Circuit Level Research Articles

Test-retest reliability and predictive utility of a macroscale principal functional connectivity gradient.

Mapping individual differences in brain function has been hampered by poor reliability as well as limited interpretability. Leveraging patterns of brain-wide functional connectivity (FC) offers some promise in this endeavor. In particular, a macroscale principal FC gradient that recapitulates a hierarchical organization spanning molecular, cellular, and circuit level features along a sensory-to-association cortical axis has emerged as both a parsimonious and interpretable measure of individual differences in behavior. However, the measurement reliabilities of this FC gradient have not been fully evaluated. Here, we assess the reliabilities of both global and regional principal FC gradient measures using test-retest data from the young adult Human Connectome Project (HCP-YA) and the Dunedin Study. Analyses revealed that the reliabilities of principal FC gradient measures were (1) consistently higher than those for traditional edge-wise FC measures, (2) higher for FC measures derived from general FC (GFC) in comparison with resting-state FC, and (3) higher for longer scan lengths. We additionally examined the relative utility of these principal FC gradient measures in predicting cognition and aging in both datasets as well as the HCP-aging dataset. These analyses revealed that regional FC gradient measures and global gradient range were significantly associated with aging in all three datasets, and moderately associated with cognition in the HCP-YA and Dunedin Study datasets, reflecting contractions and expansions of the cortical hierarchy, respectively. Collectively, these results demonstrate that measures of the principal FC gradient, especially derived using GFC, effectively capture a reliable feature of the human brain subject to interpretable and biologically meaningful individual variation, offering some advantages over traditional edge-wise FC measures in the search for brain-behavior associations.

Dissociation of Centrally and Peripherally Induced Transcranial Magnetic Stimulation Effects in Nonhuman Primates.

Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation method that is rapidly growing in popularity for studying causal brain-behavior relationships. However, its dose-dependent centrally induced neural mechanisms and peripherally induced sensory costimulation effects remain debated. Understanding how TMS stimulation parameters affect brain responses is vital for the rational design of TMS protocols. Studying these mechanisms in humans is challenging because of the limited spatiotemporal resolution of available noninvasive neuroimaging methods. Here, we leverage invasive recordings of local field potentials in a male and a female nonhuman primate (rhesus macaque) to study TMS mesoscale responses. We demonstrate that early TMS-evoked potentials show a sigmoidal dose-response curve with stimulation intensity. We further show that stimulation responses are spatially specific. We use several control conditions to dissociate centrally induced neural responses from auditory and somatosensory coactivation. These results provide crucial evidence regarding TMS neural effects at the brain circuit level. Our findings are highly relevant for interpreting human TMS studies and biomarker developments for TMS target engagement in clinical applications.SIGNIFICANCE STATEMENT Transcranial magnetic stimulation (TMS) is a widely used noninvasive brain stimulation method to stimulate the human brain. To advance its utility for clinical applications, a clear understanding of its underlying physiological mechanisms is crucial. Here, we perform invasive electrophysiological recordings in the nonhuman primate brain during TMS, achieving a spatiotemporal precision not available in human EEG experiments. We find that evoked potentials are dose dependent and spatially specific, and can be separated from peripheral stimulation effects. This means that TMS-evoked responses can indicate a direct physiological stimulation response. Our work has important implications for the interpretation of human TMS-EEG recordings and biomarker development.

Complex Synaptic and Intrinsic Interactions Disrupt Input/Output Functions in the Hippocampus of Scn1b Knock-Out Mice.

