Circuit Characteristics Research Articles

Significance of gender, brain region and EEG band complexity analysis for Parkinson's disease classification using recurrence plots and machine learning algorithms.

Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment of the disease progression and reflects these complex patterns. This study focuses on the non-linear analysis of resting state EEG signals in PD, with a gender-specific, brain region-specific, and EEG band-specific approach, utilizing recurrence plots (RPs) and machine learning (ML) algorithms for classification. For this an open EEG dataset consisting of 14 PD and 14 healthy (HC) subjects is utilized. Recurrence plots and cross-recurrence plots (CRPs) were constructed for each frequency band and brain region, extracting complexity measures such as determinism (DET) and entropy (ENT). The interpretability of the ML model decisions is investigated using explainability technique. The scattered distribution of points in RPs of male PD individuals reflects the complex and dynamic nature of abnormal brain function. Also, CRPs confirms the enhanced effect of Beta Gamma synchronization during PD in the Parietal region. Low DET and high ENT corresponds to the complex non-linear characteristics of EEG signals and brain neuronal circuits during PD condition in male subjects. The extracted recurrence features served as inputs to the ML models, which achieved high classification performance, across all the scenarios. This study demonstrates the potential of recurrence plot-based complexity analysis combined with machine learning for the gender-specific, region-specific, and band-specific assessment of EEG signals during resting state in Parkinson's disease.

Dual-mode Low Noise Large Range Magnetic Sensor based on Giant Magnetoimpedance Effect

Magnetic sensors are widely used in the fields of navigation, transportation, robotics, automation, and medical equipment, and the performance requirements of sensors are getting higher and higher. In this article, a bimodal magnetic sensor with two advantages of a large number of processes and low noise is proposed. The sensor consists of a 640μH core-wound inductor in series with a 100pF capacitor. When the external magnetic field changes, the magnetization state of the core in the inductor changes, the inductance value also changes, while the resonant frequency and impedance value of the sensor change with the magnetic field.<br>In this paper, the giant magnetic impedance characteristics of the RLC series circuit were analyzed, and the relationship between magnetic permeability, inductance value, and external magnetic field was established, and the series resonant frequency of the circuit was simulated to calculate the characteristics of the circuit with respect to the inductance variation.Then, two test systems were set up to test the resonance frequency versus magnetic field and the noise characteristics of the sensor.<br>In impedance mode, the effects of capacitance, drive signal frequency, and static bias magnetic field on the sensor noise floor were first analyzed to determine the optimal parameters of the sensor. When the series capacitance of the sensor is 100pF, the drive signal frequency is 1MHz, and the static bias magnetic field is 7.66Oe, the sensor has the optimal performance with an equivalent noise floor of about $200 p T / \sqrt{H z} @ 1 H z$,an impedance rate of change sensitivity of 160.6%/Oe, and a linear range of about 2Oe.In the frequency mode, the sensor operates linearly up to 25Oe, and using a logistic regression model to fit the resonant frequency to the magnetic field variation, the fit reaches 0.9974, and when the static bias magnetic field is about 7.66Oe, the sensor sensitivity is about 47kHz/Oe.<br>Not only that, with commercial components costing only ¥10 and excellent performance, the sensor has great market potential compared with other common different kinds of magnetic sensors on the market.

Optimization of microwave components using machine learning and rapid sensitivity analysis

