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Applications · Neuroscience, Cognitive Science

Gaspard Oliviers, Mufeng Tang, Rafal Bogacz

Predictive coding (PC) is an influential computational model of visual learning and inference in the brain. Classical PC was proposed as a top-down generative model, where the brain actively predicts upcoming visual inputs, and inference minimises the prediction errors. Recent studies have also shown that PC can be formulated as a discriminative model, where sensory inputs predict neural activities in a feedforward manner. However, experimental evidence suggests that the brain employs both generative and discriminative inference, while unidirectional PC models show degraded performance in tasks requiring bidirectional processing. In this work, we propose bidirectional PC (bPC), a PC model that incorporates both generative and discriminative inference while maintaining a biologically plausible circuit implementation. We show that bPC matches or outperforms unidirectional models in their specialised generative or discriminative tasks, by developing an energy landscape that simultaneously suits both tasks. We also demonstrate bPC's superior performance in two biologically relevant tasks including multimodal learning and inference with missing information, suggesting that bPC resembles biological visual inference more closely.

Deep Learning · Everything Else

Jingyang Ma, Chongming Liu, Songting Li, Douglas Zhou

As widely used neuron model in Spiking Neural Networks (SNNs), the Leaky Integrate-and-Fire (LIF) model assumes the linear summation of injected currents. However, recent studies have revealed that a biological neuron can integrate inputs nonlinearly and perform computations such as XOR while an LIF neuron cannot. To bridge this gap, we propose the Dendritic LIF (DLIF) model, which incorporates a bilinear dendritic integration rule derived from neurophysiological experiments. At the single-neuron level, we theoretically demonstrate that a DLIF neuron can capture input correlations, enabling it to perform nonlinear classification tasks. At the network level, we prove that DLIF neurons can preserve and propagate correlation structures from the input layer to the readout layer. These theoretical findings are further confirmed by our numerical experiments. Extensive experiments across diverse architectures—including ResNet, VGG, and Transformer—demonstrate that DLIF achieves state-of-the-art performance on static (CIFAR-10/100, ImageNet) and neuromorphic (DVS-Gesture, DVS-CIFAR10) benchmarks, surpassing LIF and other advanced alternatives while maintaining comparable computational cost. This work provides a biologically plausible and computationally powerful spiking neuron model, paving the way for next-generation brain-inspired computing.

Applications · Genetics, Cell Biology, Health, etc

Jiarui Li, Zixiang Yin, Haley Smith, Zhengming Ding, Samuel Landry, Ramgopal Mettu

CD8+ “killer” T cells and CD4+ “helper” T cells play a central role in the adaptive immune system by recognizing antigens presented by Major Histocompatibility Complex (pMHC) molecules via T Cell Receptors (TCRs). Modeling binding between T cells and the pMHC complex is fundamental to understanding basic mechanisms of human immune response as well as in developing therapies. While transformer-based models such as TULIP have achieved impressive performance in this domain, their black-box nature precludes interpretability and thus limits a deeper mechanistic understanding of T cell response. Most existing post-hoc explainable AI (xAI) methods are confined to encoder-only, co-attention, or model-specific architectures and cannot handle encoder-decoder transformers used in TCR-pMHC modeling. To address this gap, we propose Quantifying Cross-Attention Interaction (QCAI), a new post-hoc method designed to interpret the cross-attention mechanisms in transformer decoders. Quantitative evaluation is a challenge for XAI methods; we have compiled TCR-XAI, a benchmark consisting of 274 experimentally determined TCR-pMHC structures to serve as ground truth for binding. Using these structures we compute physical distances between relevant amino acid residues in the TCR-pMHC interaction region and evaluate how well our method and others estimate the importance of residues in this region across the dataset. We show that QCAI achieves state-of-the-art performance on both interpretability and prediction accuracy under the TCR-XAI benchmark.

