# NeuroAI & Computational Neuroscience Domain: Neuroscience & Brain-Computer Interfaces · 227 papers indexed · newest 2026-10-27 Neural networks as models of the brain, predictive coding, neural population dynamics and foundation models trained on neural data. Key concepts: NeuroAI, neural population dynamics, predictive coding, brain-like, neural data foundation model ## Most influential - **[Brain tumor segmentation with Deep Neural Networks](https://space-kitty.com/papers/arxiv:1505.03540)**. Mohammad Havaei, Axel Davy, David Warde-Farley et al. · 2016 · Medical Image Analysis (arXiv:1505.03540 · doi:10.1016/j.media.2016.05.004 · 3,277 citations). Source: > In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive p… - **[Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images](https://space-kitty.com/papers/doi:10.1109/tmi.2016.2538465)**. Sérgio Pereira, Adriano B. Pinto, Victor Alves et al. · 2016 · IEEE Transactions on Medical Imaging (doi:10.1109/tmi.2016.2538465 · 2,735 citations). Source: - **[Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing](https://space-kitty.com/papers/doi:10.1146/annurev-vision-082114-035447)**. Nikolaus Kriegeskorte · 2015 · Annual Review of Vision Science (doi:10.1146/annurev-vision-082114-035447 · 1,083 citations). Source: - **[Multi-grade brain tumor classification using deep CNN with extensive data augmentation](https://space-kitty.com/papers/doi:10.1016/j.jocs.2018.12.003)**. Muhammad Sajjad, Salman Khan, Khan Muhammad et al. · 2018 · Journal of Computational Science (doi:10.1016/j.jocs.2018.12.003 · 895 citations). Source: - **[Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker](https://space-kitty.com/papers/arxiv:1612.02572)**. James Howard Cole, Rudra P. K. Poudel, Dimosthenis Tsagkrasoulis et al. · 2017 · NeuroImage (arXiv:1612.02572 · doi:10.1016/j.neuroimage.2017.07.059 · 870 citations). Source: > Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people and deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of "brain-predicted age" as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre-processed and raw T1-weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain-predict… - **[Brain tumor classification for MR images using transfer learning and fine-tuning](https://space-kitty.com/papers/doi:10.1016/j.compmedimag.2019.05.001)**. Zar Nawab Khan Swati, Qinghua Zhao, Muhammad Kabir et al. · 2019 · Computerized Medical Imaging and Graphics (doi:10.1016/j.compmedimag.2019.05.001 · 843 citations). Source: - **[MR-based synthetic CT generation using a deep convolutional neural network method](https://space-kitty.com/papers/arxiv:1704.07239)**. Xiao Han · 2017 · Medical Physics (arXiv:1704.07239 · doi:10.1002/mp.12155 · 868 citations). Source: > Liver lesion segmentation is an important step for liver cancer diagnosis, treatment planning and treatment evaluation. LiTS (Liver Tumor Segmentation Challenge) provides a common testbed for comparing different automatic liver lesion segmentation methods. We participate in this challenge by developing a deep convolutional neural network (DCNN) method. The particular DCNN model works in 2.5D in that it takes a stack of adjacent slices as input and produces the segmentation map corresponding to the center slice. The model has 32 layers in total and makes use of both long range concatenation con… - **[Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation](https://space-kitty.com/papers/arxiv:1904.07773)**. Junhao Wen, Elina Thibeau–Sutre, Mauricio Díaz et al. · 2020 · Medical Image Analysis (arXiv:1904.07773 · doi:10.1016/j.media.2020.101694 · 801 citations). Source: > Over 30 papers have proposed to use convolutional neural network (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present wor… - **[Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition](https://space-kitty.com/papers/arxiv:1406.3284)**. Charles F. Cadieu, Ha Hong, Daniel L. K. Yamins et al. · 2014 · PLoS Computational Biology (arXiv:1406.3284 · doi:10.1371/journal.pcbi.1003963 · 824 citations). Source: > The primate visual system achieves remarkable visual object recognition performance even in brief presentations and under changes to object exemplar, geometric transformations, and background variation (a.k.a. core visual object recognition). This remarkable performance is mediated by the representation formed in inferior temporal (IT) cortex. In parallel, recent advances in machine learning have led to ever higher performing models of object recognition using artificial deep neural networks (DNNs). It remains unclear, however, whether the representational performance of DNNs rivals that of th… - **[Deep convolutional neural networks for multi-modality isointense infant brain image segmentation](https://space-kitty.com/papers/doi:10.1016/j.neuroimage.2014.12.061)**. Wenlu Zhang, Rongjian Li, Houtao Deng et al. · 2015 · NeuroImage (doi:10.1016/j.neuroimage.2014.12.061 · 817 citations). Source: - **[A deep