Recent years have witnessed a tremendous popularity growth of optimization methods in high-frequency electronics, including microwave design. With the increasing complexity of passive microwave components, meticulous tuning of their geometry parameters has become imperative to fulfill demands imposed by the diverse application areas. More and more often, achieving the best possible performance requires global optimization. Unfortunately, global search is an intricate undertaking. To begin with, reliable assessment of microwave components involves electromagnetic (EM) analysis entailing significant CPU expenses. On the other hand, the most widely used nature-inspired algorithms require large numbers of system simulations to yield a satisfactory design. The associated costs are impractically high if not prohibitive. The use of available mitigation methods, primarily surrogate-based approaches, is impeded by dimensionality-related problems and the complexity in microwave circuit characteristics. This research introduces a procedure for expedited globalized parameter adjustment of microwave passives. The search process is embedded in a surrogate-assisted machine learning framework that operates in a dimensionality-restricted domain, spanned by the parameter space directions being of importance in terms of their effects on the circuit characteristic variability. These directions are established using a fast global sensitivity analysis procedure developed for this purpose. Domain confinement reduces the cost of surrogate model establishment and improves its predictive power. The global optimization phase is complemented by local tuning. Verification experiments demonstrate the remarkable efficacy of the presented approach and its advantages over the benchmark methods that include machine learning in full-dimensionality space and population-based metaheuristics.

Study of autotransformer bridges for measurements of the impedance parameters

The analysis of the existing impedance measurement methods showed that for the establishment of precision comparators that operate in a wide range of values in the audio frequency range, it is best to use transformer and autotransformer bridges. Autotransformer bridges are used for measurements in a wide range of the impedance values. The use of autotransformer bridges allows reducing the measurement error to 10–7–10–9. High metrological characteristics of transformer bridge circuits make it possible to use them in commercial devices and precision measuring equipment. Simple autotransformer bridges do not provide the opportunity to measure the impedance parameters in a wide range of values of the tangent of the loss angle (phase shift). For the synthesis of measuring circuits of bridges and their balancing, it is necessary to have a precision quadrature channel that will ensure high accuracy of the transmission coefficient both by phase and by module. The structures of universal autotransformer comparators and their properties are determined by two main factors: by the method of forming the source of the complex balancing signal and by the types of schemes for replacing the impedances of the compared objects. To determine ways to improve universal precision impedance comparators based on autotransformer bridges, it is necessary to develop and analyze mathematical models of universal comparators. The conducted theoretical analysis showed that in the process of comparison, it is possible to compare impedances with different substitution schemes with a direct reading of reactive and active parameters. By choosing the appropriate transmission direction, with a simple reconstruction of the measuring circuit, it is possible to compare two impedances with a parallel substitution scheme, two impedances with a series substitution scheme, or two impedances with a different substitution scheme. The obtained results made it possible to implement them in a universal autotransformer-comparator bridge.

Analysis of an Axial Field Hybrid Excitation Synchronous Generator

An axial field hybrid excitation synchronous generator (AF-HESG) is proposed for an independent power supply system, and its electromagnetic performance is studied in this paper. The distinguishing feature of the proposed generator is the addition of static magnetic bridges at both ends to place the field windings and the use of a sloping surface to increase the additional air-gap cross-sectional area. The advantage of the structure is that it achieves brushless excitation and improves the flux-regulation range. The structure and magnetic circuit characteristics are introduced in detail. Theoretical analysis of the flux-regulation principle is conducted by studying the relationship between field magnetomotive force, rotor reluctance, and air-gap flux density. Quantitative calculation is performed using a magnetomotive force (MMF)-specific permeance model, and the influence of the main parameters on the air-gap flux density and flux-regulation range is analyzed. Subsequently, magnetic field, no-load, and load characteristics are investigated through three-dimensional finite element analysis. The loss distribution is analyzed, and the temperature of the generator under rated conditions is simulated. Finally, a 30 kW, 1500 r/min prototype is developed and tested. The test results show good flux-regulation capability and stable voltage output performance of the proposed generator.

