Incoming Signal Research Articles

Microscale Chiral Rectennas for Energy Harvesting.

Wireless radiofrequency rectifiers have the potential to power the billions of "Internet of Things" (IoT) devices currently in use by effectively harnessing ambient electromagnetic radiation. However, the current technology relies on the implementation of rectifiers based on Schottky diodes, which exhibit limited capabilities for high-frequency and low-power applications. Consequently, they require an antenna to capture the incoming signal and amplify the input power, thereby limiting the possibility of miniaturizing devices to the millimeter scale. Here, the authors report wireless rectification at the GHz range in a microscale device built on single chiral tellurium with extremely low input powers. By studying the crystal symmetry and the temperature dependence of the rectification, the authors demonstrate that its origin is the intrinsic nonlinear conductivity of the material. Additionally, the unprecedented ability to modulate the rectification output by an electrostatic gate is shown. These results open the path to developing tuneable microscale wireless rectifiers with a single material.

Artificial intelligence in accounting

Artificial Intelligence (AI) technologies open up broad horizons for enhancing business efficiency and advancing various professional domains, boosting their productivity and compe­titiveness. There is an active exploration of approaches to incorporating AI technologies in the accounting sphere, promising a seamless transition from human to machine involvement. The aim of this article is to summarize the acquired experience, identify perspectives, constraints, and risks associated with the use of AI technologies in the professional activities of accountants. The research is based on the hypothesis that widespread use of AI in the professional activity of an accountant with an insufficient level of professional skepticism and caution carries significant threats and risks for both the accountant and the business as a whole. Scientific search methods, comparative and critical analysis, theoretical generalization, and synthesis were used. A prerequisite for imple­menting AI technologies in accounting is expert information systems and ERP systems. The analysis of AI technology implementation experience in various industries demonstrates their relevance in the accounting field for performing routine tasks (automated recognition of primary documents, processing incoming signals, and other standard operations with a simultaneous reduction in the probability of errors), analyzing large datasets, and providing information support for decision-making (pro­ces­sing business data and regulatory docu­ments), training professionals, and organi­zing internal and external communication (parti­cularly between humans and machines). Identi­fied potential risks include breaches of privacy and data security, misinterpretation of output data, and the disregard of activity context, external and internal environments, especially due to the absence of emotional intelligence, which influences the trust level in integrated information systems. The requirement for the application of professional assessments and judgments, mandated by regulatory documents, limits the scope of AI technology utilization in accounting. Future research should focus on exploring the possibilities of widespread integ­ration of AI technologies in information systems for accounting and improving legislation based on the principle of risk assessment.

Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers

Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an innovative approach that leverages Transformer networks, initially designed for natural language processing, to address the challenges of efficient AMR. Our Transformer network architecture is designed with the mindset of real-time edge computing on IoT devices. Four tokenization techniques are proposed and explored for creating proper embeddings of RF signals, specifically focusing on overcoming the limitations related to the model size often encountered in IoT scenarios. Extensive experiments reveal that our proposed method outperformed advanced deep learning techniques, achieving the highest recognition accuracy. Notably, our model achieved an accuracy of 65.75 on the RML2016 and 65.80 on the CSPB.ML.2018+ dataset.

Digital Being: social media and the predictive mind.

Social media is implicated today in an array of mental health concerns. While concerns around social media have become mainstream, little is known about the specific cognitive mechanisms underlying the correlations seen in these studies or why we find it so hard to stop engaging with these platforms when things obviously begin to deteriorate for us. New advances in computational neuroscience, however, are now poised to shed light on this matter. In this paper, we approach the phenomenon of social media addiction through the lens of the active inference framework. According to this framework, predictive agents like us use a 'generative model' of the world to predict our own incoming sense data and act to minimize any discrepancy between the prediction and incoming signal (prediction error). In order to live well and be able to act effectively to minimize prediction error, it is vital that agents like us have a generative model, which not only accurately reflects the regularities of our complex environment but is also flexible and dynamic and able to stay accurate in volatile and turbulent circumstances. In this paper, we propose that some social media platforms are a spectacularly effective way of warping an agent's generative model and of arresting the model's ability to flexibly track and adapt to changes in the environment. We go on to investigate cases of digital tech, which do not have these adverse effects and suggest-based on the active inference framework-some ways to understand why some forms of digital technology pose these risks, while others do not.

