], pInterpretation: The results of this primary analysis of COVID-19 incidence levels in the context of mass gatherings at sporting events provides compelling evidence that the presence of fans at NFL home games during the 2020/2021 season led to increased levels of COVID-19 cases and rates both in the counties in which the venues are nested within, and the surrounding counties in which fans likely travel from to attend. The increased spikes in COVID-19 cases and rates among those games attended by fans, particularly for large crowds of over 20,000 suggest that return to sporting and other mass gathering events should be handled with extreme caution and may indeed be premature. Within the context of large-scale sporting events, a more moderate, phased-in approach may be required to initially limit crowds until a sufficient level of herd immunity is reached. Still, even then, professional sports leagues and related large indoor/outdoor events need to continue to monitor public health data especially as variants circulate and data from the long-term effectiveness of vaccines emerges.Funding: The authors received no specific funding for this work.Conflict of Interest: None to declare.

Large-Scale and Multiscale Networks in the Rodent Brain during Novelty Exploration.

Neural activity is coordinated across multiple spatial and temporal scales, and these patterns of coordination are implicated in both healthy and impaired cognitive operations. However, empirical cross-scale investigations are relatively infrequent, because of limited data availability and to the difficulty of analyzing rich multivariate datasets. Here, we applied frequency-resolved multivariate source-separation analyses to characterize a large-scale dataset comprising spiking and local field potential (LFP) activity recorded simultaneously in three brain regions (prefrontal cortex, parietal cortex, hippocampus) in freely-moving mice. We identified a constellation of multidimensional, inter-regional networks across a range of frequencies (2–200 Hz). These networks were reproducible within animals across different recording sessions, but varied across different animals, suggesting individual variability in network architecture. The theta band (∼4–10 Hz) networks had several prominent features, including roughly equal contribution from all regions and strong inter-network synchronization. Overall, these findings demonstrate a multidimensional landscape of large-scale functional activations of cortical networks operating across multiple spatial, spectral, and temporal scales during open-field exploration.

Role of Medial Prefrontal Cortical Neurons and Oxytocin Modulation in the Establishment of Social Buffering.

Fear-related behaviors are rigidly controlled by the medial prefrontal cortex (mPFC). The mPFC is activated by the prosocial hormone oxytocin, which plays an important role in social buffering. We used a slice patch current-clamp recording in single- and pair-exposed rats who were subjected to electric shocks, to determine the cellular mechanism of the action of oxytocin in the mPFC under social buffering conditions. Pair-exposed rats showed a significant reduction in both freezing and passive avoidance behaviors compared to single-exposed rats. It was observed that input resistance in pyramidal neurons decreased in both single- and pair-exposed rats than na?ve rats, but input resistance in interneurons increased in pair-exposed rats than single-exposed rats. We found that the number of action potential (AP) spikes in the mPFC pyramidal neurons decreased significantly in pair-exposed rats than in single-exposed rats. The pyramidal neurons in the mPFC were similarly regulated by oxytocin in singleand pair-exposed rats, while the number of AP spikes in interneurons by oxytocin decreased in single-exposed rats, but there was no significant change in pair-exposed rats. Therefore, our findings reveal that a decrease in mPFC pyramidal neuronal activity in pair-exposed rats through social interaction induces a reduction in fear-related behavior via obstruction of fear-memory formation; however, no such reduction was observed in single-exposed rats. Moreover, we suggest that the oxytocin-mediated decrease in neuronal activity in the mPFC could facilitate social buffering.