Hierarchical gradients of multiple timescales in the mammalian forebrain

Many anatomical and physiological features of cortical circuits, ranging from the biophysical properties of synapses to the connectivity patterns among different neuron types, exhibit consistent variation along the hierarchical axis from sensory to association areas. Notably, the temporal correlation of neural activity at rest, known as the intrinsic timescale, increases systematically along this hierarchy in both primates and rodents, analogous to the increasing scale and complexity of spatial receptive fields. However, how the timescales for task-related activity vary across brain regions and whether their hierarchical organization appears consistently across different mammalian species remain unexplored. Here, we show that both the intrinsic timescale and those of task-related activity follow a similar hierarchical gradient in the cortices of monkeys, rats, and mice. We also found that these timescales covary similarly in both the cortex and basal ganglia, whereas the timescales of thalamic activity are shorter than cortical timescales and do not conform to the hierarchical order predicted by their cortical projections. These results suggest that the hierarchical gradient of cortical timescales might represent a universal feature of intracortical circuits in the mammalian brain.

Design of a Magnetically Coupled Resonant Implantable Medical Device Wireless Power Transmission System

This paper systematically describes the design and optimisation strategy of wireless charging technology for implantable medical devices. Starting from the theoretical basis of magnetically coupled resonant wireless power transmission (MCR-WPT), an efficient transmission circuit is designed and the resonant circuit characteristics are analysed. Meanwhile, the effects of the outer diameter, the number of turns of the transmitting coil and the turns spacing on the magnetic flux density and the AC internal resistance are obtained through the principle analysis. The coil parameters are adjusted through simulation experiments to improve the transmission efficiency. Then, the safety and stability of the device are ensured by temperature-power double closed-loop control. Then the closed-loop phase-shift control strategy is introduced to ensure that the system can output power stably in complex environments. In this paper, a low-power, safe and high-efficiency wireless energy transmission system is built, which provides a comprehensive and in-depth technical reference for the development of wireless power supply technology for implantable medical devices.

The regulative role and mechanism of BNST in anxiety disorder.

Anxiety disorders, common yet impactful emotional disturbances, significantly affect physical and mental health globally. Many neuron circuits are associated with anxiety regulation like septo-hippocampal loop, amygdala(AMYG), bed nucleus of the stria terminalis (BNST), ventral hippocampus (vHPC), and brain regions like medial prefrontal cortex (mPFC). However, the concrete mechanism of anxiety disorder in BNST is relatively unknown. Recent research showed BNST plays a critical role in modulating anxiety owing to its anatomical location and special circuit characteristics, which are considered to be a hub in the limbic system regulating anxiety. BNST consists with multiple subregions, which can project separately into different brain regions and exert projecting independently to various brain regions with distinct regulatory effects. Moreover, multiple signal pathways in BNST are reported to play significant roles in regulating anxiety and stress behavior. This review briefly describes anxiety disorders and subdivisions and functions of BNST, focusing on the main neural circuits that serve as fundamental pathways in both the genesis and potential treatment of anxiety disorders and the molecular mechanism of BNST on anxiety. The complexity of structures and mechanisms has facilitated the development of imaging techniques. Innovative multimodal imaging techniques, such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), have non-invasively illuminated BNST activities and their functional connections with other brain areas. These methodologies provide a deeper understanding of how BNST responds to anxiety-inducing stimuli, offering invaluable insights into its complex role in anxiety regulation. The continued exploration of BNST in anxiety research promises not only to elucidate fundamental neurobiological mechanisms but also to foster advancements in clinical treatments for anxiety disorders.

Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory.

Our brain has remarkable computational power, generating sophisticated behaviors, storing memories over an individual's lifetime, and producing higher cognitive functions. However, little of our neuroscience knowledge covers the human brain. Is this organ truly unique, or is it a scaled version of the extensively studied rodent brain? Combining multicellular patch-clamp recording with expansion-based superresolution microscopy and full-scale modeling, we determined the cellular and microcircuit properties of the human hippocampal CA3 region, a fundamental circuit for memory storage. In contrast to neocortical networks, human hippocampal CA3 displayed sparse connectivity, providing a circuit architecture that maximizes associational power. Human synapses showed unique reliability, high precision, and long integration times, exhibiting both species- and circuit-specific properties. Together with expanded neuronal numbers, these circuit characteristics greatly enhanced the memory storage capacity of CA3. Our results reveal distinct microcircuit properties of the human hippocampus and begin to unravel the inner workings of our most complex organ.