Pathogenic variants in SCN1B have been linked to severe developmental epileptic encephalopathies including Dravet syndrome. Scn1b knock-out (KO) mice model SCN1B loss-of-function (LOF) disorders, demonstrating seizures, developmental delays, and early death. SCN1B encodes the protein β1, an ion channel auxiliary subunit that also has roles in cell adhesion, neurite outgrowth, and gene expression. The goal of this project is to better understand of how loss of Scn1b alters information processing in the brain, resulting in seizures and associated cognitive dysfunction. Using slice electrophysiology in the CA1 region of the hippocampus from male and female Scn1b KO mice and wild-type (WT) littermates, we found that processing of physiologically relevant patterned Schaffer collateral (SC) stimulation produces larger, prolonged depolarizations and increased spiking in KO neurons compared with WTs. KO neurons exhibit enhanced intrinsic excitability, firing more action potentials with current injection. Interestingly, SC stimulation produces smaller, more facilitating excitatory and IPSCs in KO pyramidal neurons, but larger postsynaptic potentials (PSPs) with the same stimulation. We also found reduced intrinsic firing of parvalbumin (PV)-expressing interneurons and disrupted recruitment of both parvalbumin-expressing and somatostatin (SST)-expressing interneurons in response to patterned synaptic stimulation. Neuronal information processing relies on the interplay between synaptic properties, intrinsic properties that amplify or suppress incoming synaptic signals, and firing properties that produce cellular output. We found changes at each of these levels in Scn1b KO pyramidal neurons, resulting in fundamentally altered cellular information processing in the hippocampus that likely contributes to the complex phenotypes of SCN1B-linked epileptic encephalopathies.SIGNIFICANCE STATEMENT Genetic developmental epileptic encephalopathies have limited treatment options, in part because of our lack of understanding of how genetic changes result in dysfunction at the cellular and circuit levels. SCN1B is a gene linked to Dravet syndrome and other developmental epileptic encephalopathies, and Scn1b knock-out (KO) mice phenocopy the human disease, allowing us to study underlying neurophysiological changes. Here, we found changes at all levels of neuronal information processing in brains lacking Scn1b, including intrinsic excitability, synaptic properties, and synaptic integration, resulting in greatly enhanced input/output functions of the hippocampus. Our study shows that loss of Scn1b results in a complex array of cellular and network changes that fundamentally alters information processing in the hippocampus.

Increased presynaptic excitability in a migraine with aura mutation.

Migraine is a common and disabling neurological disorder. The headache and sensory amplifications of migraine are attributed to hyperexcitable sensory circuits, but a detailed understanding remains elusive. A mutation in casein kinase 1 delta (CK1δ) was identified in non-hemiplegic familial migraine with aura and advanced sleep phase syndrome. Mice carrying the CK1δT44A mutation were more susceptible to spreading depolarization (the phenomenon that underlies migraine aura), but mechanisms underlying this migraine-relevant phenotype were not known. We used a combination of whole-cell electrophysiology and multiphoton imaging, in vivo and in brain slices, to compare CK1δT44A mice (adult males) to their wild-type littermates. We found that despite comparable synaptic activity at rest, CK1δT44A neurons were more excitable upon repetitive stimulation than wild-type, with a reduction in presynaptic adaptation at excitatory but not inhibitory synapses. The mechanism of this adaptation deficit was a calcium-dependent enhancement of the size of the readily releasable pool of synaptic vesicles, and a resultant increase in glutamate release, in CK1δT44A compared to wild-type synapses. Consistent with this mechanism, CK1δT44A neurons showed an increase in the cumulative amplitude of excitatory post-synaptic currents, and a higher excitation-to-inhibition ratio during sustained activity compared to wild-type. At a local circuit level, action potential bursts elicited in CK1δT44A neurons triggered an increase in recurrent excitation compared to wild-type, and at a network level, CK1δT44A mice showed a longer duration of 'up state' activity, which is dependent on recurrent excitation. Finally, we demonstrated that the spreading depolarization susceptibility of CK1δT44A mice could be returned to wild-type levels with the same intervention (reduced extracellular calcium) that normalized presynaptic adaptation. Taken together, these findings show a stimulus-dependent presynaptic gain of function at glutamatergic synapses in a genetic model of migraine, that accounts for the increased spreading depolarization susceptibility and may also explain the sensory amplifications that are associated with the disease.

