The specification of intestinal stem cells (ISCs) during development is critical for maintaining intestinal homeostasis. However, the mechanisms underlying this process remain elusive. Here, by counting and tracing ISC in Drosophila pupal midgut, we show that ISCs are specified within a narrow 12-hour developmental window, with ~150 ISCs emerging from a pool of ~6000 intestinal epithelial cells. Single-cell sequencing revealed the involvement of Notch and Wnt signaling, with genetic experiments demonstrating that ISC specification requires both Notch suppression and Wnt activation. Furthermore, we showed that Wnt signaling is activated in discrete spatial domains, and Notch-mediated lateral inhibition specifies ISCs in these Wnt-active zones, achieving a ratio of ~1/40. Notably, Notch suppression also promoted the specification of Lgr5+ progenitors in the mouse embryonic intestine. Together, our data show that Wnt activation defines niches permissive for ISC fate, whereas Notch suppression licenses fate commitment, a spatiotemporal coordination conserved from insects to mammals.

Rolling bearings are crucial in rotating machinery, and combining natural and characteristic frequencies improves fault detection. However, natural frequencies face challenges like feature extraction difficulties and drift, necessitating resonance peak information supplementation. Existing methods for extracting resonance peaks often struggle with low quality, false peaks, and merging issues. This paper introduces a novel resonance peak extraction method based on auditory saliency (RESAS), inspired by the human auditory system. RESAS combines Gammatone filtering, multi-scale Gaussian filtering, and lateral inhibition to simulate auditory attention and efficiently extract resonance peaks. A resonance peak saliency map(RPSP) is generated, from which features are extracted and used as input to an improved random forest model(TF-RF) for fault classification. Tests on the QPZZ-II Fault Simulation Test Bench and KWCU data show that the method effectively identifies bearing faults at various speeds and loads, demonstrating its strong potential for application. Furthermore, due to its broadband characteristics and capacity to excite the system's natural frequencies, this method has potential for scalability to other impact-type fault systems.

While the unmasked priming paradigm has effectively revealed a biphasic pattern of morphological decomposition-early morpho-orthographic segmentation followed by later morpho-semantic integration-it remains unsettled which ERP components reliably reflect these distinct stages of morphological processes. To address this, we systematically compared ERP responses across three priming conditions-morphological (e.g., swiftly-swift), orthographic (e.g., surgeon-surge), and semantic (e.g., explode-burst)-focusing on the N250, early N400, late N400, and late negativity (LN). The N250 showed no facilitative effects and instead exhibited increased negativities across all conditions, challenging its reliability as a marker of early morpho-orthographic processing under unmasked priming. In contrast, both the early and late N400 subcomponents provided consistent indices of morpho-orthographic segmentation and morpho-semantic integration, respectively. In the early N400, morphological priming elicited earlier and stronger negativity attenuation than orthographic priming, with minimal semantic influence-reflecting semantically blind morpho-orthographic segmentation. In the late N400, negativity attenuation for morphological priming was further amplified and exceeded that of semantic priming, indicating morpho-semantic integration via shared morphemes. Notably, we also observed a unique biphasic orthographic priming pattern with embedded word targets: a relatively short and weak early N400 attenuation followed by an LN peak, reflecting initial facilitation from embedded word identification and subsequent lateral inhibition between orthographically related but semantically unrelated words. These findings establish the early and late N400 as robust ERP signatures of biphasic morphological processing under unmasked priming and elucidate the temporal dynamics of morphological, orthographic, and semantic processes.

We discovered a new type of assimilative color induction. An achromatic target with a white background was placed in the center of a concentric chromatic gradient that caused the glare effect. The target frequently appears to be in the same hue as the gradient. We discussed lower-level factors such as lateral inhibition and spatial summation functions, and higher-level factors such as illumination estimation.