A Time-over-Threshold asynchronous front-end in 28 nm CMOS for the readout of pixel detectors in extreme radiation environments

This work describes the design, in a 28 nm CMOS technology, of a front-end channel for the readout of pixel sensors in future particle accelerators. The channel being developed leverages the Time-Over-Threshold technique for the numerical conversion of the detector signal amplitude, and includes a low-noise charge sensitive amplifier featuring a compact gain stage architecture. The front-end circuit features an equivalent noise charge not exceeding 70 electrons r.m.s. for a detector capacitance of 50 fF, with a threshold dispersion of the order of 20 electrons r.m.s. A prototype chip including a matrix of 8x32 readout channels has been submitted for fabrication. The design of the front-end channel, together with the main simulation results, is discussed in the paper.

Circuits and mechanisms for TMS-induced corticospinal waves: Connecting sensitivity analysis to the network graph.

Transcranial magnetic stimulation (TMS) is a non-invasive, FDA-cleared treatment for neuropsychiatric disorders with broad potential for new applications, but the neural circuits that are engaged during TMS are still poorly understood. Recordings of neural activity from the corticospinal tract provide a direct readout of the response of motor cortex to TMS, and therefore a new opportunity to model neural circuit dynamics. The study goal was to use epidural recordings from the cervical spine of human subjects to develop a computational model of a motor cortical macrocolumn through which the mechanisms underlying the response to TMS, including direct and indirect waves, could be investigated. An in-depth sensitivity analysis was conducted to identify important pathways, and machine learning was used to identify common circuit features among these pathways. Sensitivity analysis identified neuron types that preferentially contributed to single corticospinal waves. Single wave preference could be predicted using the average connection probability of all possible paths between the activated neuron type and L5 pyramidal tract neurons (PTNs). For these activations, the total conduction delay of the shortest path to L5 PTNs determined the latency of the corticospinal wave. Finally, there were multiple neuron type activations that could preferentially modulate a particular corticospinal wave. The results support the hypothesis that different pathways of circuit activation contribute to different corticospinal waves with participation of both excitatory and inhibitory neurons. Moreover, activation of both afferents to the motor cortex as well as specific neuron types within the motor cortex initiated different I-waves, and the results were interpreted to propose the cortical origins of afferents that may give rise to certain I-waves. The methodology provides a workflow for performing computationally tractable sensitivity analyses on complex models and relating the results to the network structure to both identify and understand mechanisms underlying the response to acute stimulation.

Rbm24-mediated post-transcriptional regulation of skeletal and cardiac muscle development, function and regeneration.

RNA-binding proteins are critically involved in the post-transcriptional control of gene expression during embryonic development and in adult life, contributing to regulating cell differentiation and maintaining tissue homeostasis. Compared to the relatively well documented functions of transcription factors, the regulatory roles of RNA-binding proteins in muscle development and function remain largely elusive. However, deficiency of many RNA-binding proteins has been associated with muscular defects, neuromuscular disorders and heart diseases, such as myotonic dystrophy, amyotrophic lateral sclerosis, and cardiomyopathy. Rbm24 is highly conserved among vertebrates and is one of the best characterized RNA-binding proteins with crucial implication in the myogenic and cardiomyogenic programs. It presents the distinctive particularity of displaying highly restricted expression in both skeletal and cardiac muscles, with changes in subcellular localization during the process of differentiation. Functional analyses using different vertebrate models have clearly demonstrated its requirement for skeletal muscle differentiation and regeneration as well as for myocardium organization and cardiac function, by regulating the expression of both common and distinct target genes in these tissues. The challenge remains to decipher the dynamic feature of post-transcriptional circuits regulated by Rbm24 during skeletal myogenesis, cardiomyogenesis, and muscle repair. This review discusses current understanding of its function in striated muscles and its possible implication in human disease, with the aim of identifying research gaps for future investigation.

