Channel Interaction Research Articles

Experimentally validated modeling of dynamic drug-hERG channel interactions reproducing the binding mechanisms and its importance in action potential duration.

Background and ObjectiveAssessment of drug cardiotoxicity is critical in the development of new compounds and modeling of drug-binding dynamics to hERG can improve early cardiotoxicity assessment. We previously developed a methodology to generate Markovian models reproducing preferential state-dependent binding properties, trapping dynamics and the onset of IKr block using simple voltage clamp protocols. Here, we test this methodology with real IKr blockers and investigate the impact of drug dynamics on action potential prolongation. MethodsExperiments were performed on HEK cells stably transfected with hERG and using the Nanion SyncroPatch 384i. Three protocols, P-80, P0 and P 40, were applied to obtain the experimental data from the drugs and the Markovian models were generated using our pipeline. The corresponding static models were also generated and a modified version of the O´Hara-Rudy action potential model was used to simulate the action potential duration. ResultsThe experimental Hill plots and the onset of IKr block of ten compounds were obtained using our voltage clamp protocols and the models generated successfully mimicked these experimental data, unlike the CiPA dynamic models. Marked differences in APD prolongation were observed when drug effects were simulated using the dynamic models and the static models. ConclusionsThese new dynamic models of ten well-known IKr blockers constitute a validation of our methodology to model dynamic drug–hERG channel interactions and highlight the importance of state-dependent binding, trapping dynamics and the time-course of IKr block to assess drug effects even at the steady-state.

Numerical Study of Pressure Response to Action Potential by Water Permeation With Ion Transports

Abstract While the permeation mechanism of solute (e.g., ions and glucose) through biological membrane has been studied extensively, the mechanical role of water transport in intracellular phenomena has not received much attention. In the present study, to investigate the effect of water permeation on the intracellular pressure response, a novel permeation flux model through a biological membrane is developed by incorporating the coupling permeabilities (between water and ion fluxes) as the water–ion interaction in the ion channels. The proposed model is applied to a two–dimensional permeation problem of water and ions in a closed cell separated by a thin membrane. The permeation flux model reproduces the typical time response of intracellular pressure to action potentials with reasonable agreement with experimental results in the literature, indicating that the pressure response can be characterized by the following three parameters: water permeability, the mass ratio of water and ion, and the ratio of the permeation fluxes of water and ion. In particular, the permeation flux ratio plays an essential role in intracellular phenomena; depending on the value of the permeation flux ratio, the time lag between the action potential and the pressure response is 0.1 times smaller than that expected by the previous researchers, indicating that water transport associated with ions may trigger a pressure response. This study demonstrates the importance of water permeation in intracellular mechanical response through coupling of the fluid motion and electric fields.

CIBENet: A channel interaction and bridging-enhanced change detection network for optical and SAR remote sensing images

Effectively utilizing the complementary characteristics between optical and SAR remote sensing images to accurately identify change information is of great practical significance. Direct pixel comparisons are challenging since they come from sensors with different imaging mechanisms. Therefore, in this paper, a novel domain transformation that incorporates channel interaction and a bridging-enhanced (CIBENet) heterogeneous change detection network is proposed, where twin-depthwise separable fused gated channel transformation is the convolutional block designed for channel information interaction. And samples without changed semantic information are used to train the deep transformation model, which effectively solves the interference of semantic information on NiceGAN. A different perspective is provided for the heterogeneous change detection task. The backend change detection network takes UNet++ with twin-depthwise separable convolution as the baseline, introduces the gated channel transformation and bridging-enhanced decoder, and models the feature relationship between channels to strengthen the channel information interaction while suppressing the expression of nonchanged information. In addition, the bridging-enhanced decoder can efficiently solve localized holes and discontinuities in binary maps by bridging identical pixels. CIBENet is supervised and experimented on three heterogeneous change detection datasets, Gloucester, Shuguang, and Italy, also compared with the classical unsupervised methods CGAN, SCCN, INLPG and advanced supervised methods DTCDN, DACDT. The network model proposed in this paper significantly improved F1 and recall, and the overall accuracies were 96.60 %, 98.23 %, and 96.29 %, respectively. The experiments validated the reliability of the proposed model.