Background/Objectives: Hearing aids can be used as a treatment for tinnitus. There are indications that this treatment is most effective when the tinnitus pitch falls in the frequency range of amplification of the hearing aid. Then, the hearing aid provides masking of the tinnitus. Alternatively, it has been suggested that a gap in the amplification around the tinnitus pitch would engage lateral inhibition and thereby reduce the tinnitus. Methods: To test these ideas, we conducted a randomized controlled trial. Patients were fitted with hearing aids using three different amplification schemes: (1) standard amplification according to the NAL-NL2 prescription procedure, (2) boosted amplification at the tinnitus frequency to enhance tinnitus masking, and (3) notch-filtered amplification at the tinnitus frequency to engage lateral inhibition and suppress tinnitus. The goal was to compare the boosted and notched amplification schemes to standard amplification. The primary outcome measure was tinnitus handicap as measured by the Tinnitus Functional Index (TFI). The trial was designed as a double-blind Latin square balanced crossover study. Eighteen tinnitus patients with moderate hearing loss were included. All of them were experienced hearing aid users. After two weeks of initial adaptation to the new hearing aids with standard settings, each setting was tried for four weeks. Results: There was an average reduction of 6.9 points on the TFI score after the adaptation phase, possibly due to a placebo effect. The TFI score did not differ significantly from the standard setting after using the notched or the boosted settings. Although notched amplification performed better than boosted amplification, this difference did not reach the clinical significance level. Regardless of the TFI outcomes, most participants had an individual preference for a particular setting. This preference was approximately uniformly distributed across the three amplification schemes. Conclusions: Notch-filtered and boosted amplification did not provide better tinnitus suppression than standard amplification. The individual preferences highlighted the importance of tailor-made approaches to hearing aid amplification in clinical practice. Further studies should explore the differences among patient’s tinnitus and their preference for a hearing aid setting.

How developing organisms respond to a changing environment is a fundamental question. Pollutants and temperature are major environmental factors. Using the bristle patterning of Drosophila as a model system, we observed that cold temperature and methotrexate, a medical drug that contaminates wastewaters, increase dorsocentral (DC) bristle number, a trait normally robust. The patterning of bristles is well understood and involves the achaete-scute (ac-sc) proneural genes. Modular enhancers activate ac-sc expression in groups of cells, called proneural clusters, from which bristle precursors are selected by lateral inhibition, a process involving Notch signalling and ac-sc auto-activation. In addition, ac-sc basal expression is controlled by a cocktail of repressive factors. We observed that the deletion of the DC enhancer prevents the induction of ectopic DC bristles by methotrexate but does not stop low temperature to induce DC bristles. Indeed, we show that methotrexate has a strong synergy with mutants of factors that regulate the DC enhancer and extends the zone of activity of this enhancer. In contrast, temperature interacts with repressors of ac-sc basal expression. Thus, methotrexate and temperature both affect DC bristle patterning but by distinct mechanisms, methotrexate on the DC enhancer and cold independently of this enhancer.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-20122-6.

Although phasic alertness generally benefits cognitive performance, it often increases the impact of distracting information, resulting in impaired decision-making and cognitive control. However, it is unclear why phasic alertness has these negative effects. Here, we present a novel, biologically informed account, according to which phasic alertness generates a transient, evidence-independent input (TEI) to the decision process. This shortens overall response times but also amplifies competition between evidence accumulators, thus slowing down decision-making and impairing cognitive control. The key hypotheses of this account are supported with pupil measurements and electrophysiological data from human decision-makers of either sex performing an arrow flanker task. We also show that a computational model of the flanker task that incorporates a TEI can reproduce the behavioral effects of phasic alertness but only when the evidence accumulators compete with each other through lateral inhibition. Our results reveal a close interplay between dynamic changes in alertness, cognitive control, and evidence accumulation.

We propose a mean field model of the primary visual cortex (V1), connected to a realistic retina model, to study the impact of the retina on motion anticipation. We first consider the case where the retina does not itself provide anticipation-which is then only triggered by a cortical mechanism, the "anticipation by latency"-and unravel the effects of the retinal input amplitude, of stimulus features such as speed and contrast and of the size of cortical extensions and fiber conduction speed. Then we explore the changes in the cortical wave of anticipation when V1 is triggered by retina-driven anticipatory mechanisms: gain control and lateral inhibition by amacrine cells. Here, we show how retinal and cortical anticipation combine to provide an efficient processing where the simulated cortical response is in advance over the moving object that triggers this response, compensating the delays in visual processing.

A salient visual object with a distinct feature from the surrounding environment automatically captures attention. While the saliency signals have been found in many brain regions, their source remains highly controversial. Here, we investigated the neural origin of visual saliency using cortical layer-dependent functional magnetic resonance imaging (fMRI) of cerebral blood volume (CBV) at 7 Tesla. Behaviorally, human observers were better at detecting salient foreground bars with a larger orientation contrast from uniformly oriented background bars. Saliency-sensitive signals were strongest in the superficial layers of the primary visual cortex (V1) and in the middle layers of the intraparietal sulcus (IPS) of the parietal cortex. Layer-dependent effective connectivity revealed the transmission of saliency signals along the feedforward pathway from V1 to IPS. Furthermore, behavioral sensitivity to the foreground stimulus correlated significantly with the fMRI response in the superficial layers of V1. Our findings provide mesoscale evidence that a visual saliency map is created by iso-feature suppression through lateral inhibition in the superficial layers of V1, and then feeds forward to attentional control brain regions to guide attention and eye movements.