The Mechanical Basis of Memory – the MeshCODE Theory

One of the major unsolved mysteries of biological science concerns the question of where and in what form information is stored in the brain. I propose that memory is stored in the brain in a mechanically encoded binary format written into the conformations of proteins found in the cell-extracellular matrix (ECM) adhesions that organise each and every synapse. The MeshCODE framework outlined here represents a unifying theory of data storage in animals, providing read-write storage of both dynamic and persistent information in a binary format. Mechanosensitive proteins that contain force-dependent switches can store information persistently, which can be written or updated using small changes in mechanical force. These mechanosensitive proteins, such as talin, scaffold each synapse, creating a meshwork of switches that together form a code, the so-called MeshCODE. Large signalling complexes assemble on these scaffolds as a function of the switch patterns and these complexes would both stabilise the patterns and coordinate synaptic regulators to dynamically tune synaptic activity. Synaptic transmission and action potential spike trains would operate the cytoskeletal machinery to write and update the synaptic MeshCODEs, thereby propagating this coding throughout the organism. Based on established biophysical principles, such a mechanical basis for memory would provide a physical location for data storage in the brain, with the binary patterns, encoded in the information-storing mechanosensitive molecules in the synaptic scaffolds, and the complexes that form on them, representing the physical location of engrams. Furthermore, the conversion and storage of sensory and temporal inputs into a binary format would constitute an addressable read-write memory system, supporting the view of the mind as an organic supercomputer.

Electrical activity of fungi: Spikes detection and complexity analysis

Oyster fungi Pleurotus djamor generate actin potential like spikes of electrical potential. The trains of spikes might manifest propagation of growing mycelium in a substrate, transportation of nutrients and metabolites and communication processes in the mycelium network. The spiking activity of the mycelium networks is highly variable compared to neural activity and therefore can not be analysed by standard tools from neuroscience. We propose original techniques for detecting and classifying the spiking activity of fungi. Using these techniques, we analyse the information-theoretic complexity of the fungal electrical activity. The results can pave ways for future research on sensorial fusion and decision making of fungi.

Neuron‐Gated Silicon Nanowire Field Effect Transistors to Follow Single Spike Propagation within Neuronal Network

Silicon nanowire field effect transistors (SiNW‐FETs) provide a local probe for sensing neuronal activity at the subcellular scale, thanks to their nanometer size and ultrahigh sensitivity. The combination with micropatterning or microfluidic techniques to build model neurons networks above SiNW arrays could allow monitoring of spike propagation and tailor specific stimulations, being useful to investigate network communications at multiple scales, such as plasticity or computing processes. This versatile device could be useful in many research areas, including diagnosis, prosthesis, and health security. Herein, SiNW‐based arrays enable to record electrical signals from matured neurons, such as local field potential and unitary spike within ex vivo preparations and hippocampal neurons grown on chip respectively. Furthermore, the ability to guide neurites above the sensor array during 3 weeks of cultures is demonstrated and the propagation of spikes along cells is followed. The SiNW‐FETs are obtained by a top‐down approach with complementary metal oxide semiconductor (CMOS) technology, showing the possibility to implement them at the manufacturing level. These results confirm further the potentiality of the approach to follow spike propagation over large distances and at precise locations along neuronal cells, by providing a multiscale addressing at the nano and mesoscales.

Photoelectric dye-based retinal prosthesis (OUReP) as a novel type of artificial retina

We have developed the world's first novel type of artificial retina, OUReP (Okayama University Retinal Prosthesis), in which a photoelectric dye that converts light energy into electric potential is covalently bonded to the surface of a polyethylene thin film as an insulator. The receptor that absorbs light and the output device that generates displacement current to stimulate nearby neurons are integrated in a sheet of thin film. It has become possible to measure the surface potential of the artificial retina OUReP using a Kelvin probe that measures the surface potential of semiconductors. When light is turned on and off to the artificial retina OUReP, the surface potential changes rapidly. As the light intensity is increased, the potential change on the surface of the artificial retina becomes larger. As for safety, the artificial retina OUReP was not toxic in all tests for biological evaluation of medical devices. As for efficacy, the artificial retina OUReP was implanted under the retina by vitreous surgery in monkey eyes which had chemically-induced macular degeneration with photoreceptor cell loss. Over the next 6 months, retinal detachment did not occur during the course, and the artificial retina was in contact with the retinal tissue. The amplitude of the visual evoked potential attenuated by macular degeneration recovered 1 month after implantation of the artificial retina, and the recovery of amplitude was maintained until 6 months after the implantation. By using multielectrode array-mounted dish recording system, it has been proved that action potential spikes are induced when the artificial retina is placed on degenerative retinal tissue of retinal dystrophic rats or mice and exposed to light, which is used as an index of the effectiveness of the artificial retina. We have established manufacturing and quality control of the device in a clean room facility, proved the safety and efficacy, and are preparing for first-in-human investigator-initiated clinical trials.