EPSViTs: A hybrid architecture for image classification based on parameter-shared multi-head self-attention

Vision transformers have been successfully applied to image recognition tasks due to their ability to capture long-range dependencies within an image. However, they still suffer from weak local feature extraction, easy loss of channel interaction information in one-dimensional multi-head self-attention modeling, and large number of parameters. This paper proposes a lightweight image classification hybrid architecture named EPSViTs (Efficient Parameter Shared Transformer, EPSViTs). Firstly, a new local feature extraction module is designed to effectively enhance the expression of local features. Secondly, using the parameter sharing approach, a lightweight multi-head self-attention module based on information interaction is designed, which can globally model the image from both spatial and channel dimensions, and mine the potential correlation of the image in space and channel. Extensive experiments are conducted on three public datasets, a subset of ImageNet, Cifar100 and APTOS2019, a private dataset Mushroom66, and the results show that the hybrid architecture EPSViTs proposed in this paper based on parameter sharing for multi-head self-attentive image classification has obvious advantages, especially on a subset of ImageNet to reach 89.18%, which is a 3.8% improvement compared to Edgevits_xxs, verifying the effectiveness of the model.

Toward high-resolution modeling of small molecule-ion channel interactions.

Ion channels are critical drug targets for a range of pathologies, such as epilepsy, pain, itch, autoimmunity, and cardiac arrhythmias. To develop effective and safe therapeutics, it is necessary to design small molecules with high potency and selectivity for specific ion channel subtypes. There has been increasing implementation of structure-guided drug design for the development of small molecules targeting ion channels. We evaluated the performance of two RosettaLigand docking methods, RosettaLigand and GALigandDock, on the structures of known ligand-cation channel complexes. Ligands were docked to voltage-gated sodium (NaV), voltage-gated calcium (CaV), and transient receptor potential vanilloid (TRPV) channel families. For each test case, RosettaLigand and GALigandDock methods frequently sampled a ligand-binding pose within a root mean square deviation (RMSD) of 1-2Å relative to the experimental ligand coordinates. However, RosettaLigand and GALigandDock scoring functions cannot consistently identify experimental ligand coordinates as top-scoring models. Our study reveals that the proper scoring criteria for RosettaLigand and GALigandDock modeling of ligand-ion channel complexes should be assessed on a case-by-case basis using sufficient ligand and receptor interface sampling, knowledge about state-specific interactions of the ion channel, and inherent receptor site flexibility that could influence ligand binding.

MCNet: Multivariate long-term time series forecasting with local and global context modeling

Time series data typically exhibit various intra-sequence and inter-sequence correlations, resulting in intricate, intertwined dependencies, which pose challenges for accurately predicting future long-term trends. Previous studies have not fully considered the two correlations, and they also still face challenges of excessive time and memory complexity when dealing with long-term predictions. To address these challenges and establish high-precision prediction models, we propose MCNet that consists of a local branch and a global branch. The local branch aims at capturing short-term variations of intra-sequences, as well as capturing inter-sequence correlations. The global branch models long-term dependencies within sequences. Specifically, the local branch consists only of MLP module, which effectively captures short-term variations of intra-sequences by independently modeling the temporal information within and between patches of the most recent time series. Subsequently, inter-sequence dependencies are captured through the channel interaction module, which further explores more key information to improve the performance of MCNet. Meanwhile, global branch models long-term dependencies within the time series through structured global convolution. Experimental results on multiple popular long-term time series forecasting benchmarks demonstrate that MCNet outperforms state-of-the-art methods, yielding a relative improvement of 12% for multivariate time series while also being more efficient.

Breast tumor detection using multi‐feature block based neural network by fusion of CT and MRI images

AbstractRadiologists and clinicians must automatically examine breast and tumor locations and sizes accurately. In recent years, several neural network‐based feature fusion versions have been created to improve medical image segmentation. Multi‐modal image fusion photos may efficiently identify tumors. This work uses image fusion to identify computed tomography and magnetic resonance imaging alterations. A Gauss‐log ratio operator is recommended for difference image production. The Gauss‐log ratio and log ratio difference image complement the objective of improving the difference map through image fusion. The feature change matrix extracts edge, texture, and intensity from each picture pixel. The final change detection map classifies feature vectors as “changed” or “unchanged” which has been mapped for high‐resolution or low‐resolution pixels. This paper proposes a multi‐feature blocks (MFB) based neural network for multi‐feature fusion. This neural network modeling approach globalizes pixel spatial relationships. MFB‐based feature fusion also aims to capture channel interactions between feature maps. The proposed technique outperforms state‐of‐the‐art approaches which have been discussed in detail in experimental results section.