A Novel LCLC Parallel Resonant Circuit for High-Frequency Induction Heating Application

The application of induction heating power supply in the continuous production line of tinplate has garnered significant research and scholarly attention. However, the impedance matching of LC or CLC resonant circuits in the system lacks flexibility and is susceptible to overvoltage during startup. As a solution to the problem, a novel four-order LCLC parallel resonant circuit was proposed in this study for high-frequency induction heating power supply. By incorporating auxiliary inductors in parallel with CLC compensating capacitor branches, the induction heating system can operate reliably and achieve optimal load impedance matching. The equivalent circuit and mathematical model of the new resonant load were established, and the frequency characteristics of the circuit system were analyzed. Then, the parallel resonance characteristics of the new resonant circuit were comprehensively elucidated, including the quality factor, impedance characteristics, behavior of resonant current, and properties of voltage regulation. Finally, a simulation model of a high-frequency induction heating power supply was developed based on the proposed LCLC resonant circuit and compared with LC and CLC resonant circuits. The results demonstrated that the induction heating power supply system utilizing the proposed LCLC parallel resonant load exhibits superior parallel resonant characteristics, enhanced load impedance-matching flexibility, and improved output voltage stability when compared to traditional LC or CLC parallel resonant loads.

Diketopyrrolepyrrole-Based Copolymers: Achieving Equally Effective Ambipolar Charge Transport in Organic Field-Effect Transistors and High-Voltage Logic Circuits

A copolymer named PDPADPP, comprised of diketopyrrolopyrrole (DPP) and a cyano group connected by a vinylene spacer, undergoes synthesis through a palladium-catalyzed Suzuki coupling reaction. Its electrical behavior in organic field-effect transistors (OFETs) is investigated, revealing ambipolar transport characteristics. Initially, as-cast OFETs display low hole and electron mobilities of 0.016 and 0.004 cm2 V−1 s−1, respectively. However, upon thermal annealing at 240 °C, significant enhancement occurs, with balanced ambipolar transport and improved mobilities of 0.065 and 0.116 cm2 V−1 s−1 for holes and electrons, respectively. Furthermore, compact modeling utilizing the industry-standard BSIM is conducted to evaluate its potential in high-voltage logic circuits. The simulation results underscore the PDPADPP-based ambipolar transistor's excellent performance in logic applications, particularly after annealing at 240 °C, showcasing ideal circuit characteristics.

Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs

Efficient simulation of quantum circuits has become indispensable with the rapid development of quantum hardware. The primary simulation methods are based on state vectors and tensor networks. As the number of qubits and quantum gates grows larger in current quantum devices, traditional state-vector-based quantum circuit simulation methods prove inadequate due to the overwhelming size of the Hilbert space and extensive entanglement. Consequently, brutal force tensor network simulation algorithms become the only viable solution in such scenarios. The two main challenges faced in tensor network simulation algorithms are optimal contraction path finding and efficient execution on modern computing devices, with the latter determining the actual efficiency. In this study, we investigate the optimization of such tensor network simulations on modern GPUs and propose general optimization strategies from two aspects: computational efficiency and accuracy. First, we propose to transform critical Einstein summation operations into GEMM operations, leveraging the specific features of tensor network simulations to amplify the efficiency of GPUs. Second, by analyzing the data characteristics of quantum circuits, we employ extended precision to ensure the accuracy of simulation results and mixed precision to fully exploit the potential of GPUs, resulting in faster and more precise simulations. Our numerical experiments demonstrate that our approach can achieve a 3.96× reduction in verification time for random quantum circuit samples in the 18-cycle case of Sycamore, with sustained performance exceeding 21 TFLOPS on one A100. This method can be easily extended to the 20-cycle case, maintaining the same performance, accelerating by 12.5× compared to the state-of-the-art CPU-based results and 4.48–6.78× compared to the state-of-the-art GPU-based results reported in the literature. Furthermore, the strategies presented in this article hold general applicability for accelerating problems that can be tackled by contracting tensor networks such as graphical models and combinatorial optimizations.