Applications · Chemistry and Drug Discovery

Zhiyu Wang, Bingxin Zhou, Weishu Zhao, Yang Tan, Jing Wang, Pietro Lio, liang hong

Proteins are essential biological macromolecules that execute life functions. Local motifs within protein structures, such as active sites, are the most critical components for linking structure to function and are key to understanding protein evolution and enabling protein engineering. Existing computational methods struggle to identify and compare these local structures, which leaves a significant gap in understanding protein structures and harnessing their functions. This study presents PLASMA, the first deep learning framework for efficient and interpretable residue-level protein substructure alignment. We reformulate the problem as a regularized optimal transport task and leverage differentiable Sinkhorn iterations. For a pair of input protein structures, PLASMA outputs a clear alignment matrix with an interpretable overall similarity score. Through extensive quantitative evaluations and three biological case studies, we demonstrate that PLASMA achieves accurate, lightweight, and interpretable residue-level alignment. Additionally, we introduce PLASMA-PF, a training-free variant that provides a practical alternative when training data are unavailable. Our method addresses a critical gap in protein structure analysis tools and offers new opportunities for functional annotation, evolutionary studies, and structure-based drug design. Reproducibility is ensured via our official implementation at https://github.com/ZW471/PLASMA-Protein-Local-Alignment.git.

Applications · Genetics, Cell Biology, Health, etc

Alice Del Vecchio, Andreas C Kapourani, Abdullah M Athar, Agnieszka Dobrowolska, Andrew Anighoro, Benjamin Tenmann, Lindsay Edwards, Cristian Regep

DNA language models are emerging as powerful tools for representing genomic sequences, with recent progress driven by self-supervised learning. However, performance on downstream tasks is sensitive to tokenization strategies reflecting the complex encodings in DNA, where both regulatory elements and single-nucleotide changes can be functionally significant. Yet existing models are fixed to their initial tokenization strategy; single-nucleotide encodings result in long sequences that challenge transformer architectures, while fixed multi-nucleotide schemes like byte pair encoding struggle with character level modeling. Drawing inspiration from the Byte Latent Transformer's combining of bytes into patches, we propose that 'patching' provides a competitive and more efficient alternative to tokenization for DNA sequences. Furthermore, patching eliminates the need for a fixed vocabulary, which offers unique advantages to DNA. Leveraging this, we propose a biologically informed strategy, using evolutionary conservation scores as a guide for 'patch' boundaries. By prioritizing conserved regions, our approach directs computational resources to the most functionally relevant parts of the DNA sequence. We show that models up to an order of magnitude smaller surpass current state-of-the-art performance in existing DNA benchmarks. Importantly, our approach provides the flexibility to change patching without retraining, overcoming a fundamental limitation of current tokenization methods.

Deep Learning · Everything Else

Md Zesun Ahmed Mia, Malyaban Bal, Abhronil Sengupta

The quadratic complexity of self-attention mechanism presents a significant impediment to applying Transformer models to long sequences. This work explores computational principles derived from astrocytes—glial cells critical for biological memory and synaptic modulation—as a complementary approach to conventional architectural modifications for efficient self-attention. We introduce the Recurrent Memory Augmented Astromorphic Transformer (RMAAT), an architecture integrating abstracted astrocyte functionalities. RMAAT employs a recurrent, segment-based processing strategy where persistent memory tokens propagate contextual information. An adaptive compression mechanism, governed by a novel retention factor derived from simulated astrocyte long-term plasticity (LTP), modulates these tokens. Attention within segments utilizes an efficient, linear-complexity mechanism inspired by astrocyte short-term plasticity (STP). Training is performed using Astrocytic Memory Replay Backpropagation (AMRB), a novel algorithm designed for memory efficiency in recurrent networks. Evaluations on the Long Range Arena (LRA) benchmark demonstrate RMAAT's competitive accuracy and substantial improvements in computational and memory efficiency, indicating the potential of incorporating astrocyte-inspired dynamics into scalable sequence models.