ДИНАМІЧНА МОДЕЛЬ ІТЕРАЦІЙНОГО ЕЛЕКТРОПРИВОДА ПОДАЧІ З ДВОМА ГВИНТОВИМИ ПЕРЕДАЧАМИ ДЛЯ ПРЕЦИЗІЙНИХ ВЕРСТАТІВ ТА ОБРОБНИХ ЦЕНТРІВ

A variant of a simplified design diagram is proposed and the corresponding kinematic diagram of a two-motor gearless drive mechanism with two screw gears (SG) is given for an iterative two-channel electric drive for the longitudinal feed of the working tool (worktable with a product) of a coordinate multi-purpose metal-cutting machine of especially high precision, model 24K70AF4. Compensators of the negative dynamic interaction of load control channels have been determined. A generalized dynamic model of the cutting process has been constructed, taking into account both the inertia of the cutting process and the influence of the machine “WT-cutter” elastic system dynamics. A dynamic model of a conditional compensator for the cutting process is determined and calculated. A refined mathematical model of motion in steady-state feed modes of the two-channel electric drive has been obtained, taking into account the compensation of the dynamic interaction of the channels on the load and the influence of friction nonlinearities in the drive mechanism and the working tool of the machine. A structural-algorithmic diagram of an iterative two-channel compensated feed electric drive with two SGs and subordinated adjustment of control channels has been designed. The diagram includes a generalized dynamic model of the cutting process and a dynamic model of the corresponding conditional compensator for the cutting process, and also takes into account the main static moments of resistance and nonlinearity of friction in the drive load. References 13, tables 2, figures 5.

Association Between Intracochlear Electrode Design and Electrically-Evoked Compound Action Potential Measures in Cochlear Implant Users.

Cochlear implant (CI) electrode design has changed over time. Changes in intracochlear electrode design might influence the spread of neural activation along the auditory nerve and the number of independent channels. This study aimed to investigate the impact of intracochlear electrode design on the electrode-neuron interface using electrophysiological measures. Prospective cohort study. A single tertiary hospital. Fifty-two ears who were implanted with CI divided into 3 groups based on the design of intracochlear electrode arrays. Twenty-three ears were implanted with lateral wall straight electrodes. Eighteen ears were implanted with the slim perimodiolar electrode, and 11 ears were implanted with the old perimodiolar electrode. Various electrically-evoked compound action potential (ECAP) metrics were measured to quantify spread of excitation and channel interaction. ECAP threshold and slope were not significantly different among groups. ECAP spread of excitation (SOE) half-width and channel interaction index (CII) were significantly larger in subjects implanted with the lateral wall straight electrodes, indicating a wider spread of excitation compared to those with perimodiolar electrodes. Electrode impedance was significantly lower in subjects implanted with perimodiolar electrodes than those with lateral wall electrodes. Perimodiolar electrode groups yielded significantly narrower SOE half-widths and smaller CII than the lateral wall straight electrode group. This may indicate that the electrode array that hugged the modiolus had less overlap in neural excitation between adjacent electrodes, resulting in reduced channel interaction and potentially better spectral resolution than the electrode array positioned more laterally.

Application of multimodality perception scene construction based on Internet of Things (IoT) technology in art teaching

Numerous impediments beset contemporary art education, notably the unidimensional delivery of content and the absence of real-time interaction during instructional sessions. This study endeavors to surmount these challenges by devising a multimodal perception system entrenched in Internet of Things (IoT) technology. This system captures students’ visual imagery, vocalizations, spatial orientation, movements, ambient luminosity, and contextual data by harnessing an array of interaction modalities encompassing visual, auditory, tactile, and olfactory sensors. The synthesis of this manifold information about learning scenarios entails strategically placing sensors within physical environments to facilitate intuitive and seamless interactions. Utilizing digital art flower cultivation as a quintessential illustration, this investigation formulates tasks imbued with multisensory channel interactions, pushing the boundaries of technological advancement. It pioneers advancements in critical domains such as visual feature extraction by utilizing DenseNet networks and voice feature extraction leveraging SoundNet convolutional neural networks. This innovative paradigm establishes a novel art pedagogical framework, accentuating the importance of visual stimuli while enlisting other senses as complementary contributors. Subsequent evaluation of the usability of the multimodal perceptual interaction system reveals a remarkable task recognition accuracy of 96.15% through the amalgamation of Mel-frequency cepstral coefficients (MFCC) speech features with a long-short-term memory (LSTM) classifier model, accompanied by an average response time of merely 6.453 seconds—significantly outperforming comparable models. The system notably enhances experiential fidelity, realism, interactivity, and content depth, ameliorating the limitations inherent in solitary sensory interactions. This augmentation markedly elevates the caliber of art pedagogy and augments learning efficacy, thereby effectuating an optimization of art education.