We propose a system biology approach to understand how GRNs’ dynamical feedback with diffusion of some molecular components underlie the emergence of spatial cellular patterns. We use experimental data on the GRN underlying cell differentiation and spatial arrangement in the root epidermis of WT and mutant Arabidopsis phenotypes to validate our proposal. We test a generalized model of reaction-diffusion, which includes cell-to-cell interaction through lateral inhibition dynamics. The GRN corresponds to the reactive part, and diffusion involves two of its components. The Arabidopsis thaliana root epidermis has a distinct interspersed spatial pattern of hair and non-hair cells. Central to this process is the diffusion of CPC and GL3/EGL3 proteins, which drive lateral inhibition to coordinate cell identity. Existing models have shown a limited predictive power due to incomplete GRN topologies and the lack of explicit diffusion dynamics. Here, we introduce a diffusion-coupled meta-GRN model that integrates positive and negative feedback loops to simulate root epidermal pattern formation in wild-type and mutant lines under varying diffusion levels. By explicitly simulating CPC and GL3/EGL3 protein diffusion, in addition to recovering 28 single and multiple loss-of-function mutant phenotypes, as well as capturing trichoblast and atrichoblast spatial distributions relative to cortex cells, this study presents a 2-D morphospace or phenotypic landscape for epidermis patterning depending on diffusion levels. The findings highlight the critical role of protein diffusion and its dynamic feedback loops with complex GRN in shaping cellular spatial configurations and offer new insights into an extended reaction-diffusion dynamic patterning mechanism that is surely at play in other biological systems.

Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks.

Temporal lobe epilepsy (TLE) is the most common form of epilepsy and is characterized by focal seizures originating from limbic structures, including the hippocampus. Patients with TLE often experience impaired consciousness. A recent awake mouse model study demonstrated decreased cortical cholinergic innervation during focal seizures with impaired consciousness, based on cortical slow wave activity and decreased behavioral responsiveness. But the underlying mechanisms for reduced cortical cholinergic activity are not fully understood. This study employs the same awake mouse model combined with electrophysiology recordings in key network nodes, cell-specific calcium imaging in the lateral septum, and neurotransmitter sensing in one of the major subcortical cholinergic systems, the nucleus basalis of Meynert (NBM). We demonstrate that decreased cortical cholinergic innervation during focal seizures comes from both direct inhibition and indirect de-excitation of the NBM, showing a parallel pathway NBM suppression mechanism from the LS directly and through the paratenial thalamic nucleus indirectly. This work contributes to a deeper understanding of the neural processes involved in impaired consciousness during focal seizures and may open the way to new treatments for this disorder.

It has been suggested that episodic memory relies on the well-studied machinery of spatial memory. This influential notion faces hurdles that become evident with dynamically changing spatial scenes and an immobile agent. Here I propose a model of episodic memory that can accommodate such episodes via temporal indexing. Indices in the model have flexible duration, capable of exhibiting both fixed duration and broadening time fields akin to classical time cells. The latter cannot index episodes beyond short durations and are reminiscent of timing codes in scalar expectancy theory. Contrary to timing repetitive events, the present model focuses on the one-shot indexing of within-episode structure. Hippocampal indices are recruited by a combination of contextual inputs, lateral inhibition, and drive from temporal analogues of grid cells, functioning as an on-demand sequence generator and memory store. Indices learn connections to cortical representations, modulated by an amygdala signal. This architecture relies on biologically plausible, common network motifs, which can replay dynamically changing and spatially structured events, while an agent is immobile, suggests a mechanism for modulating the speed of recall, and can replay disjoint collections (i.e., broken chains) of indices with preserved temporal order. The model is embedded in an extensive review/perspective along two conceptual axes: first, how the model fits in with other accounts of time coding, serial order memory, and flexible temporal cognition and, second, how we can simultaneously reconcile the model framework with classical accounts of episodic memory à la Tulving, as well as with modern reinforcement learning and generative model accounts of hippocampal function. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Low temperature is a common stressor for winter field crops, notably impacting wheat roots. However, there is scarce knowledge on subterranean wheat root responses to low temperatures. To address this knowledge gap, we investigated the adaptive responses and underlying mechanisms of wheat roots to cold stress using a hydroponic system. Our investigation revealed that 4°C stress alters wheat root system architecture (RSA) by inhibiting primary and lateral root growth while increasing root hair density and length. Transcriptomic analysis identified auxin (IAA) and reactive oxygen species (ROS) as potentially crucial regulatory factors in the adaptation of wheat roots to low temperatures. Furthermore, a protein interaction network links ROS metabolism and auxin signaling, with NADPH-dependent thioredoxin reductase A (NTRA) serving as a central regulatory node. Pharmacological manipulation (utilizing IAA and polar transport inhibitors) and histochemical localization experiments demonstrated that the interplay between lateral roots and root hairs under low-temperature stress is associated with the spatial distribution of IAA in the epidermis and stele. Time-course analysis indicated that ROS function as early signaling molecules that initiate downstream IAA signaling pathways. Pharmacological intervention using ROS scavengers and histochemical localization experiments revealed that scavenging ROS disrupts the epidermal distribution of auxin, consequently impeding root hair development. We propose a model termed "ROS-IAA spatio-temporal coordination mediates RSA plasticity." In this model, the low-temperature-induced ROS surge acts as an upstream trigger for auxin redistribution, ultimately balancing the trade-off between lateral root inhibition and root hair promotion. These findings enhance our comprehension of auxin responsiveness and RSA plasticity in wheat root adaptation to low temperatures, offering theoretical underpinning for leveraging wheat roots as a target for breeding climate-resilient varieties.