A prefrontal cortex alpha/delta switch controls the transition from positive to negative affective states

Positive and negative emotional states in rats can be studied by investigating ultrasonic vocalizations (USVs). Positive affect in rats is indexed by 50 kHz hedonic USVs, and negative affect is indexed by 22 kHz aversive calls. We examined the relationship of emotional states in rats using medial prefrontal cortex (MPFC) quantitative electroencephalograms (qEEG) and found that hedonic USVs were associated with active wake qEEG (high alpha/low delta power), and aversive USVs occurred with groggy wake qEEG (low alpha/high delta). Further, alpha frequency electrical stimulation of the MPFC induces hedonic calls and reward-seeking behavior, whereas delta frequency stimulation produces aversive calls and avoidance behavior. The brain region responsible for generating motor output for USVs, the periaqueductal gray (PAG), shows a motor-evoked potential that is temporally locked to the alpha (hedonic) and delta (aversive) motor-evoked potential. Closed-loop alpha frequency electrical stimulation could prevent delta qEEG and aversive USVs. At the neuronal circuit level, the alpha rhythm was associated with synaptic long-term potentiation (LTP) in the cortex, whereas the delta rhythm was associated with synaptic depotentiation (LTD) in the cortex. At the pharmacological level, NMDAR and growth factor modulation regulated these forms of neuroplasticity. At the single neuron level, excitatory neurons show increased activity in response to alpha frequencies and decreased activity during delta frequencies. In humans, the feeling of joy increased alpha and decreased delta power in frontal scalp qEEG, and the opposite response was seen for sadness. Thus, the synchronization of alpha/delta oscillations through the neuronal circuit responsible for emotional expression coordinates emotional behavior, and the switch between active wake/positive affect and groggy wake/negative affect is under the control of an LTP- LTD synaptic plasticity mechanism.

A Pathogenic Missense Mutation in Kainate Receptors Elevates Dendritic Excitability and Synaptic Integration through Dysregulation of SK Channels.

Numerous rare variants that cause neurodevelopmental disorders (NDDs) occur within genes encoding synaptic proteins, including ionotropic glutamate receptors. However, in many cases, it remains unclear how damaging missense variants affect brain function. We determined the physiological consequences of an NDD causing missense mutation in the GRIK2 kainate receptor (KAR) gene, that results in a single amino acid change p.Ala657Thr in the GluK2 receptor subunit. We engineered this mutation in the mouse Grik2 gene, yielding a GluK2(A657T) mouse, and studied mice of both sexes to determine how hippocampal neuronal function is disrupted. Synaptic KAR currents in hippocampal CA3 pyramidal neurons from heterozygous A657T mice exhibited slow decay kinetics, consistent with incorporation of the mutant subunit into functional receptors. Unexpectedly, CA3 neurons demonstrated elevated action potential spiking because of downregulation of the small-conductance Ca2+ activated K+ channel (SK), which mediates the post-spike afterhyperpolarization. The reduction in SK activity resulted in increased CA3 dendritic excitability, increased EPSP-spike coupling, and lowered the threshold for the induction of LTP of the associational-commissural synapses in CA3 neurons. Pharmacological inhibition of SK channels in WT mice increased dendritic excitability and EPSP-spike coupling, mimicking the phenotype in A657T mice and suggesting a causative role for attenuated SK activity in aberrant excitability observed in the mutant mice. These findings demonstrate that a disease-associated missense mutation in GRIK2 leads to altered signaling through neuronal KARs, pleiotropic effects on neuronal and dendritic excitability, and implicate these processes in neuropathology in patients with genetic NDDs.SIGNIFICANCE STATEMENT Damaging mutations in genes encoding synaptic proteins have been identified in various neurodevelopmental disorders, but the functional consequences at the cellular and circuit level remain elusive. By generating a novel knock-in mutant mouse, this study examined the role of a pathogenic mutation in the GluK2 kainate receptor (KAR) subunit, a subclass of ionotropic glutamate receptors. Analyses of hippocampal CA3 pyramidal neurons determined elevated action potential firing because of an increase in dendritic excitability. Increased dendritic excitability was attributable to reduced activity of a Ca2+ activated K+ channel. These results indicate that a pathogenic KAR mutation results in dysregulation of dendritic K+ channels, which leads to an increase in synaptic integration and backpropagation of action potentials into distal dendrites.