Applications · Neuroscience, Cognitive Science

Mingqing Xiao, Qingyan Meng, Zongpeng Zhang, Di He, Dongsheng Li, Zhouchen Lin

Brain-inspired neuromorphic computing with spiking neural networks (SNNs) is a promising energy-efficient computational approach. However, successfully training deep SNNs in a more biologically plausible and neuromorphic-hardware-friendly way is still challenging. Most recent methods leverage spatial and temporal backpropagation (BP), not adhering to neuromorphic properties. Despite the efforts of some online training methods, tackling spatial credit assignments by alternatives with competitive performance as spatial BP remains a significant problem. In this work, we propose a novel method, online pseudo-zeroth-order (OPZO) training. Our method only requires a single forward propagation with noise injection and direct top-down signals for spatial credit assignment, avoiding spatial BP's problem of symmetric weights and separate phases for layer-by-layer forward-backward propagation. OPZO solves the large variance problem of zeroth-order methods by the pseudo-zeroth-order formulation and momentum feedback connections, while having more guarantees than random feedback. Combining online training, OPZO can pave paths to on-chip SNN training. Experiments on neuromorphic and static datasets with both fully connected and convolutional networks demonstrate the effectiveness of OPZO with competitive performance compared with spatial BP, as well as estimated low training costs.

Applications · Genetics, Cell Biology, Health, etc

Qiangwei Peng, Zihan Wang, Junda Ying, Yuhao Sun, Qing Nie, Lei Zhang, Tiejun Li, Peijie Zhou

The Wasserstein–Fisher–Rao (WFR) metric extends dynamic optimal transport (OT) by coupling displacement with change of mass, providing a principled geometry for modeling unbalanced snapshot dynamics. Existing WFR solvers, however, are often unstable, computationally expensive, and difficult to scale. Here we introduce \textbf{WFR Flow Matching (WFR-FM)}, a simulation-free training algorithm that unifies flow matching with dynamic unbalanced OT. Unlike classical flow matching which regresses only a transport vector field, WFR-FM simultaneously regresses a vector field for displacement and a scalar growth rate function for birth–death dynamics, yielding continuous flows under the WFR geometry. Theoretically, we show that minimizing the WFR-FM loss exactly recovers WFR geodesics. Empirically, WFR-FM yields more accurate and robust trajectory inference in single-cell biology, reconstructing consistent dynamics with proliferation and apoptosis, estimating time-varying growth fields, and applying to generative dynamics under imbalanced data. It outperforms state-of-the-art baselines in efficiency, stability, and reconstruction accuracy. Overall, WFR-FM establishes a unified and efficient paradigm for learning dynamical systems from unbalanced snapshots, where not only states but also mass evolve over time. The Python code is available at .

Deep Learning · Generative Models and Autoencoders

Roman Tarasov, Petr Mokrov, Milena Gazdieva, Evgeny Burnaev, Aleksandr Korotin

Neural network-based optimal transport (OT) is a recent and fruitful direction in the generative modeling community. It finds its applications in various fields such as domain translation, image super-resolution, computational biology and others. Among the existing OT approaches, of considerable interest are adversarial minimax solvers based on semi-dual formulations of OT problems. While promising, these methods lack theoretical investigation from a statistical learning perspective. Our work fills this gap by establishing upper bounds on the generalization error of an approximate OT map recovered by the minimax quadratic OT solver. Importantly, the bounds we derive depend solely on some standard statistical and mathematical properties of the considered functional classes (neural nets). While our analysis focuses on the quadratic OT, we believe that similar bounds could be derived for general OT case, paving the promising direction for future research. Our experimental illustrations are available online https://github.com/milenagazdieva/StatOT.