Adjusting listening effort relative to expected value of listening

Listening effort is a commonly reported difficulty among those who have hearing loss. A person’s ability to engage or disengage mental resources at strategic times could be an important signature of a person’s capacity to guard against wasted effort. This experiment gave listeners an opportunity to voluntarily reduce effort at specific moments based on the expected value of the incoming signal. First, sentences presented in the clear were immediately repeated, inviting reduced attention to the first presentation. In a second condition, the immediate repetition was randomly dropped on some trials, inviting increased vigilance to the first presentation. In a third condition, the first sentence was presented with degraded quality, inviting increased attention to the repetition to disambiguate misperceived words. Pupillometry was used as an index of moment-to-moment changes in listening effort. Data showed elevated pupil dilations linked in time with the disambiguated words and diminished pupil size specifically when there was less value in attending to the signal. These results support the need to expand the concept of listening effort beyond a “more” or “less” framework, toward a framework of efficiency.

Effects of formant peak flattening on vowel perception: A larger-scale web-based experiment

Ito et al. reported that suppressing the first or the second formant in Japanese synthesized vowels did not importantly change listener perception. However, quantitative analyses of data collected in the English (Nenadić et al., 2020) and the Serbian language (Nenadić et al., 2023) have shown that formant suppression increases response entropy between participants (i.e., decreases agreement in vowel identity). Previous studies never tested more than fifteen participants, but it was noted that certain listeners gave different responses to a manipulated synthesized vowel in comparison to its original version more often than others. We tested 118 native monolingual speakers of Serbian language (87% female, 13% male; age 18 to 44, M = 21.07, SD = 4.71) in an online replication of the experiment. The results again show that listeners agree less about vowel identity for stimuli with suppressed formants. Certain listeners again tended to change their response to the manipulated version of the vowel more often than others. Importantly, this tendency did not correlate with their mean response latency. We discuss possible causes for these findings, including differences in listener strategies and time requirements of a more careful processing of the incoming signal.

Improving a method for non-coherent processing of signals by a network of two small-sized radars for detecting a stealth unmanned aerial vehicle

The object of this study is the process of detecting stealth unmanned aerial vehicles by a network of two small-sized radars with incoherent signal processing. The main hypothesis of the study assumed that combining two small-sized radars into a network could improve the quality of detection of stealth unmanned aerial vehicles with incoherent signal processing. The improved method for detecting a stealth unmanned aerial vehicle by a network of two small-sized radars with incoherent signal processing, unlike the known ones, provides for the following: – synchronous inspection of the airspace by small-sized radars; – sounding signal emission by each small-sized radar; – reception of echo signals from a stealth unmanned aerial vehicle by two small-sized radars; – coordinated filtering of incoming echo signals (separation of echo signals); – quadratic detection of signals at the outputs of matched filters; – summation of the detected signals at the outputs of the matched filters; – summation of the outputs of adders of two small-sized radars. The scheme of a stealth unmanned aerial vehicle detector is presented, optimal according to the Neumann-Pearson criterion, with incoherent signal processing. The quality of detection of a stealth unmanned aerial vehicle by a network of two small-sized radars with incoherent signal processing was evaluated. It was found that with incoherent processing, the gain in the value of the conditional probability of correct detection is on average from 19 % to 26 %, depending on the value of the signal-to-noise ratio. The gain in the value of the conditional probability of correct detection is greater at low values of the signal-to-noise ratio. At the same time, the gain in signal-to-noise value is more significant with coherent signal processing than with non-coherent signal processing by a network of two small-sized radars

Identification of common stria vascularis cellular alteration in sensorineural hearing loss based on ScRNA-seq.