Inexact radix-4 Booth multipliers based on new partial product generation scheme for image multiplication

Inexact computing, is a modern approach for the development of low power and high-performance digital circuits, which involves approximation stages to get the utmost accurate result and are very much applicable in some error-tolerant applications like image processing. Such applications are limited to human eyesight ability, which gives us the freedom to make the approximate output. In this manuscript, two inexact partial product generators, viz. IPPG1, IPPG2 are proposed for the design of radix-4 based 8 × 8 Booth multipliers IRBM1.1, IRBM1.2, respectively. Moreover, an inexact 4:2 compressor is also proposed to add such partial product bits generated by IPPG1 and IPPG2, and nomenclate them as IRBM2.1 and IRBM 2.2, respectively. IPPG1 and IPPG2 are designed by introducing errors in the Karnaugh’s map to reduce the circuit complexity and errors, respectively. Similarly, errors are introduced in the truth table of the exact 4:2 compressor to design a low power, high speed inexact 4:2 compressor. Error metrics estimation of such proposed inexact Booth multipliers and the state-of-the-art designs are performed using MATLAB and circuit performance parameters like area, computational delay and power dissipation are extracted using gpdk45 nm technology. While comparing, IRBM1.2 offers minimum error metrics than all other reported design so far. Moreover, in the circuit level prospect, IRBM1.1 consumes minimum power and IRBM2.2 is the fastest among its reported counter-parts. IRBM2.2 offers minimum power-delay-area (PDA) product over all of its reported counter-parts. For the validation of the proposed designs, an image multiplication, edge-detection using Sobel operator, and convolutional neural network-based application is incorporated and quality assessment parameters are measured using MATLAB.

An analytical drain current modelling of DMGC CGAA FET: A circuit level implementation

The GAA FET has emerged as a promising device due to its excellent control over short-channel effects and improved electrostatic control. This manuscript presents the analytical modelling for the Dual Material Graded Channel (DMGC) Cylindrical GAA FET to characterize the drain current in linear, saturation, as well as subthreshold regions. The model incorporates the effect of supply voltage, radius, and thickness of oxide layer on the device enabling a comprehensive analysis of the device behaviour. The influence of subthreshold swing is also presented. Next, the analysis is extended to investigate the important analog performance parameters that includes transconductance and output conductance. The validation of the analytical model across a wide range of operating conditions with the simulated data is performed and observed to be a close match. Building upon the insights gained from the analytical modelling, a common source amplifier based on the DMGC CGAA FET is designed. The amplifier’s performance has been optimized by carefully selecting the biasing conditions and a maximum gain value of 7.745 is achieved. Further, an improvement of 42.28% in output voltage is observed for DMGC in comparison with SMGC making it a promising device for high-performance integrated circuit design.

Local and long-range GABAergic circuits in hippocampal area CA1 and their link to Alzheimer's disease.

GABAergic inhibitory neurons are the principal source of inhibition in the brain. Traditionally, their role in maintaining the balance of excitation-inhibition has been emphasized. Beyond homeostatic functions, recent circuit mapping and functional manipulation studies have revealed a wide range of specific roles that GABAergic circuits play in dynamically tilting excitation-inhibition coupling across spatio-temporal scales. These span from gating of compartment- and input-specific signaling, gain modulation, shaping input-output functions and synaptic plasticity, to generating signal-to-noise contrast, defining temporal windows for integration and rate codes, as well as organizing neural assemblies, and coordinating inter-regional synchrony. GABAergic circuits are thus instrumental in controlling single-neuron computations and behaviorally-linked network activity. The activity dependent modulation of sensory and mnemonic information processing by GABAergic circuits is pivotal for the formation and maintenance of episodic memories in the hippocampus. Here, we present an overview of the local and long-range GABAergic circuits that modulate the dynamics of excitation-inhibition and disinhibition in the main output area of the hippocampus CA1, which is crucial for episodic memory. Specifically, we link recent findings pertaining to GABAergic neuron molecular markers, electrophysiological properties, and synaptic wiring with their function at the circuit level. Lastly, given that area CA1 is particularly impaired during early stages of Alzheimer's disease, we emphasize how these GABAergic circuits may contribute to and be involved in the pathophysiology.