Computer Vision · Classification and Understanding

Kanglin Qu, Pan Gao, Qun Dai, Yuanhao Sun

Synergistically capturing intricate local structures and global contextual dependencies has become a critical challenge in point cloud representation learning. To address this, we introduce PointLearner, a point cloud representation learning network that closely aligns with biological vision which employs an active, foveation-inspired processing strategy, thus enabling local geometric modeling and long-range dependency interactions simultaneously. Specifically, we first design a point-focused attention, which simulates foveal vision at the visual focus through a competitive normalized attention mechanism between local neighbors and spatially downsampled features. The spatially downsampled features are extracted by a pooling method based on learnable inducing points, which can flexibly adapt to the non-uniform distribution of point clouds as the number of inducing points is controlled and they interact directly with point clouds. Second, we propose a context-scan state space that mimics eye's saccade inference, which infers the overall semantic structure and spatial content in the scene through a scan path guided by the Hilbert curve for the bidirectional S6. With this focus-then-context biomimetic design, PointLearner demonstrates remarkable robustness and achieves state-of-the-art performance across multiple point cloud tasks. The code is available at https://github.com/Point-Cloud-Learning/PointLearner.

General Machine Learning · Representation Learning

Gong Zhiren, Chao Yang, Wendi Ren, Shuang Li

Humans navigate daily life by combining two modes of behavior: deliberate planning in novel situations and fast, automatic responses in familiar ones. Modeling human decision-making therefore requires capturing how people switch between these modes. We present a framework for learning human habits with rule-guided active inference, extending the view of the brain as a prediction machine that minimizes mismatches between expectations and observations, and computationally modeling of human(-like) behavior and habits. In our approach, habits emerge as symbolic rules that serve as compact, interpretable shortcuts for action. To learn these rules alongside the human models, we design a biologically inspired wake--sleep algorithm. In the wake phase, the agent engages in active inference on real trajectories: reconstructing states, updating beliefs, and harvesting candidate rules that reliably reduce free energy. In the sleep phase, the agent performs generative replay with its world model, refining parameters and consolidating or pruning rules by minimizing joint free energy. This alternating rule–model consolidation lets the agent build a reusable habit library while preserving the flexibility to plan. Experiments on basketball player movements, car-following behavior, medical diagnosis, and visual game strategy demonstrate that our framework improves predictive accuracy and efficiency compared to logic-based, deep learning, LLM-based, model-based RL, and prior active inference baselines, while producing interpretable rules that mirror human-like habits.

Applications · Genetics, Cell Biology, Health, etc

Yihang Xiao, Jinyi Liu, YAN ZHENG, Shaoqing Jiao, Jianye Hao, Xiaohan Xie, Limingzhi, Ruitao Wang, Fei Ni, Yuxiao Li 等

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data analysis are pivotal for advancing biological research, enabling precise characterization of cellular heterogeneity. However, existing analysis approaches require extensive manual programming and complex tool integration, posing significant challenges for researchers. To address this, we introduce CellAgent, an autonomous, LLM-driven approach that performs end-to-end scRNA-seq and spatial transcriptomics data analysis through natural language interactions. CellAgent employs a multi-agent hierarchical decision-making framework, simulating a “deep-thinking” workflow to ensure that analytical steps are logically coherent and aligned with the overarching research goal. To further enhance its capabilities, we develop sc-Omni, a high-performance, expert-curated toolkit that consolidates essential tools for scRNA-seq and spatial transcriptomics analysis. Additionally, we introduce a self-reflective optimization mechanism, enabling automated, iterative refinement of results through specialized evaluation methods, effectively replacing traditional manual assessments. Benchmarking against human experts demonstrates that CellAgent achieves significant improvement in efficiency across multiple downstream applications while maintaining excellent performance comparable to existing approaches and preserving natural language interactions. By translating high-level scientific questions into optimized computational workflows, CellAgent represents a step toward a new, more accessible paradigm in bioinformatics, allowing researchers to perform complex data analyses autonomously. In lowering technical barriers, CellAgent serves to advance the democratization of the scientific discovery process in genomics.