The stria vascularis (SV), located in the lateral wall of the cochlea, maintains cochlear fluid homeostasis and mechanoelectrical transduction (MET) activity required for sound wave conduction. The pathogenesis of a number of human inheritable deafness syndromes, age related hearing loss, drug-induced ototoxicity and noise-induced hearing loss results from the morphological changes and functional impairments in the development of the SV. In this study, we investigate the implications of intercellular communication within the SV in the pathogenesis of sensorineural hearing loss (SNHL). We aim to identify commonly regulated signaling pathways using publicly available single-cell transcriptomic sequencing (scRNA-seq) datasets. We analyzed scRNA-seq data, which was derived from studying the cochlear SV in mice with SNHL compared to normal adult mice. After quality control and filtering, we obtained the major cellular components of the mouse cochlear SV and integrated the data. Using Seurat's FindAllMarkers and FindMarkers packages, we searched for novel conservative genes and differential genes. We employed KEGG and GSEA to identify molecular pathways that are commonly altered among different types of SNHL. We utilized pySCENIC to discover new specific regulatory factors in SV subpopulation cells. With the help of CellChat, we identified changes in subpopulation cells showing similar trends across different SNHL types and their alterations in intercellular communication pathways. Through the analysis of the integrated data, we discovered new conserved genes to SV specific cells and identified common downregulated pathways in three types of SNHL. The enriched genes for these pathways showing similar trends are primarily associated with the Electron Transport Chain, related to mitochondrial energy metabolism. Using the CellChat package, we further found that there are shared pathways in the incoming signaling of specific intermediate cells in SNHL, and these pathways have common upstream regulatory transcription factor of Nfe2l2. Combining the results from pySCENIC and CellChat, we predicted the transcription factor Nfe2l2 as an upstream regulatory factor for multiple shared cellular pathways in IC. Additionally, it serves as an upstream factor for several genes within the Electron Transport Chain. Our bioinformatics analysis has revealed that downregulation of the mitochondrial electron transport chain have been observed in various conditions of SNHL. E2f1, Esrrb, Runx1, Yy1, and Gata2 could serve as novel important common TFs regulating the electron transport chain. Adm has emerged as a potential new marker gene for intermediate cells, while Itgb5 and Tesc show promise as potential new marker genes for marginal cells in the SV. These findings offer a new perspective on SV lesions in SNHL and provide additional theoretical evidence for the same drug treatment and prevention of different pathologies of SNHL.

Concurrent M-QAM transmission performance assessment in a combined four SOA-MZIs arrangement

In this paper, the utilization of four semiconductor optical amplifier Mach-Zehnder interferometers (SOA-MZIs) is explored to achieve concurrent M-QAM (M-ary Quadrature Amplitude Modulation) transmission. Utilizing an actively mode-locked laser, we generate pulses with a duration of 0.5 ps at a repetition rate of 20 GHz, serving as our sampling source. The incoming M-QAM signals, spanning various frequencies, undergo down-mixing at the SOA-MZIs combined exit, resulting in diverse output frequencies by respecting the bandpass sampling procedures. The unparalleled simultaneous M-QAM transmission is assessed through critical metrics such as error vector magnitude (EVM) and bit error rate (BER). The SOA-MZI conversion efficiency (CE) exhibits a notable transition, ranging from 90 % at 76 GHz to 68 % at 84 GHz based on 1024-QAM data. The concurrent down-mixed signals showcase EVM values of 3, 2, 1, and 0.25 % for 16-, 64-, 256-, and 1024-QAM, respectively, at the first range of output frequencies. Besides, an exceptional BER value of 93×10−6 is attained at a symbol rate of 0.25 GSymb/s at 84 GHz for the 1024-QAM replica. A comparative analysis reveals that under identical parameter settings, the proposed Gaussian filter expounds lower EVM values compared to the root raised cosine (RRC) filter. These results highlight the success of the novel experimental approach employing SOA-MZIs for simultaneous down-mixing of four M-QAM carriers with varying levels, showcasing outstanding performance outcomes.

Integrated analysis reveals the dysfunction of intercellular communication and metabolic signals in dilated cardiomyopathy

AimsDilated cardiomyopathy refers to a heart muscle condition characterized by structural and functional irregularities in the myocardium that are not related to ischemia. Due to diverse etiologies such as genetic mutations, infections, and exposure to toxins, dilated cardiomyopathy can lead to substantial morbidity and mortality despite advances in the management of heart failure in dilated cardiomyopathy patients. We sought to analyze the characteristics of cell-cell communication and the metabolic signaling pathways in dilated cardiomyopathy. Methods and resultsThe single-nucleus sequencing data of left ventricle samples were acquired from two donor datasets and two dilated cardiomyopathy datasets. Three dilated cardiomyopathy bulk-sequencing datasets were included to determine the shared dilated cardiomyopathy-specific alterations in differentially expressed genes and signaling pathways. Using “CellChat,” we analyzed intercellular communication to grasp how cell clusters interact and to map out the impaired signaling pathways in both donor and dilated cardiomyopathy conditions. Gene set enrichment analysis was applied to compare the metabolic signaling before and after dilated cardiomyopathy. We showcased how cell clusters exhibited abnormal cell-to-cell signaling transduction and how each cell type displayed dysfunctional metabolic signaling pathways through the integration of various datasets. The crucial ligand-receptor signaling contributing to outgoing or incoming signaling of dilated cardiomyopathy was identified in a cell-type dependent way, and the cell-specific metabolic alterations in glucose, lipid and amino acid were determined. The expression of gene pairs in BMP and NOTCH signal, as well as the gene expression in the arginine metabolism was validated. ConclusionsWe reveal the key signals and metabolic pathways for dilated cardiomyopathy adaptation and maintenance, providing potential targets for dilated cardiomyopathy interference.