Time-dependent statistical NBTI model for aging assessment in circuit level implemented with open model interface

As technology nodes continue to shrink, the impact of aging problems on devices has become increasingly important. One significant aging problem that affects device characteristics and circuit performance is Negative Bias Temperature Instability (NBTI). Under small-size conditions, fluctuations in device parameters such as threshold voltage due to aging are not negligible. To capture these fluctuations, an improved statistical model has been proposed. The time-dependent statistical model accounts for the average change in the number of charged defects due to NBTI and enables the evaluation of degradation fluctuations over time. The model prediction of the threshold voltage degradation of devices in advanced technology is verified with different measured data. Furthermore, the model is implemented with the Open Model Interface (OMI) for the circuit-level degradation simulation, taking advantage of OMI's convenience and compatibility with different circuit simulators. The integration of the proposed model into the OMI benefits in predicting the degradations difference among the transistors in the circuits, reducing the overestimation of aging degradation compared to the methods that assume every transistor experiences the same and worst degradation.

Device and circuit level performance assessments of gate engineered Ge/GaAs heterojunction doping less TFET

AbstractRecently, the doping‐less tunnel FET has gained popularity due to its lower process complexity than conventional TFETs with heavily doped source and drain regions. In this literature, we have introduced a symmetric gate Ge/GaAs heterojunction doping‐less TFET(SG HJ DL TFET) using 4.6 eV work function for both the top gate and bottom gate and an asymmetric gate Ge/GaAs heterojunction doping‐less TFET (AG HJ DL TFET) using 3.9 eV work function as top gate and 4.6 eV work function as a bottom gate. A comparative study has been performed between these two devices. The back gate misalignment engineering in the AG HJ DL TFET induces higher charge plasma near the source‐channel junction which will enhance the drain current. Due to a larger drain current, an improved ratio (11.8 times), and steeper SS (37%) are achieved in optimized AG HJ DL TFET than SG HJ DL TFET. Furthermore, the Analog/Radio Frequency and linearity performance parameters have been examined, and a significant improvement is noticed in the optimized AG HJ DL TFET. The impact of different interface trap charges (ITCs) on device performances is also investigated and it is found that AG HJ DL TFET shows more immunity to ITCs than SG HJ DL TFET. Finally, the Verilog‐A‐based model has been developed for the AG HJ DL TFET to utilize its circuit‐level behavior. The optimized device‐based Common Source amplifier (CS amplifier) has been simulated in the Cadence Virtuoso tool. The AG HJ DL TFET‐based CS amplifier offers a 6.83% improvement in voltage gain over the other reported work.

Analog and mixed circuit analysis of nanosheet FET at elevated temperatures

In this paper, for the first time, the performance of 3D Nanosheet FETs (NSFETs) is reported in the inversion (INV), accumulation (ACC), and junctionless (JL) modes at elevated temperatures. It is observed that, as the temperature increases from 25 °C to 200 °C, a decrement of 68% in mobility is observed for INV mode. In contrast, an increment of around 29% in mobility is observed for ACC and JL modes owing to the less scattering effects. Various crucial DC and analog/RF figures of merit (FOMs), such as SS, DIBL, gm, f T, etc, are assessed for different temperatures in all three modes. Further, the impact of temperature on circuit performance is demonstrated. From the circuit analyses, at 25 °C, an increment of around 12% in propagation delay is noticed for the JL and ACC mode inverter compared to the INV mode inverter due to higher I ON for INV mode. However, at 200 °C, JL inverter outperforms INV and ACC modes because of an increment in mobility. Moreover, for ring oscillator (RO), an oscillation frequency of 43.39 GHz, 38.9 GHz and 38.8 GHz for INV, ACC and JL mode ROs at 25 °C, whereas oscillation frequencies of 27.08 GHz, 39.2 GHz and 42.88 GHz are noticed for INV, ACC and JL mode ROs at 200 °C respectively. Though JL NSFET offers less intrinsic capacitances, at 25 °C, the frequency of oscillations (f osc) is high for INV mode because of higher I ON. Whereas, at 200 °C, the JL mode outperforms due to the absence of mobility degradation with temperature. These results will give an understanding of this future generation device at both device and circuit levels.