Large, Stable Spikes Exhibit Differential Broadening in Excitatory and Inhibitory Neocortical Boutons

SummaryPresynaptic action potential spikes control neurotransmitter release and thus interneuronal communication. However, the properties and the dynamics of presynaptic spikes in the neocortex remain enigmatic because boutons in the neocortex are small and direct patch-clamp recordings have not been performed. Here, we report direct recordings from boutons of neocortical pyramidal neurons and interneurons. Our data reveal rapid and large presynaptic action potentials in layer 5 neurons and fast-spiking interneurons reliably propagating into axon collaterals. For in-depth analyses, we establish boutons of mature cultured neurons as models for excitatory neocortical boutons, demonstrating that the presynaptic spike amplitude is unaffected by potassium channels, homeostatic long-term plasticity, and high-frequency firing. In contrast to the stable amplitude, presynaptic spikes profoundly broaden during high-frequency firing in layer 5 pyramidal neurons, but not in fast-spiking interneurons. Thus, our data demonstrate large presynaptic spikes and fundamental differences between excitatory and inhibitory boutons in the neocortex.

Health Profiling Using Event-Related Potential (ERP) Brain Signals and Spiking Neural Network (SNN)

Unhealthy lifestyles, especially on nutritional factors have become a major problem causing many diseases in Malaysians in recent years. Identification of lifestyle profiles such as preventive for individuals who adopt healthy and curative for individuals who do not maintain their lifestyle is needed to increase their awareness regarding their lifestyle. Because self-assessment is known to be vulnerable to produce response biases that lead to misclassification, identification of profiles based on brain responses needs to be done. An Event-related potential (ERP) is the main tools of cognitive neurologists and make ideal techniques for studying perception and attention. This research captured brain activity using electroencephalography (EEG) during receiving images of healthy and unhealthy foods that act as health-related stimuli. These EEG signals converted mathematically into the ERP signals and entered into the classification interface as input. In terms of classification, the methodology used is a dynamic developing Spiking Neural Network (deSSN) based on the Neucube architecture. ERP analysis results shown the mean amplitude of the LPP component in the Parietal and Occipital lobes is higher for healthy food in the preventive group. Whereas in the curative group it has been shown to be higher for unhealthy foods. This result is thought to reflect their preference in choosing food in their daily lifestyle. However, the results of the classification have shown that unhealthy food stimulation in the LPP wave showed superior results compared to data analysis in other conditions. Classification with ERP data is believed to support the results of self-assessment and build methods of making profiles that are more accurate and reliable.

Natural Product Emerging as Potential SARS Spike Glycoproteins-ACE2 Inhibitors to Combat COVID-19 Attributed by In-Silico Investigations

COVID-19 is a pandemic infectious disorder that emerged as a major outbreak for the community and health care system across the globe. Since the currently available drug therapeutics available for COVID-19 are prone to provide symptomatic and supportive relief, which has invited the entire scientist of all over the nations to investigate therapeutic drug candidates accompanied by anti-COVID-19 activity. The recognition of ACE2 mediated entry of SARS-CoV-2 encouraged us to investigate natural products as a potential inhibitor of the SARS spike glycoprotein-Human ACE2 complex. Using the strategy of molecular docking, we have assessed berberine, indigo blue, β-sitosterol, glycyrrhizin, indirubin, hesperetin, bicylogermacrene, β-caryophyllene, chrysophanic acid, rhein, curcumin, and eugenol for their inhibitory activity towards SARS spike glycoprotein-Human ACE2 complex. We have investigated including indigo blue, glycyrrhizin, β-sitosterol, indirubin, bicylogermacrene, curcumin, hesperetin, rhein, berberine with an affinity of -11.2, -10.9. -10.1, -9.8, -9.5, -9.3, -9.2, -9.1 and -9.0 kcal/mol respectively as in silico inhibitors of SARS spike glycoprotein-Human ACE2 complex which can vitalize the researchers for in-vivo assessment of these natural products.

Brain Waves: Emergence of Localized, Persistent, Weakly Evanescent Cortical Loops.