Neuromorphic systems that emulate the information transmission of biological neural networks face challenges in their integration owing to the disparate features of neuron‐ and synapse‐mimicking devices, leading to complex and inefficient system architectures. Herein, the study proposes a steep‐switching nonvolatile field‐effect transistor leveraging a CuInP2S6/h‐BN/WSe2 heterostructure to enable reconfigurable neuron‐ and synapse‐modes by electrostatically modulating the carrier density of the channel to control its Fermi level, thereby facilitating leaky‐integrate‐and‐fire (LiF) neuron operation. In addition, an additional ferroelectric‐gating effect enhances the chemical potential of the channel through interactions between ferroelectric dipoles and channel carriers, allowing LiF operation at a reduced operating bias condition. The synaptic mode is activated by shifting the Fermi level of the channel toward the valence band, where the increased carrier density induces a screening effect that suppresses impact ionization and causes the device to operate predominantly through ferroelectric effects, enabling weight‐modulated synaptic functionality. A device‐to‐system level simulation of the spiking neural network is performed based on a single device neuron‐synapse integrated system, achieving an accuracy of 95.83% for human face recognition via lateral inhibition function of the neuron device. This study presents a promising approach for the development of a cointegrated and highly scalable neuromorphic computing technology.

The dentate gyrus is critical for spatial memory formation and shows task-related activation of cellular ensembles considered as memory engrams. Semilunar granule cells (SGCs), a sparse dentate projection neuron subtype, were reported to be enriched among behaviorally activated neurons. By examining SGCs and granule cells (GCs) labeled during contextual memory formation in TRAP2 mice, we empirically tested competing hypotheses for GC and SGC recruitment into memory ensembles. Consistent with more excitable neurons being recruited into memory ensembles, SGCs showed greater sustained firing than GCs. Additionally, labeled SGCs showed less adapting firing than unlabeled SGCs. The lack of glutamatergic connections between behaviorally labeled SGCs and GCs in our recordings is inconsistent with SGC-driven local circuit feedforward excitation underlying ensemble recruitment. Moreover, there was little evidence for individual SGCs or labeled neuronal ensembles supporting lateral inhibition of unlabeled neurons. Instead, labeled GCs and SGCs received more spontaneous excitatory synaptic inputs than their unlabeled counterparts. Labeled neuronal pairs received more temporally correlated spontaneous excitatory synaptic inputs than labeled-unlabeled neuronal pairs. These findings challenge the proposal that SGCs drive dentate GC ensemble refinement, while supporting a role for intrinsic excitability and correlated inputs in preferential SGC recruitment to contextual memory engrams.

Inhibitory local interneurons (LNs) play an essential role in sensory processing by refining stimulus representations via a diverse collection of mechanisms. The morphological and physiological traits of individual LN types, as well as their connectivity within sensory networks, enable each LN type to support different computations such as lateral inhibition or gain control and are therefore ideal targets for modulatory neurons to have widespread impacts on network activity. In this study, we combined detailed connectivity analyses, serotonin receptor expression, neurophysiology, and computational modeling to demonstrate the functional impact of serotonin on a constrained LN network in the olfactory system of Drosophila. This subnetwork is composed of three LN types and we describe each of their distinctive morphology, connectivity, biophysical properties and odor response properties. We demonstrate that each LN type expresses different combinations of serotonin receptors and that serotonin differentially impacts the excitability of each LN type. Finally, by applying these serotonin induced changes in excitability to a computational model that simulates the impact of inhibition exerted by each LN-type, we predict a role for serotonin in adjusting the dynamic range of antennal lobe output neurons and in noise reduction in odor representations. Thus, a single modulatory system can differentially impact LN types that subserve distinct roles within the olfactory system.