learning model integrating FCNNs and CRFs for brain tumor segmentation](https://space-kitty.com/papers/arxiv:1702.04528)**. Xiaomei Zhao, Yihong Wu, Guidong Song et al. · 2017 · Medical Image Analysis (arXiv:1702.04528 · doi:10.1016/j.media.2017.10.002 · 813 citations). Source: > Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training C… - **[Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://space-kitty.com/papers/doi:10.1016/j.compmedimag.2021.102026)**. Michael Yeung, Evis Sala, Carola‐Bibiane Schönlieb et al. · 2021 · Computerized Medical Imaging and Graphics (doi:10.1016/j.compmedimag.2021.102026 · 748 citations). Source: > Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art for most medical image segmentation tasks. Issues with class imbalance pose a significant challenge in medical datasets, with lesions often occupying a considerably smaller volume relative to the background. Loss functions used in the training of deep learning algorithms differ in their robustness to class imbalance, with direct consequences for model convergence. The most commonly used loss functions for segmentati… > _Abstract excerpt by Michael Yeung et al., CC-BY, https://doi.org/10.1016/j.compmedimag.2021.102026; truncated._ - **[Multi-Classification of Brain Tumor Images Using Deep Neural Network](https://space-kitty.com/papers/doi:10.1109/access.2019.2919122)**. Hossam H. Sultan, Nancy M. Salem, Walid Al‐Atabany · 2019 · IEEE Access (doi:10.1109/access.2019.2919122 · 774 citations). Source: - **[Multimodal deep learning models for early detection of Alzheimer’s disease stage](https://space-kitty.com/papers/doi:10.1038/s41598-020-74399-w)**. Janani Venugopalan, Tong Li, Hamid Reza Hassanzadeh et al. · 2021 · Scientific Reports (doi:10.1038/s41598-020-74399-w · 732 citations). Source: > Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) … > _Abstract excerpt by Janani Venugopalan et al., CC-BY, https://doi.org/10.1038/s41598-020-74399-w; truncated._ - **[Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.](https://space-kitty.com/papers/doi:10.1037/pspa0000098)**. Yilun Wang, Michał Kosiński · 2018 · Journal of Personality and Social Psychology (doi:10.1037/pspa0000098 · 766 citations). Source: > We show that faces contain much more information about sexual orientation than can be perceived or interpreted by the human brain. We used deep neural networks to extract features from 35,326 facial images. These features were entered into a logistic regression aimed at classifying sexual orientation. Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 81% of cases, and in 71% of cases for women. Human judges achieved much lower accuracy: 61% for men and 54% for women. The accuracy of the algorithm increased to 91% and 83%, respectively, gi… > _Abstract excerpt by Yilun Wang et al., CC-BY, https://doi.org/10.1037/pspa0000098; truncated._ ## Newest - **[Neuronal Identity as an Organizational Basis for Analyzing Neural Population Dynamics](https://space-kitty.com/papers/arxiv:2610.36745)**. Seungjae Han, Joshua Yedam You, Soi Kim et al. · 2026 (arXiv:2610.36745). Source: - **[Local learning is all you need: brain-aligned predictive coding networks](https://space-kitty.com/papers/arxiv:2610.37327)**. Dirk Gütlin, Ryszard Auksztulewicz, Adrien Doerig · 2026 (arXiv:2610.37327). Source: - **[SpatialPC: Deep Computation with Sublinear Local Credit Propagation](https://space-kitty.com/papers/arxiv:2610.36139)**. Benjamin Prada, Shion Matsumoto, Ankur Arjun Mali · 2026 (arXiv:2610.36139). Source: - **[Persistence, Exposure, and Credit Routing in Linear Temporal Predictive Coding](https://space-kitty.com/papers/arxiv:2610.36412)**. Akira Tanase, Julian Gould, Jeffrey Seely · 2026 (arXiv:2610.36412). Source: - **[Neural Video Coding under Limited Delay: Theoretical Optimality and System Design](https://space-kitty.com/papers/arxiv:2610.25729)**. Likun Cai, Jun Chen, Ashish Khisti · 2026 (arXiv:2610.25729). Source: - **[AlignPred: Error-Controlled KV Cache Compression via Alignment and Predictive Coding](https://space-kitty.com/papers/arxiv:2610.17970)**. Xu Yang, Jiapeng Zhang, Zhiwei Zuo et al. · 2026 (arXiv:2610.17970). Source: - **[Error-Based Bidirectional Predictive Coding Enables Deep, 4-Bit Predictive Coding Networks](https://space-kitty.com/papers/arxiv:2610.15933)**. Garrett Kinman, Zeljko Zilic · 2026 (arXiv:2610.15933). Source: - **[PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation](https://space-kitty.com/papers/arxiv:2610.14409)**. Alex Gower · 2026 (arXiv:2610.14409). Source: - **[DeltaNet-PC: Test-Time Memory Without Backpropagation Through Time](https://space-kitty.com/papers/arxiv:2610.15185)**. Todd Morrill, Richard Zemel · 2026 (arXiv:2610.15185). Source: - **[Analog-Friendly Predictive Coding without Activation Derivatives](https://space-kitty.com/papers/arxiv:2609.32350)**. Francesco Innocenti · 2026 (arXiv:2609.32350). 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