Metamaterial modeling in circuit level for THz wave manipulation

Leveraging both method and concept a two-layer THz absorber based on periodic arrays of graphene rings is proposed. The design methodology based on the equivalent circuit model is developed for the proposed absorber. The device is described as an impedance and also simulated by the FEM full-wave method to verify the circuit model accuracy. According to the simulation results, the proposed THz absorber can show perfect absorption from 0.5 THz to 3.5 THz while adjustability capability is obtained for different chemical potentials. Additionally, the sensitivity against geometrical parameters and different incident angels is investigated. Based on the provided results and the simplicity of the structure, the proposed absorber is an ideal candidate for several applications ranging from security to medical imaging.

Physics-Integrated Machine Learning for Efficient Design and Optimization of a Nanoscale Carbon Nanotube Field-Effect Transistor

We propose an efficient framework for optimizing the design of Carbon Nanotube Field-Effect Transistor (CNTFET) through the integration of device physics, machine learning (ML), and multi-objective optimization (MOO). Firstly, we leverage the calibrated TCAD model based on experimental data to dissect the physical mechanisms of CNTFET, gaining insights into its operational principles and unique physical properties. This model also serves as a foundation, enabling multi-scale performance evaluations essential for dataset construction. In the ML phase, a chain structure of Support Vector Regression (SVR Chain) guided by a comprehensive statistical analysis of the design metrics is utilized to predict the design metrics. The surrogate model based on the SVR Chain achieves an average mean absolute percentage error (MAPE) of 1.59% across all design metrics without overfitting, even with limited data. The established ML model exhibits its competence in rapidly producing a global response surface for multi-scale CNTFET. Remarkably, an anomalous equivalent oxide thickness (EOT) and ON-state current (I on ) relationship is observed in CNTFET behavior due to extreme gate length scaling in long channel devices. This intriguing observation is further elucidated through a physics-based explanation. We further compare shallow and deep learning-based TCAD digital twins for model selection guidance. Using the Non-Dominated Sorted Genetic Algorithm-II (NSGA-II) in MOO, we harmonize metrics at both device and circuit levels, significantly reducing the design space. The closed-loop framework expedites the early-stage development of advanced transistors, overcoming the challenges posed by limited data.

Reduced thalamic excitation to motor cortical pyramidal tract neurons in parkinsonism.

Degeneration of midbrain dopaminergic (DA) neurons alters the connectivity and functionality of the basal ganglia-thalamocortical circuits in Parkinson's disease (PD). Particularly, the aberrant outputs of the primary motor cortex (M1) contribute to parkinsonian motor deficits. However, cortical adaptations at cellular and synaptic levels in parkinsonism remain poorly understood. Using multidisciplinary approaches, we found that DA degeneration induces cell subtype- and input-specific reduction of thalamic excitation to M1 pyramidal tract (PT) neurons. At molecular level, we identified that N-methyl-d-aspartate (NMDA) receptors play a key role in mediating the reduced thalamocortical excitation to PT neurons. At circuit level, we showed that the reduced thalamocortical transmission in parkinsonian mice can be rescued by chemogenetically suppressing basal ganglia outputs. Together, our data suggest that cell subtype- and synapse-specific adaptations in M1 contribute to altered cortical outputs in parkinsonism and are important aspects of PD pathophysiology.

A Swine Model of Neural Circuit Electromagnetic Fields: Effects of Immediate Electromagnetic Field Stimulation on Cortical Injury.