Performance Analysis and Simulation of IRS-Aided Wireless Networks Communication

This paper introduces the novel IRS-based Optimal Relay Selection (ORS-IRS) method, aimed at analyzing the performance of wireless communication systems with an emphasis on symmetry. The ORS-IRS approach presents an innovative communication algorithm that seamlessly integrates Intelligent Reflecting Surfaces (IRS) with relay selection techniques. Through adaptive adjustments of reflection coefficients, IRS elements efficiently manipulate incoming signals, fostering symmetry in signal strength enhancement and latency reduction for improved signal delivery to the intended destination. This symmetrical optimization in channel capacity and transmission power ensures reliable data transmission with low latency, achieved through the seamless integration of IRS and relay selection techniques. In contrast, the Cell-Free Massive MIMO (CF-M-MIMO), with its decentralized architecture, excels in serving a larger user base and attaining remarkable capacity gains, showcasing a different dimension of symmetry. The Decode-and-Forward (DF) relaying approach demonstrates its potential in enhancing signal reliability across extended distances, contributing to the overall symmetry of the comparative analysis. This comprehensive evaluation provides valuable insights into selecting appropriate transmission strategies, particularly for applications that demand high capacity and reliability in the design of modern wireless communication systems with a symmetrical focus.

Maximum likelihood based direction estimation for noncircular signals

In the direction estimation for the signals incident on a sensor array, maximum likelihood (ML) based methods can provide superior performance than subspace based ones such as the multiple signal classification (MUSIC). When the incoming signals are noncircular the exploitation of the property can allow us to improve estimation performance. Based on the deterministic ML criterion, a direction estimation method for strictly noncircular signals is proposed that can outperform the conventional alternating maximization (AM) with no use of noncircularity. Using a generalized Gaussian probability density function, we approach the ML estimation, which is formulated as a nonlinear multidimensional problem. The application of the alternating projection to the multidimensional problem leads to the maximization of an objective function of two variables associated with the direction and the initial phase of a noncircular signal. Theoretically optimizing the phase variable, we obtain the maximum through one-dimensional search with respect to the direction variable only. The complexity of the proposed noncircular AM (NC-AM) is far less than that of the existing ML based method, noncircular decoupled maximization (NC-DM). Moreover, simulation results demonstrate that NC-AM outperforms NC-DM as well as the noncircular MUSIC.

DEVELOPMENT OF OBSTACLE BYPASSING METHOD OF ECO-AGROROBOT

In this paper, the movement of a laboratory model of a wheeled eco-acrorobot equipped with photovoltaic panels and performing agricultural work is investigated. Having an active role of eco-agrorobot within the framework of the smart village project the linear movement, maneuverability and capability to bypass the obstacles encountered during the movement of the eco-acrorobot are being investigated. The eco-agrorobot is powered by solar panels installed on it. Eco-agrorobot is supposed to be driven by two asynchronous electric motors controlled by a frequency converter installed on front gears. The rear wheels are "royal" (passive) type and do not create traction force. The movement of the eco-agrorobot is controlled by the software. The eco-agrorobot performs agricultural work by moving along a predetermined trajectory in the field previously set to its memory. The ability of the eco-agrorobot to bypass the obstacles encountered during its movement is achieved via two leading wheels controlled by obstacle detectors installed on the robot. The algorithm of maneuvering and bypassing obstacles by the eco-agrorobot was developed, the calculation of the parameters of the solar panels and the selection of other devices were applied. In the presented laboratory model of the eco-agrorobot, the linear movement and maneuverability of the eco-agrorobot is achieved via two DC motors controlled by four ultrasonic sensors installed on the robot. The robot's trajectory is determined by processing the incoming signals through the Arduino microprocessor. Keywords: frequency control, regulated electric drive, wheeled robot, ultrasonic sensor, solar panel, smart village.