Colloid Transport in Bicontinuous Nanoporous Media.

Colloid transport and retention in porous media are critical processes influencing various Earth science applications, from groundwater remediation to enhanced oil recovery. These phenomena become particularly complex in the confined spaces of nanoporous media, where strong boundary layer effects and nanoconfinement significantly alter colloid behavior. In this work, we use particle dynamics models to simulate colloid transport and retention processes in bicontinuous nanoporous (BNP) media under pressure gradients. By utilizing particle-based models, we track the movement of each colloid and elucidate the underlying colloid retention mechanisms. Under unfavorable attachment conditions, the results reveal two colloid retention mechanisms: physical straining and trapping in low-flow zone. Furthermore, we investigate the effects of critical factors including colloid volume fraction, d, pressure difference, ΔP, interaction between colloids and BNP media, Ec-p, and among colloids, Ec-c, on colloid transport. Analysis of breakthrough curves and colloid displacements demonstrates that higher values of d, lower values of ΔP, and strong Ec-p attractions significantly increase colloid retention, which further lead to colloid clogging and jamming. In contrast, Ec-c has minimal impact on colloid transport due to the limited colloid-colloid interaction in nanoporous channels. This work provides critical insights into the fundamental factors governing colloid transport and retention within stochastic nanoporous materials.

Asymmetric pulses delivered by a cochlear implant allow a reduction in evoked firing rate and in spatial activation in the guinea pig auditory cortex

Despite that fact that the cochlear implant (CI) is one of the most successful neuro-prosthetic devices which allows hearing restoration, several aspects still need to be improved. Interactions between stimulating electrodes through current spread occurring within the cochlea drastically limit the number of discriminable frequency channels and thus can ultimately result in poor speech perception. One potential solution relies on the use of new pulse shapes, such as asymmetric pulses, which can potentially reduce the current spread within the cochlea. The present study characterized the impact of changing electrical pulse shapes from the standard biphasic symmetric to the asymmetrical shape by quantifying the evoked firing rate and the spatial activation in the guinea pig primary auditory cortex (A1). At a fixed charge, the firing rate and the spatial activation in A1 decreased by 15 to 25 % when asymmetric pulses were used to activate the auditory nerve fibers, suggesting a potential reduction of the spread of excitation inside the cochlea. A strong “polarity-order” effect was found as the reduction was more pronounced when the first phase of the pulse was cathodic with high amplitude. These results suggest that the use of asymmetrical pulse shapes in clinical settings can potentially reduce the channel interactions in CI users.

Genes related to neurotransmitter receptors as potential biomarkers for Alzheimer's disease

IntroductionAlzheimer's disease (AD) is a leading cause of dementia and is rapidly emerging as one of the costliest and most burdensome diseases. Neurotransmitter receptors play a vital role in many neuronal processes, primarily regulating signal inhibition within the brain to facilitate cell communication. ObjectivesOur research aims to identify potential biomarkers associated with AD and how these biomarkers impact immune infiltration. MethodsWe extracted mRNA expression data from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were employed to identify hub genes as biomarkers in AD. The Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Variation Analysis (GSVA) were used for functional enrichment. Furthermore, we examined 22 immune cell types infiltration using “CIBERSORT”. ResultsIn this study, we identified 70 neurotransmitter receptor genes showing differential expression in AD: 22 were up-regulated, and 48 were down-regulated. Functional analyses indicated these genes were involved in essential biochemical pathways, including G protein-coupled receptors, neurotransmitter receptor activity, and ion channel interactions. WGCNA generated three co-expression modules, with one demonstrating the strongest association with AD. Five key NRGs (HTR3C, HTR3E, ADRA2A, HTR3A, and ADRA1D) were identified using a combination of differential genes. These genes have better diagnostic value by ROC analysis. Immune infiltration analysis showed that these genes were closely associated with the levels of resting mast cells, activated natural killer (NK) cells, and plasma cells in AD compared to controls. ConclusionOur study identified five NRGs (ADRA1D, ADRA2A, HTR3A, HTR3C, and HTR3E) with significant associations with AD. These findings may offer promising sights for further studies.

TriConvUNeXt: A Pure CNN-Based Lightweight Symmetrical Network for Biomedical Image Segmentation.