Applications · Genetics, Cell Biology, Health, etc

Heming Zhang, Di Huang, Wenyu Li, Michael Province, Yixin Chen, Philip Payne, Fuhai Li

In precision medicine, quantitative multi-omic features, topological context, and textual biological knowledge play vital roles in identifying disease-critical signaling pathways and targets, guiding the discovery of novel therapeutics and effective treatment strategies. Existing pipelines capture only one or two of these—numerical omics ignore topological context, text-centric LLMs lack quantitative grounded reasoning, and graph-only models underuse rich node semantics and the generalization power of LLMs—thereby limiting mechanistic interpretability. Although Process Reward Models (PRMs) aim to guide reasoning in LLMs, they remain limited by coarse step definitions, unreliable intermediate evaluation, and vulnerability to reward hacking with added computational cost. These gaps motivate jointly integrating quantitative multi-omic signals, topological structure with node annotations, and literature-scale text via LLMs, using subgraph reasoning as the principle bridge linking numeric evidence, topological knowledge and language context. To resolve this challenge, we propose GALAX (Graph Augmented LAnguage model with eXplainability), an innovative framework that integrates pretrained Graph Neural Networks (GNNs) into Large Language Models (LLMs) via reinforcement learning guided by a Graph Process Reward Model (GPRM), which generates disease-relevant subgraphs in a step-wise manner initiated by an LLM and iteratively evaluated by a pretrained GNN and schema-based rule check, enabling process-level supervision without explicit labels. As an application, we also introduced Target-QA, a benchmark combining CRISPR-identified targets, multi-omic profiles, and biomedical graph knowledge across diverse cancer cell lines, which enables GNN pretraining for supervising step-wise graph construction and supports long-context reasoning over text-numeric graphs (TNGs), providing a scalable and biologically grounded framework for explainable, reinforcement-guided subgraph reasoning toward reliable and interpretable target and pathway discovery in precision medicine.

Applications · Genetics, Cell Biology, Health, etc

Kai Zhuang, Jiawei Zhang, Yumou Liu, Hanqun Cao, Chunbin Gu, Mengdi Liu, Zhangyang Gao, Zitong Jerry Wang, Xuanhe Zhou, Pheng-Ann Heng 等

Scientific Large Language Models (Sci-LLMs) have emerged as a promising frontier for accelerating biological discovery. However, these models face a fundamental challenge when processing raw biomolecular sequences: the tokenization dilemma. Whether treating sequences as a specialized language, risking the loss of functional motif information, or as a separate modality, introducing formidable alignment challenges, current strategies fundamentally limit their reasoning capacity. We challenge this sequence-centric paradigm by positing that a more effective strategy is to provide Sci-LLMs with high-level structured context derived from established bioinformatics tools, thereby bypassing the need to interpret low-level noisy sequence data directly. Through a systematic comparison of leading Sci-LLMs on biological reasoning tasks, we tested three input modes: sequence-only, context-only, and a combination of both. Our findings are striking: the context-only approach consistently and substantially outperforms all other modes. Even more revealing, the inclusion of the raw sequence alongside its high-level context consistently degrades performance, indicating that raw sequences act as informational noise, even for models with specialized tokenization schemes. These results suggest that the primary strength of existing Sci-LLMs lies not in their nascent ability to interpret biomolecular syntax from scratch, but in their profound capacity for reasoning over structured, human-readable knowledge. Therefore, we argue for reframing Sci-LLMs not as sequence decoders, but as powerful reasoning engines over expert knowledge. This work lays the foundation for a new class of hybrid scientific AI agents, repositioning the developmental focus from direct sequence interpretation towards high-level knowledge synthesis.