Background Neurologic diseases have profound disability, mortality, and socioeconomic effects worldwide. Treatment of these disorders varies but is largely limited to unique factors associated with neural physiology. Early studies have evaluated alterations in electromagnetic fields (EMF) due to neural disorders with subsequent modulation of EMF as a potential treatment modality. Swine models have begun to be evaluated as translational models in this effect. Methods EMF measurements of a Yucatan miniswine were recorded using proprietary non-contact, non-invasive induction sensors with a dual layer Mu-metal and interlaced copper mesh helmet. The swine then underwent controlled cortical impact (CCI) to simulate traumatic brain injury (TBI). Twenty minutes post-injuryafter surgical wound closure, the swine underwent targeted EMF signal modulation using a signal generator to stimulate the swine's injured cortical circuit using a sinusoidal wave individualized at 2.5 Hz with a 500mV positive offset at 1V. After 10 days of stimulation, settings were modified to another individualized frequency of 5.5 Hz, 500mV positive offset and 1V for stimulation. Behavioral patterns in swine were evaluated, and EMF measurements were recorded daily prior to, during, and after stimulation. Artificial intelligence (AI) models evaluated patterns in EMF signals.Histology of the stimulated swine cortex was evaluated using hematoxylin and eosin staining and pentachrome staining and compared to a control swine without stimulation and a swine that had received stimulation two days post-injury in a delayed fashion. Serial serum specimens and tissue at the time of euthanasia were obtained for assessment of neuron-specific enolase (NSE) concentration. Results Pre-operative and post-stimulation measurements demonstrated differences in patterns and activity early on. There was an identified peak at 1.6Hz, not frequently seen pre-operatively. There were convergent frequencies in both data setsat 10.5 Hz and 3.9 Hz. Plateaus and decreased variability of changes in slope were identified early in the post-injury phase. AI modeling identified early similarities in pre-operative and post-stimulation measurements through the patterns of peaks with similarities on postoperative day 10 and similarities in the valleys on postoperative day 17.Histologic specimens identified increased degrees of apoptosis and cellular death in the non-stimulated control compared to the stimulated swine. Similarly, the immediately stimulated swine had less apoptosis and increased histologic viability at the site of injury compared to the two-day delayed stimulation swine. There were increased levels of NSE noted in the stimulated swine at the site of injury compared to non-injured sites and the control swine. Conclusions Cortical function was appropriately measured through induction sensors and shielding in the form of a helmet and electromagnetic field channels. Early stimulation resulted in the early and durable recovery of neuronal circuit-driven electromagnetic field patterns. Histology identified increased viability of neurons with fewer apoptotic neurons and glial cells in stimulated swine with early stimulation identifying the best effect compared to a non-stimulated subject. This recovery identifies change and recovery at the circuit, cellular, and subcellular levels that potentiatethe need for further study of EMF modulation as a treatment modality in neurological disorders.

Single Event Upset Study of 22 nm Fully Depleted Silicon-on-Insulator Static Random Access Memory with Charge Sharing Effect.

In this paper, the single event effect of 6T-SRAM is simulated at circuit level and device level based on a 22 nm fully depleted silicon-on-insulator (FDSOI) process, and the effects of charge sharing and bipolar amplification are considered in device-level simulation. The results demonstrate that, under the combined influence of these two effects, the circuit's upset threshold and critical charge decreased by 15.4% and 23.5%, respectively. This indicates that the charge sharing effect exacerbates the single event effects. By analyzing the incident conditions of two different incident radius particles, it is concluded that the particles with a smaller incident radius have a worse impact on the SRAM circuit, and are more likely to cause the single event upset in the circuit, indicating that the ionization distribution generated by the incident particle affects the charge collection.

The locus coeruleus-dorsal hippocampal CA1 pathway is involved in depression-induced perioperative neurocognitive disorders in adult mice.

Patients undergoing surgical anesthesia increasingly suffer from preoperative depression. Clinical studies have shown that depression is a risk factor for perioperative neurocognitive disorders (PNDs) in elder patients. However, the underlying mechanism, especially at the neural circuit level, remains poorly understood. Right carotid artery separation under sevoflurane and chronic social defeat stress (CSDS) in adult mice were used to establish surgical anesthesia and chronic depression models. Cognitive function was assessed by the Y maze and novel object recognition tests. A chemogenetic approach was used to modulate the locus coeruleus-dorsal hippocampal CA1 (LC-dCA1) circuit. Hippocampal synaptic alterations were evaluated by Golgi staining and whole-cell patch clamp recording. We found that CSDS induced synaptic impairments in dorsal hippocampal CA1 pyramidal neurons and cognitive deficits in adult mice after surgery under sevoflurane. Chemogenetic activation of the LC-dCA1 pathway significantly alleviated the CSDS-induced synaptic impairments and cognitive dysfunction. On the contrary, inhibition of this pathway could mimic CSDS-induced deficits. Furthermore, we showed that dopamine played an important role in CSDS-induced PNDs in adult mice after surgery/sevoflurane. Overall, our results have demonstrated a vital role for the LC-dCA1 pathway in CSDS-induced PNDs in adult mice undergoing surgery with sevoflurane anesthesia.