Biomedical image segmentation is essential in clinical practices, offering critical insights for accurate diagnosis and strategic treatment approaches. Nowadays, self-attention-based networks have achieved competitive performance in both natural language processing and computer vision, but the computational cost has reduced their popularity in practical applications. The recent study of Convolutional Neural Network (CNN) explores linear functions within modified CNN layer demonstrating pure CNN-based networks can still achieve competitive results against Vision Transformer (ViT) in biomedical image segmentation, with fewer parameters. The modified CNN, i.e., Depthwise CNN, however, leaves the features cleaved off in the channel dimension and prevents the extraction of features in the perspective of channel interaction. To effectively further explore the feature learning power of modified CNN with biomedical image segmentation, we design a lightweight multi-convolutional multi-scale convolutional network block (MSConvNeXt) for U-shape symmetrical network. Specifically, a network block consisting of a depthwise CNN, a deformable CNN, and a dilated CNN is proposed to capture semantic feature information effectively while with low computational cost. Furthermore, channel shuffling operation is proposed to dynamically promote an efficient feature fusion among the feature maps. The network block hereby is properly deployed within U-shape symmetrical encoder-decoder style network, named TriConvUNeXt. The proposed network is validated on a public benchmark dataset with a comprehensive evaluation in both computational cost and segmentation performance against 13 baseline methods. Specifically, TriConvUNeXt achieves 1% higher than UNet and TransUNet in Dice-Coefficient while 81% and 97% lower in computational cost. The implementation of TriConvUNeXt is made publicly accessible via https://github.com/ziyangwang007/TriConvUNeXt .

Elastohydrodynamic interactions in soft hydraulic knots

Soft intertwined channel systems are frequently found in fluid flow networks in nature. The passage geometry of these systems can deform due to fluid flow, which can cause the relationship between flow rate and pressure drop to deviate from the Hagen–Poiseuille linear law. Although fluid–structure interactions in single deformable channels have been extensively studied, such as in Starling's resistor and its variations, the flow transport capacity of an intertwined channel with multiple self-intersections (a ‘hydraulic knot’), is still an open question. We present experiments and theory on soft hydraulic knots formed by interlinked microfluidic devices comprising two intersecting channels separated by a thin elastomeric membrane. Our experiments show flow–pressure relationships similar to flow limitation, where the limiting flow rate depends on the knot configuration. To explain our observations, we develop a mathematical model based on lubrication theory coupled with tension-dominated membrane deflections that compares favourably with our experimental data. Finally, we present two potential hydraulic knot applications for microfluidic flow rectification and attenuation.

MFI-CD: a lightweight siamese network with multidimensional feature interaction for change detection

ABSTRACT Change detection has always played a crucial role in observing the Earth’s surface. Although visual Transformers (ViTs) have achieved excellent performance in change detection, they require high computational complexity. In addition, it does not explicitly address the differences caused by factors such as climate, lighting, and shooting angle in remote sensing (RS) image pairs. Therefore, we propose an efficient, lightweight Siamese network that considers multi-dimensional feature interaction. Firstly, we use a lightweight Transformer-based headless backbone network to extract feature information at each stage for bi-temporal images. To better capture the details and structure of images and eliminate the adverse effects of climate, lighting, and shooting angle, we design a multidimensional feature interaction method that uses spatial, channel, and amplitude dimension interaction methods after feature extraction operations at different stages. Furthermore, this approach achieves domain adaptation between bi-temporal domains to a certain extent while preserving the original semantic correspondence. Comprehensive experiments and extensive ablation studies on two common datasets, LEVIR-CD and S2Looking, have shown that our method achieves better performance with fewer parameters.

Lanthanide transport in angstrom-scale MoS2-based two-dimensional channels.

Rare earth elements (REEs), critical to modern industry, are difficult to separate and purify, given their similar physicochemical properties originating from the lanthanide contraction. Here, we systematically study the transport of lanthanide ions (Ln3+) in artificially confined angstrom-scale two-dimensional channels using MoS2-based building blocks in an aqueous environment. The results show that the uptake and permeability of Ln3+ assume a well-defined volcano shape peaked at Sm3+. This transport behavior is rooted from the tradeoff between the barrier for dehydration and the strength of interactions of lanthanide ions in the confinement channels, reminiscent of the Sabatier principle. Molecular dynamics simulations reveal that Sm3+, with moderate hydration free energy and intermediate affinity for channel interaction, exhibit the smallest dehydration degree, consequently resulting in the highest permeability. Our work not only highlights the distinct mass transport properties under extreme confinement but also demonstrates the potential of dialing confinement dimension and chemistry for greener REEs separation.