An inhomogeneous anisotropic physical model of the brain cortex is presented that predicts the emergence of nonevanescent (weakly damped) wave-like modes propagating in the thin cortex layers transverse to both the mean neural fiber direction and the cortex spatial gradient. Although the amplitude of these modes stays below the typically observed axon spiking potential, the lifetime of these modes may significantly exceed the spiking potential inverse decay constant. Full-brain numerical simulations based on parameters extracted from diffusion and structural MRI confirm the existence and extended duration of these wave modes. Contrary to the commonly agreed paradigm that the neural fibers determine the pathways for signal propagation in the brain, the signal propagation because of the cortex wave modes in the highly folded areas will exhibit no apparent correlation with the fiber directions. Nonlinear coupling of those linear weakly evanescent wave modes then provides a universal mechanism for the emergence of synchronized brain wave field activity. The resonant and nonresonant terms of nonlinear coupling between multiple modes produce both synchronous spiking-like high-frequency wave activity as well as low-frequency wave rhythms. Numerical simulation of forced multiple-mode dynamics shows that, as forcing increases, there is a transition from damped to oscillatory regime that can then transition quickly to a nonoscillatory state when a critical excitation threshold is reached. The resonant nonlinear coupling results in the emergence of low-frequency rhythms with frequencies that are several orders of magnitude below the linear frequencies of modes taking part in the coupling. The localization and persistence of these weakly evanescent cortical wave modes have significant implications in particular for neuroimaging methods that detect electromagnetic physiological activity, such as EEG and magnetoencephalography, and for the understanding of brain activity in general, including mechanisms of memory.

Coupling simulation with artificial neural networks for the optimisation of HVAC controls in manufacturing environments

Manufacturing remains one of the most energy intensive sectors, additionally, the energy used within buildings for heating, ventilation and air conditioning (HVAC) is responsible for almost half of the UK’s energy demand. Commonly, these are analysed in isolation from one another. Use of machine learning is gaining popularity due to its ability to solve non-linear problems with large data sets and little knowledge about relationships between parameters. Such models use relationships between inputs and outputs to make further predictions on unseen data, without requiring any understanding regarding the system, making them highly suited to dealing with the stochastic data sets found in a manufacturing environment. This has been seen in literature for determining electrical energy demand for residential or commercial buildings, rather than manufacturing environments. This study proposes a novel method of coupling simulation with machine learning to predict indoor workshop conditions and building energy demand, in response to production schedules, outdoor conditions, building behaviour and use. Such predictions can subsequently allow for more efficient management of HVAC systems. Based upon predicted energy consumption, potential spikes were identified and manufacturing schedules subsequently optimised to reduce peak energy demand. Coupling simulation techniques with machine learning algorithms eliminates the requirement for costly and intrusive methods of data collection, providing a method of predicting and optimising building energy consumption in the manufacturing sector.

Self-perturbation of the salt partition at the water/1,2-dichloroethane interface

Open circuit potential (OCP) measurements of the partition of tetrapentylammonium chloride (TPeACl) at the water/1,2-dichloroethane (DCE) interface reveal two remarkable processes. First one is characterized by a substantial drop and further decay of the OCP from its expected equilibrium value. This process has been previously investigated and ascribed to the anomalous extraction of the salt driven by its accumulation in the hydrated salt clusters in the DCE phase. The second process, which has been observed for the first time in the present study, is characterized by the short-term potential spikes overlapping the long-term OCP records. These spikes are assumed to be associated with the collision and fusion of the hydrated salt clusters or their aggregates produced by the former process with the water/DCE interface. Self-perturbation of the steady-state salt partition by a single cluster or aggregate impact offers a new approach to study kinetics and the mechanism of the hydrated salt cluster formation.