Advancing renewable energy: A quadratic high-efficiency high gain step-up DC/DC converter

Presenting a new high-gain converter that incorporates a coupled inductor, this paper offers a range of advantages over existing designs. The circuit features a simple structure with two cascade semi-stages, two power switches operating in sync, and a coupled inductor. It provides an extensive output voltage range, ensures continuous input current with minimal ripple, maintains positive output voltage polarity, and follows a standard ground configuration. These features make it well-suited for various applications, particularly in photovoltaic systems. Additionally, the voltage stress across each power switch is significantly lower than that of other boost converters, allowing for the selection of power MOSFETs with reduced drain-source ON resistance to ensure high efficiency. This research presents comprehensive assessments of the steady-state and stress conditions as well as thorough comparisons with other similar converters operating in continuous conduction mode. Theoretical advantages of the proposed circuit design are further reinforced by experimental results obtained from a 200-W step-up 25–370 V arrangement.

DC characteristics of a dynamic vision sensor's photoreceptor circuit evaluated for event-based sensing in the mid-wave infrared

Forthcoming infrared event-based sensors will have utility in the space-based surveillance domain where they could potentially perform traditional sensing and tracking functions with significantly enhanced temporal resolution and reduced downstream datalink demands and power consumption. As a first step toward extending event-based sensing technology into the mid-wave infrared, a DC simulation of the conventional event-based sensor unit cell's photoreceptor circuit is performed, and the results are compared with measurements of a printed circuit board implementation of the same circuit to assess what design freedom is available to interface the photoreceptor with a mid-wave infrared photodetector. Detailed analysis of the circuit and measurements provides insight into which fundamental properties of the transistors drive the photoreceptor's dynamic range and demonstrates several characteristics that are relevant to mid-wave infrared sensing. These characteristics include the capability to produce a stable, low voltage bias on the infrared photodetector to maximize sensitivity, and that operating the circuit below room temperature increases the photoreceptor's dynamic range. Measurements show that even the simplest implementation of the photoreceptor circuit exhibits a dynamic range of logarithmic compression of 150 dB at room temperature. However, the dynamic range is ultimately limited by the mid-wave infrared photodetector's dark current activation energy, which is significantly lower than the threshold voltage (energy) of the photoreceptor's feedback transistor and, thus, there is an incentive for the photodetector's dark current to be diffusion-limited.

A new integrated modelling and control of ternary pumped storage hydro power generation for small signal stability studies

Pumped storage hydro power generation (PSHPG), a reliable renewable energy source with an energy storage feature, can impart efficient damping torque to power system oscillations to improve small signal stability with appropriate modelling and additional compensation through the governor. But ternary PSHPG (T-PSHPG) has the unique feature of hydraulic short circuit (HSC) mode, where both pumping and generating modes operate simultaneously. This study presents a novel fourth-order modified Heffron Phillips (MHP) model of T-PSHPG in coordination with a multi-band Power system stabilizer (MB-PSS) employing 2-way penstock, where primary penstock circulates water from the upper reservoir to the lower reservoir through the turbine and secondary penstock divides primary penstock, thereby creating short-circuit path through the pump. Also, this model includes additional reactive power droop control to maintain the voltage profile. A new state space matrix has been developed for coordinating T-PSHPG in HSC mode with MB-PSS. The control parameters of the proposed coordinated model are set by a new multi-objective differential evolution-improved particle swam optimization (DE-IPSO) algorithm. With variable load, random solar and wind source penetrations, it has been found that the proposed model can provide adequate damping actions. The sensitivity of the proposed model has been observed by varying critical model parameters by ±50 %. For all conditions, the settling time of speed variation is within 2.672 s to 5.951 s and the minimum damping ratio lies within 0.105 to 0.455. The minimum damping ratio is 0.32 and the settling time is found to be 3.1 s for system response subject to sudden solar power variation, which has been observed by shifting system eigenvalues toward the left half of the complex plane. Time and frequency domain parameters with sensitivity analysis predict system oscillations being damped effectively with consistency for the proposed modelling and control strategy. The results are verified in real-time with OPAL-RT real-time digital simulator. It has been justified by the present study that appropriate modelling of T-PSHPG along with coordinated control action provided by MB-PSS can handle critical power system oscillations efficiently and effective damping torque can be imparted in HSC mode.