Spiking neural networks (SNNs) have garnered considerable attention as energy‐efficient and biologically inspired computing paradigms. However, despite the growing interest, the development of hardware‐based SNNs has remained limited, primarily because of insufficient research on hardware‐based spiking neuron devices. In this study, a CuInP2S6 (CIPS)‐based threshold switching field‐effect transistor (TS‐FET) is presented, featuring steep switching characteristics, and demonstrate its potential as an energy‐efficient spiking neuron device. The proposed device exhibits outstanding characteristics: ultra‐steep subthreshold swing (SS ≈7.5 mV dec−1), high on/off current ratio (>107), and ultra‐low off current (≈0.3 pA) due to the ferroionic properties of CIPS. The tunable dynamics for Cu+ ion migration induce a phase transition, leading to sharp resistance switching and efficient spiking. This device successfully mimics key neuronal dynamics, including leaky integrate‐and‐fire, threshold tuning, and spatiotemporal dynamics, without requiring auxiliary reset circuits. Furthermore, SNN is constructed by integrating CIPS‐based synaptic and neuron devices and evaluate face classification performance using an unsupervised learning approach, achieving a recognition accuracy of 95.83% via the lateral inhibition function of the neuron device. The findings highlight the potential of CIPS TS‐FET as energy‐efficient spiking neuron device applications for next‐generation SNN‐based neuromorphic computing systems.

Summary.The role of intra- and interglomerular networks in the olfactory bulb (OB) in transforming olfactory receptor neuron (ORN) input across concentration changes remains poorly understood. We addressed this question by implementing a mathematical model of the OB input-output transformation in which the output of each glomerulus is a function of its ORN input, local and lateral inhibition. The model produced outputs with concentration-response relationships that depended on the input ORN Hill coefficient and half-activation value. Some glomeruli responded with monotonic increases or decreases, while others responded with non-monotonic decreases then increases or increased then decreased. The model yielded several novel predictions that challenged recent findings in the field, including the existence of an abundance of non-monotonic responses and that inhibition across the interglomerular network should increase with excitation. We tested these predictions using single and dual-color in vivo 2-photon Ca2+ imaging from ORN and MTC glomeruli in awake mice. MTC glomeruli responded to odors with monotonic and non-monotonic concentration-response relationships in an odor-specific manner. Notably the mean excitation and suppression across the glomerular population were significantly correlated with one another and half of the MTC glomeruli exhibited some degree of non-monotonicity. The magnitude of non-monotonicity in MTC glomeruli was significantly correlated with the affinity of its ORN input and decreasing MTC glomeruli were innervated by low affinity or non-responsive ORN input. The results support a model in which rising levels of local and lateral inhibition will generate heterogeneous concentration-response relationships, which we propose will facilitate odor discrimination.

Reinforcement learning models often rely on uncertainty estimation to guide decision-making in dynamic environments. However, the role of memory limitations in representing statistical regularities in the environment is less understood. This study investigated how limited memory capacity influence uncertainty estimation, potentially leading to misestimations of outcomes and environmental statistics. We developed a computational model incorporating active working memory processes and lateral inhibition to demonstrate how relevant information is selected, stored, and used to estimate uncertainty. The model allows for the detection of contextual changes by estimating expected uncertainty and perceived volatility. Two experiments were conducted to investigate limitations in information availability and uncertainty estimation. The first experiment explored the effect of cognitive load on memory reliance for uncertainty estimation. The results show that cognitive load diminished reliance on memory, lowered expected uncertainty, and increased perceptions of environmental volatility. The second experiment assessed how outcome exposure conditions affect the ability to detect environmental changes, revealing differences in the mechanisms used for environmental change detection. The findings emphasize the importance of memory constraints in uncertainty estimation, highlighting how misestimation of uncertainties is influenced by individual experiences and the capacity of working memory (WM) to store relevant information. These insights contribute to understanding the role of WM in decision-making under uncertainty and provide a framework for exploring the dynamics of reinforcement learning in memory-limited systems.