An ensemble unsupervised spiking neural network for objective recognition

It is now known that the spiking neuron is a basic unit of spiking neural networks (SNNs). Spiking neurons modulate the nervous cells via receiving external incentives, generation of action potential and firing spikes. The SNNs usually used for pattern recognition tasks or complex computation depending on the brain-like characteristic. Although the SNNs have no advantages comparing with the deep neural networks in terms of classification accuracy, the SNNs have more characteristics of biological neurons. In this paper, a hierarchical SNN, comprising convolutional and pooling layers, is designed. The proposed SNN consists of excitatory and inhibitory neurons based on the mechanism of the primate brain. A temporal coding (rank order) manner is used to encode the input patterns. It depends on the rank of the spike arrival on post synapses to establish the priority of input spikes for a particular pattern. The spike-timing-dependent plasticity (STDP) learning rule is used in convolutional layers to extract visual features in an unsupervised learning manner. During the classification stage, a lateral inhibition mechanism is used to prevent the non-firing neurons and produce distinguishable results. In order to improve the performance of our SNN, an ensemble SNN architecture using the voting method is proposed, and transfer learning is used to avoid re-training the SNN when solving the different tasks. The hand-written digits classification task on MNIST, CIFAR-10, and BreaKHis databases are used to verify the performance of the proposed SNN. Experimental results show that by using the ensemble architecture and transfer learning, the classification accuracy of 99.27% for the MNIST database, overall accuracy is 93% for the CIFAR-10 database, and overall accuracy is 96.97% for BreaKHis database. In the meantime, this work achieves a better performance than the benchmarking approaches. Taken together, the results of our work suggest that the ensemble SNN architecture with transfer learning is key to improving the performance of the SNN.

Protonic conductor: better understanding neural resting and action potential.

With the employment of the transmembrane electrostatic proton localization theory with a new membrane potential equation, neural resting and action potential is now much better understood as the voltage contributed by the localized protons/cations at a neural liquid- membrane interface. Accordingly, the neural resting/action potential is essentially a protonic/cationic membrane capacitor behavior. It is now understood with a newly formulated action potential equation: when action potential is <0 (negative number), the localized protons/cations charge density at the liquid-membrane interface along the periplasmic side is >0 (positive number); when the action potential is >0, the concentration of the localized protons and localized nonproton cations is <0, indicating a "depolarization" state. The nonlinear curve of the localized protons/cations charge density in the real-time domain of an action potential spike appears as an inverse mirror image to the action potential. The newly formulated action potential equation provides biophysical insights for neuron electrophysiology, which may represent a complementary development to the classic Goldman-Hodgkin-Katz equation. With the use of the action potential equation, the biological significance of axon myelination is now also elucidated as to provide protonic insulation and prevent any ions both inside and outside of the neuron from interfering with the action potential signal, so that the action potential can quickly propagate along the axon with minimal (e.g., 40 times less) energy requirement.NEW & NOTEWORTHY The newly formulated action potential equation provides biophysical insights for neuron electrophysiology, which may represent a complementary development to the classic Goldman-Hodgkin-Katz equation. The nonlinear curve of the localized protons/cations charge density in the real-time domain of an action potential spike appears as an inverse mirror image to the action potential. The biological significance of axon myelination is now elucidated as to provide protonic insulation and prevent any ions from interfering with action potential signal.

In Silico Screening of Potential Spike Glycoprotein Inhibitors of SARS-CoV-2 with Drug Repurposing Strategy.

ObjectiveTo select potential molecules that can target viral spike proteins, which may potentially interrupt the interaction between the human angiotension-converting enzyme 2 (ACE2) receptor and viral spike protein by virtual screening.MethodsThe three-dimensional (3D)-coordinate file of the receptor-binding domain (RBD)-ACE2 complex for searching a suitable docking pocket was firstly downloaded and prepared. Secondly, approximately 15,000 molecular candidates were prepared, including US Food and Drug Administration (FDA)-approved drugs from DrugBank and natural compounds from Traditional Chinese Medicine Systems Pharmacology (TCMSP), for the docking process. Then, virtual screening was performed and the binding energy in Autodock Vina was calculated. Finally, the top 20 molecules with high binding energy and their Chinese medicine (CM) herb sources were listed in this paper.ResultsIt was found that digitoxin, a cardiac glycoside in DrugBank and bisindigotin in TCMSP had the highest docking scores. Interestingly, two of the CM herbs containing the natural compounds that had relatively high binding scores, Forsythiae fructus and Isatidis radix, are components of Lianhua Qingwen (莲花清瘟), a CM formula reportedly exerting activity against severe acute respiratory syndrome (SARS)-Cov-2. Moreover, raltegravir, an HIV integrase inhibitor, was found to have a relatively high binding score.ConclusionsA class of compounds, which are from FDA-approved drugs and CM natural compounds, that had high binding energy with RBD of the viral spike protein. Our work provides potential candidates for other researchers to identify inhibitors to prevent SARS-CoV-2 infection, and highlights the importance of CM and integrative application of CM and Western medicine on treating COVID-19.