A 2.4 GHz High-Efficiency Rectifier Circuit for Ambient Low Electromagnetic Power Harvesting.

A novel 2.4 GHz high-efficiency rectifier circuit suitable for working under very-low-input electromagnetic (EM) power conditions (-20 to -10 dBm) is proposed for typical indoor power harvesting. The circuit features a SMS7630 Schottky diode in a series with a voltage booster circuit at the front end and a direct-current (DC)-pass filter at the back end. The voltage booster circuit consists of an asymmetric coupled transmission line (CTL) and a high-impedance microstrip line (of 100 Ω instead of 50 Ω) to significantly increase the potential at the diode's input, thereby enabling the diode to operate effectively even in very-low-power environments. The experimental measurements show that the microwave direct-current (MW-DC) conversion efficiency of the rectifier circuit reaches 31.1% at a -20 dBm input power and 62.4% at a -10 dBm input power, representing a 7.4% improvement compared to that of the state of the art. Furthermore, the rectifier circuit successfully shifts the input power level corresponding to the peak rectification efficiency from 0 dBm down to -10 dBm. This design is a promising candidate for powering low-energy wireless sensors in typical indoor environments (e.g., the home or office) with low EM energy density.

Control of innate olfactory valence by segregated cortical amygdala circuits.

Animals exhibit innate behaviors that are stereotyped responses to specific evolutionarily relevant stimuli in the absence of prior learning or experience. These behaviors can be reduced to an axis of valence, whereby specific odors evoke approach or avoidance responses. The posterolateral cortical amygdala (plCoA) mediates innate attraction and aversion to odor. However, little is known about how this brain area gives rise to behaviors of opposing motivational valence. Here, we sought to define the circuit features of plCoA that give rise to innate attraction and aversion to odor. We characterized the physiology, gene expression, and projections of this structure, identifying a divergent, topographic organization that selectively controls innate attraction and avoidance to odor. First, we examined odor-evoked responses in these areas and found sparse encoding of odor identity, but not valence. We next considered a topographic organization and found that optogenetic stimulation of the anterior and posterior domains of plCoA elicits attraction and avoidance, respectively, suggesting a functional axis for valence. Using single cell and spatial RNA sequencing, we identified the molecular cell types in plCoA, revealing an anteroposterior gradient in cell types, whereby anterior glutamatergic neurons preferentially express VGluT2 and posterior neurons express VGluT1. Activation of these respective cell types recapitulates appetitive and aversive behaviors, and chemogenetic inhibition reveals partial necessity for responses to innate appetitive or aversive odors. Finally, we identified topographically organized circuits defined by projections, whereby anterior neurons preferentially project to medial amygdala, and posterior neurons preferentially project to nucleus accumbens, which are respectively sufficient and necessary for innate attraction and aversion. Together, these data advance our understanding of how the olfactory system generates stereotypic, hardwired attraction and avoidance, and supports a model whereby distinct, topographically distributed plCoA populations direct innate olfactory responses by signaling to divergent valence-specific targets, linking upstream olfactory identity to downstream valence behaviors, through a population code. This suggests a novel amygdala circuit motif in which valence encoding is represented not by the firing properties of individual neurons, but by population level identity encoding that is routed through divergent targets to mediate distinct behaviors of opposing appetitive and aversive responses.