Magneto-optic interferometric system: Exploring building blocks and subcircuits

Recent developments in the standardization of magnetic field generators have been considered for optoelectronic industrial-scale applications. In this work, a strategy to incorporate all optical components that can be used to implement a broad range of functions for communications applications is presented. We propose a circuit design for a magneto-optic interferometric system based on building blocks and subcircuits consisting of an interferometer with magneto-optic phase shifters and a magnetic field generator. The paper shows the building blocks based on elementary 2 × 2 photonics coupling components. We demonstrate that such a design can be configured as a tunable system based on the phase shift experienced by the incoming signals from two input waveguides and their transitions.

Analysis of a Low-Power OTA-Based Neural Amplifier Design for EEG Signal Acquisition

This paper presents the design of an efficient operational transconductance amplifier (OTA) to be explicitly used for electroencephalogram (EEG) signal acquisition in neural amplifiers (NAs). The central objective of this study revolves around addressing the fundamental compromise between noise and power in the design of NAs. The overarching goal is to effectively mitigate this trade-off by introducing novel approaches. The proposed design’s novelty lies in utilizing an adaptive biasing technique instead of the traditional current mirror biasing technique. Furthermore, the conventional input differential pair of the OTA is modified to employ a self cascode flipped voltage follower (SCFVF), which eventually reduces power consumption of the NA to 0.822 [Formula: see text]W. Additionally, notable improvements in noise performance are achieved and found to be 624.9 nV/[Formula: see text] at 1 Hz. The gain and bandwidth range of the input low-noise amplifier is 54.2 dB and 2.8 Hz–207 Hz, respectively, which effectively amplifies low-amplitude noisy incoming signals, addressing the specific requirements of EEG signal acquisition.

An 18–28 GHz dual-mode down-converter IC for 5G applications

Emerging spectrum trends require a higher integration of 5G New Radio Frequency Range 1 (FR1) and Frequency Range 2 (FR2) bands to enhance the availability of spectrum and spectrum-sharing opportunities. To enable the reception of both FR1 and FR2 bands in a seamless hardware entity, we propose combining homodyne and heterodyne architectures. This necessitates the incorporation of a down-converter module that transfers the incoming signals from FR2 bands down to FR1, ensuring compatibility with an FR1 direct-conversion receiver (DCR) for the final signal reception. The primary focus of this paper is the design and implementation of the required integrated down-converter. The module includes an integrated balun, a low-noise amplifier (LNA) with a bypass mode, a dual-mode mixer, and an intermediate frequency (IF) amplifier. The introduced bypass mode helps to further elevate the linearity performance compared to the nominal mode. The bypass mode is designed for joint communication and sensing operation to avoid the compression of the receiver. This work also incorporates a local oscillator (LO) signal distribution network with phase tuning elements using a mixed-signal approach. The circuit is implemented in a 22-nm CMOS process, and the active die area is 0.6 mm2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {mm}^\ ext {2}$$\\end{document}. The measurements demonstrate that the implemented chip can efficiently perform the required frequency conversion over a wide frequency range of 18–28 GHz. Conversion gain of 4.5–7.5 dB, noise figure of 15–19.7 dB, 1 dB compression point (IP1dB) of − 16 to − 10 dBm, and input third-order intercept point (IIP3) of − 5 to 0 dBm are achieved. The measured IP1 dB and IIP3 for the bypass mode are +0.5 to +4.5 dBm and +8.5 to +10 dBm, respectively.

Ocean Bottom Seismometer Clock Correction using Ambient Seismic Noise

Ocean-bottom seismometers (OBSs) are equipped with seismic sensors that record acoustic and seismic events at the seafloor, which makes them suitable for investigating tectonic structures capable of generating earthquakes offshore. One critical parameter to obtain accurate earthquake locations is the absolute time of the incoming seismic signals recorded by the OBSs. It is, however, not possible to synchronize the internal clocks of the OBSs with a known reference time, given that GNSS signals are unable to reach the instrument at the sea bottom. To address this issue, here we introduce a new method to synchronize the clocks of large-scale OBS deployments. Our approach relies on the theoretical time-symmetry of time-lapse (averaged) crosscorrelations of ambient seismic noise. Deviations from symmetry are attributed to clock errors. This implies that the recovered clock errors will be obscured by lapse crosscorrelations' deviations from symmetry that are not due to clock errors. Non-uniform surface wave illumination patterns are arguably the most notable source which breaks the time symmetry. Using field data, we demonstrate that the adverse effects of non-uniform illumination patterns on the recovered clock errors can be mitigated by means of a weighted least-squares inversion that is based on station-station distances. In addition, our methodology permits the recovery of timing errors at the time of deployment of the OBSs. This error can be attributed to either: i) a wrong initial time synchronization of the OBS or ii) a timing error induced by changing temperature and pressure conditions while the OBS is sunk to the ocean floor. The methodology is implemented in an open-source Python package named OCloC, and we applied it to the OBS recordings acquired in the context of the IMAGE project in and around Reykjanes, Iceland. As expected, most OBSs suffered from clock drift. Surprisingly, we found incurred timing errors at the time of deployment for most of the OBSs.