Spiking dynamic behaviors of NbO2 memristive neurons: A model study

Neuromorphic computing based on spikes has broad prospects in energy-efficient computation. Memristive neuron in this study is composed of two volatile memristors that have been shown to exhibit rich biological neuronal dynamics. Here, we show spiking dynamic behaviors of NbO2 memristive neurons by a detailed simulation study. With a DC input voltage, the operation windows of both periodic oscillation and neuron-like action potential spikes are recognized in the resistance–voltage phase diagrams of NbO2 memristive neurons. With a voltage pulse as the input, the periodic oscillation region can be classified into three subregions including the spike-OFF, spike-ON, and meta-spike transition regions. When the memristive neuron operates in the meta-spike transition region, it can regulate the “ON” and “OFF” states of the oscillation circuit by changing the ending time of the input pulse. It implies that both the input signal and the output signal determine the state of the circuit. The demonstration of a phase matching method provides a useful way for controlling “ON” and “OFF” states of the periodic oscillation behavior of the memristive neuron. Moreover, the effect of the circuit parameters on the peak-to-valley amplitude of the output spikes with action potential is investigated. A stable and controllable waveform output can be regulated by changing the capacitance, incorporating a series resistor, and customizing the active memristor. All these results provide a reliable reference for implementing memristive neurons in neuromorphic computing.

Spike Phase Shift Relative to Beta Oscillations Mediates Modality Selection.

How does the brain selectively process signals from stimuli of different modalities? Coherent oscillations may function in coordinating communication between neuronal populations simultaneously involved in such cognitive behavior. Beta power (12-30Hz) is implicated in top-down cognitive processes. Here we test the hypothesis that the brain increases encoding and behavioral influence of a target modality by shifting the relationship of neuronal spike phases relative to beta oscillations between primary sensory cortices and higher cortices. We simultaneously recorded neuronal spike and local field potentials in the posterior parietal cortex (PPC) and the primary auditory cortex (A1) when male rats made choices to either auditory or visual stimuli. Neuronal spikes exhibited modality-related phase locking to beta oscillations during stimulus sampling, and the phase shift between neuronal subpopulations demonstrated faster top-down signaling from PPC to A1 neurons when animals attended to auditory rather than visual stimuli. Importantly, complementary to spike timing, spike phase predicted rats' attended-to target in single trials, which was related to the animals' performance. Our findings support a candidate mechanism that cortices encode targets from different modalities by shifting neuronal spike phase. This work may extend our understanding of the importance of spike phase as a coding and readout mechanism.

Sex Differences in Biophysical Signatures across Molecularly Defined Medial Amygdala Neuronal Subpopulations

The medial amygdala (MeA) is essential for processing innate social and non-social behaviors, such as territorial aggression and mating, which display in a sex-specific manner. While sex differences in cell numbers and neuronal morphology in the MeA are well established, if and how these differences extend to the biophysical level remain unknown. Our previous studies revealed that expression of the transcription factors, Dbx1 and Foxp2, during embryogenesis defines separate progenitor pools destined to generate different subclasses of MEA inhibitory output neurons. We have also previously shown that Dbx1-lineage and Foxp2-lineage neurons display different responses to innate olfactory cues and in a sex-specific manner. To examine whether these neurons also possess sex-specific biophysical signatures, we conducted a multidimensional analysis of the intrinsic electrophysiological profiles of these transcription factor defined neurons in the male and female MeA. We observed striking differences in the action potential (AP) spiking patterns across lineages, and across sex within each lineage, properties known to be modified by different voltage-gated ion channels. To identify the potential mechanism underlying the observed lineage-specific and sex-specific differences in spiking adaptation, we conducted a phase plot analysis to narrow down putative ion channel candidates. Of these candidates, we found a subset expressed in a lineage-biased and/or sex-biased manner. Thus, our results uncover neuronal subpopulation and sex differences in the biophysical signatures of developmentally defined MeA output neurons, providing a potential physiological substrate for how the male and female MeA may process social and non-social cues that trigger innate behavioral responses.