How Recommendation Affects Customer Search: A Field Experiment

The findings of this study have important implications for digital platform designers, managers, and regulators. First, the large-scale field experiment provides valuable insights into the relationship between product recommendation and consumer search under different scenarios. It highlights the importance of understanding consumer demand states and previous interests. Platforms can use these findings to customize product recommendations at an individual level and foster channel complementarity between recommendation and search. Second, the study emphasizes the need to consider channel spillovers. Optimizing recommender systems without considering the impact of channel interactions with search engines may lead to suboptimal results. Platforms should aim for a more coordinated integration of recommendation and search channels, as our conceptual framework illustrates how customers in different demand states can be influenced and served by both systems. Third, the findings offer insights into the potential impact of data regulations on e-commerce platforms. The study demonstrates that data regulations have a greater impact on the recommendation channel compared with the search channel. Platforms should find a balance between recommendation and search when facing stringent data regulations. They may strategically focus on the search channel to gather revealed customer interests, leading to a deeper integration of both channels.

Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data

The development of intelligent oceans requires exploration and an understanding of the various characteristics of the oceans. The emerging Internet of Underwater Things (IoUT) is an extension of the Internet of Things (IoT) to underwater environments, and the ability of IoUT to be combined with deep learning technologies is a powerful technology for realizing intelligent oceans. The underwater acoustic (UWA) communication network is essential to IoUT. The thermocline with drastic temperature and density variations can significantly limit the connectivity and communication performance between IoUT nodes. To more accurately capture the complexity and variability of ocean remote sensing data, we first sample and analyze ocean remote sensing datasets and provide sufficient evidence to validate the temporal redundancy properties of the data. We propose an innovative deep learning approach called Ocean-Mixer. This approach consists of three modules: an embedding module, a mixer module, and a prediction module. The embedding module first processes the location and attribute information of the ocean water and then passes it to the subsequent modules. In the mixing module, we apply a temporal decomposition strategy to eliminate redundant information and capture temporal and channel features through a self-attention mechanism and a multilayer perceptron (MLP). The prediction module ultimately discerns and integrates the temporal and channel relationships and interactions among various ocean features, ensuring precise forecasting. Numerous experiments on ocean temperature and salinity datasets show that Mixer-Ocean performs well in improving the accuracy of time series prediction. Mixer-Ocean is designed to support multi-step prediction and capture the changes in the ocean environment over a long period, thus facilitating efficient management and timely decision-making for innovative ocean-oriented applications, which has far-reaching significance for developing and conserving marine resources.

Reduced Channel Interaction Improves Timbre Recognition Under Vocoder Simulation of Cochlear Implant Processing.

This study aimed to investigate the influence of the number of channels and channel interaction on timbre perception in cochlear implant (CI) processing. By utilizing vocoder simulations of CI processing, the effects of different numbers of channels and channel interaction were examined to assess their impact on timbre perception, an essential aspect of music and auditory performance. Fourteen CI recipients, with at least 1 year of CI device use, and two groups (N = 16 and N = 19) of normal hearing (NH) participants completed a timbre recognition (TR) task. NH participants were divided into two groups, with each group being tested on different aspects of the study. The first group underwent testing with varying numbers of channels (8, 12, 16, and 20) to determine an ideal number that closely reflected the TR performance of CI recipients. Subsequently, the second group of NH participants participated in the assessment of channel interaction, utilizing the identified ideal number of 20 channels, with three conditions: low interaction (54 dB/octave), medium interaction (24 dB/octave), and high interaction (12 dB/octave). Statistical analyses, including repeated-measures analysis of variance and pairwise comparisons, were conducted to examine the effects. The number of channels did not demonstrate a statistically significant effect on TR in NH participants ( p > 0.05). However, it was observed that the condition with 20 channels closely resembled the TR performance of CI recipients. In contrast, channel interaction exhibited a significant effect ( p < 0.001) on TR. Both the low interaction (54 dB/octave) and high interaction (12 dB/octave) conditions differed significantly from the actual CI recipients' performance. Timbre perception, a complex ability reliant on highly detailed spectral resolution, was not significantly influenced by the number of channels. However, channel interaction emerged as a significant factor affecting timbre perception. The differences observed under different channel interaction conditions suggest potential mechanisms, including reduced spectro-temporal resolution and degraded spectral cues. These findings highlight the importance of considering channel interaction and optimizing CI processing strategies to enhance music perception and overall auditory performance for CI recipients.