Towards Energy-Efficient Spiking Neural Networks: A Robust Hybrid CMOS-Memristive Accelerator

Spiking Neural Networks (SNNs) are energy-efficient artificial neural network models that can carry out data-intensive applications. Energy consumption, latency, and memory bottleneck are some of the major issues that arise in machine learning applications due to their data-demanding nature. Memristor-enabled Computing-In-Memory (CIM) architectures have been able to tackle the memory wall issue, eliminating the energy and time-consuming movement of data. In this work we develop a scalable CIM-based SNN architecture with our fabricated two-layer memristor crossbar array. In addition to having an enhanced heat dissipation capability, our memristor exhibits substantial enhancement of 10% to 66% in design area, power and latency compared to state-of-the-art memristors. This design incorporates an inter-spike interval (ISI) encoding scheme due to its high information density to convert the incoming input signals into spikes. Furthermore, we include a time-to-first-spike (TTFS) based output processing stage for its energy-efficiency to carry out the final classification. With the combination of ISI, CIM and TTFS, this network has a competitive inference speed of 2μs/image and can successfully classify handwritten digits with 2.9mW of power and 2.51pJ energy per spike. The proposed architecture with the ISI encoding scheme can achieve ∼10% higher accuracy than those of other encoding schemes in the MNIST dataset.

Investigation and optimisation of a lithium-drift silicon detector using Si–Li structure and bidirectional diffusion and drift techniques

Abstract The research relevance is predefined by the continuous development and improvement of radiation analysis methods and the need for more efficient and accurate detectors for various applications. This research may improve the sensitivity and resolution of Si(Li) detectors, which is important for scientific and industrial research as well as radiation safety monitoring. The research aims to analyse and improve the performance of a Si(Li) lithium-drift silicon detector. The methods used include an analytical method, classification method, functional method, statistical method, synthesis method and others. The results of the two-sided observation of lithium diffusion in silicon monocrystals provided valuable information about the characteristics of the process and its dependence on the method of silicon production. A large-diameter detector detection mode was found to be important for optimising the production of such detectors. The diffusion process in monocrystalline silicon produced by the shadowless zone melting method is relatively fast. This means that lithium ions penetrate the material rapidly and spread evenly throughout its volume. This fast diffusion process can be useful for detectors that need to respond quickly to incoming signals. It was found that in monocrystalline silicon produced by the Czochralski method, there is a delayed penetration of lithium ions.

“I’m a fish!” Deepening receptivity to neurodiversity: a neuroscientifically informed integration of psychoanalytic psychotherapy, reciprocal prediction, and mindfulness

ABSTRACT Receptivity to our patients’ experience is a vital aspect of the psychoanalytic endeavor. As we receive incoming transmissions, we resonate with what is active in the patient. We hope to then jointly metabolize the experience. When we meet neurodiversity, realms of experience emerge that may elude us. Precipitously formulated ideas in the therapist, based on a neurotypical frame of reference, can impinge upon the discovery of our patients’ authentic world. How do we open ourselves to receive their true experience? This clinical narrative tracks the psychoanalytic travels of an individual who identifies as neurodivergent, who helped the therapist learn to deepen receptivity by dipping into a less differentiated place to follow the here-and-now experience from the bottom up. Throughout the journey, interweaving neuroscientific and psychoanalytic perspectives offered a powerful matrix from which an enriched understanding of our process could emerge. Psychoanalytic concepts including evenly suspended attention, unconscious-to-unconscious communication, alpha function, reverie, and negative capability are explored alongside neuroscientific insights into the stress response, mirror neurons, and the default mode network. A predictive coding lens introduced a view of the therapeutic exchange as a continuous reciprocal prediction, evoking the hypothesis that deepening receptivity required opening awareness to incoming signals and lessening the hold of prior predictions. To bring greater therapist awareness to the present moment and lessen the influence of self-referential evaluation, neuroscientifically-informed reflections also inspired the practice of mindfulness. Subsequent developments suggest that these approaches helped deepen receptivity to the experiences being communicated, leading to new understandings with transformative potential.