An optical scheme of on-chip matrixing by phase-change based tunable weighting of photonic tensor unit

In this paper, we propose an optical scheme of on-chip matrixing for matrix-vector multiplications (MVMs) by configuring each matrix element into a photonic tensor processing unit (TPU) with wavelength division multiplexing and the actively tunable weighting for scalar multiplication. The low loss chalcogenide phase change material of Ge2Sb2Se4Te (GSST) is employed and modeled with intermediate states for multilevel tunable weighting of each TPU. The dynamic electro-thermal process of GSST phase transition using ITO for electrical heating is simulated and well confirms the switchable weighting of TPUs. Simulation results reveal that a 7 V voltage pulse of 500 ns duration followed by a 9 V voltage pulse of 1500 ns duration and another pulse of 14 V voltage and 500 ns duration can set data value of ‘0’ and ‘1’ for each TPU. Taking a set of incident light with varied wavelengths and powers as input vectors, the quantized MVM outputs of photocurrents with varied amplitude are obtained after photoelectric conversion. Finally, the photonic integrated circuit level simulations by Lumerical INTERCONNECT perfectly confirm our scheme of on-chip matrixing for optical MVMs and computing.

Predictive Mouse Ultrasonic Vocalization Sequences: Uncovering Behavioral Significance, Auditory Cortex Neuronal Preferences, and Social-Experience-Driven Plasticity.

Mouse ultrasonic vocalizations (USVs) contain predictable sequential structures like bird songs and speech. Neural representation of USVs in the mouse primary auditory cortex (Au1) and its plasticity with experience has been largely studied with single-syllables or dyads, without using the predictability in USV sequences. Studies using playback of USV sequences have used randomly selected sequences from numerous possibilities. The current study uses mutual information to obtain context-specific natural sequences (NSeqs) of USV syllables capturing the observed predictability in male USVs in different contexts of social interaction with females. Behavioral and physiological significance of NSeqs over random sequences (RSeqs) lacking predictability were examined. Female mice, never having the social experience of being exposed to males, showed higher selectivity for NSeqs behaviorally and at cellular levels probed by expression of immediate early gene c-fos in Au1. The Au1 supragranular single units also showed higher selectivity to NSeqs over RSeqs. Social-experience-driven plasticity in encoding NSeqs and RSeqs in adult females was probed by examining neural selectivities to the same sequences before and after the above social experience. Single units showed enhanced selectivity for NSeqs over RSeqs after the social experience. Further, using two-photon Ca2+ imaging, we observed social experience-dependent changes in the selectivity of sequences of excitatory and somatostatin-positive inhibitory neurons but not parvalbumin-positive inhibitory neurons of Au1. Using optogenetics, somatostatin-positive neurons were identified as a possible mediator of the observed social-experience-driven plasticity. Our study uncovers the importance of predictive sequences and introduces mouse USVs as a promising model to study context-dependent speech like communications.SIGNIFICANCE STATEMENT Humans need to detect patterns in the sensory world. For instance, speech is meaningful sequences of acoustic tokens easily differentiated from random ordered tokens. The structure derives from the predictability of the tokens. Similarly, mouse vocalization sequences have predictability and undergo context-dependent modulation. Our work investigated whether mice differentiate such informative predictable sequences (NSeqs) of communicative significance from RSeqs at the behavioral, molecular, and neuronal levels. Following a social experience in which NSeqs occur as a crucial component, mouse auditory cortical neurons become more sensitive to differences between NSeqs and RSeqs, although preference for individual tokens is unchanged. Thus, speech-like communication and its dysfunction may be studied in circuit, cellular, and molecular levels in mice.