Applications · Genetics, Cell Biology, Health, etc

Jianan Zhao, Zhihao Zhan, Narendra Chaudhary, Xinyu Yuan, Zuobai Zhang, Qian Cong, Jian Zhou, Sanchit Misra, Jian Tang

Protein-protein interactions (PPIs) are mediated at the residue level. Most sequence-based PPI models consider residue-residue interactions across two proteins, which can yield accurate interaction scores but are too slow to scale. At proteome scale, identifying candidate PPIs requires evaluating nearly *all possible protein pairs*. For $N$ proteins of average length $L$, exhaustive all-against-all search requires $\mathcal{O}(N^2L^2)$ computation, rendering conventional approaches computationally impractical. We introduce RaftPPI, a scalable framework that approximates residue-level PPI modeling while enabling efficient large-scale retrieval. RaftPPI represents residue interactions with a Gaussian kernel, approximated efficiently via structured random Fourier features, and applies a low-rank factorized attention mechanism that admits pooling into a compact embedding per protein. Each protein is encoded once into an indexable embedding, allowing approximate nearest-neighbor search to replace exhaustive pairwise scoring, reducing proteome-wide retrieval from *months* to *minutes* on a single GPU or CPU. On the human proteome with the D-SCRIPT dataset, RaftPPI retrieves the top 20% pairs from $\sim$200M candidate pairs in 5.7 minutes on an A100 GPU, or 3.3 minutes on an Intel Xeon 6980P CPU, covering 75.1% of the true interacting pairs, compared to 4.9 GPU months for the best prior method (61.2%). Across seven benchmarks with sequence- and degree-controlled splits, RaftPPI achieves state-of-the-art PPI classification and retrieval performance, while enabling residue-aware, retrieval-friendly screening at proteome scale.

Applications · Genetics, Cell Biology, Health, etc

Suyi Chen, Haibin Ling

As a critical modality for structural biology, cryogenic electron microscopy (cryo-EM) facilitates the determination of macromolecular structures at near-atomic resolution. The core computational task in single-particle cryo-EM is to reconstruct the 3D electrostatic potential of a molecule from noisy 2D projections acquired at unknown orientations. Gaussian mixture models (GMMs) provide a continuous, compact, and physically interpretable representation for molecular density and have recently gained interest in cryo-EM reconstruction. However, existing methods rely on external consensus maps or atomic models for initialization, limiting their use in self-contained pipelines. In parallel, differentiable rendering techniques such as Gaussian splatting have demonstrated remarkable scalability and efficiency for volumetric representations, suggesting a natural fit for GMM-based cryo-EM reconstruction. However, off-the-shelf Gaussian splatting methods are designed for photorealistic view synthesis and remain incompatible with cryo-EM due to mismatches in the image formation physics, reconstruction objectives, and coordinate systems. Addressing these issues, we propose **cryoSplat**, a GMM-based method that integrates Gaussian splatting with the physics of cryo-EM image formation. In particular, we develop an orthogonal projection-aware Gaussian splatting, with adaptations such as a view-dependent normalization term and FFT-aligned coordinate system tailored for cryo-EM imaging. These innovations enable stable and efficient homogeneous reconstruction directly from raw cryo-EM particle images using random initialization. Experimental results on real datasets validate the effectiveness and robustness of cryoSplat over representative baselines. The code will be released at https://github.com/Chen-Suyi/cryosplat.

Applications · Chemistry and Drug Discovery

Feng Jiang, Maolaaisha Aminanmu, Hehuan Ma, Yuzhi Guo, Tommaso Mansi, Junzhou Huang, Mangal Prakash, Rui Liao

Drug target interaction (DTI) prediction is a cornerstone of computational drug discovery, enabling rational design, repurposing, and mechanistic insights. While deep learning has advanced DTI modeling, existing approaches primarily rely on SMILES–protein pairs and fail to exploit the rich multimodal information available for small molecules and proteins. Inspired by recent successes in multimodal molecular property prediction, we introduce GRAM-DTI, a pre-training framework that integrates multimodal small molecule and protein inputs into a unified representation. GRAM-DTI extends volume-based contrastive learning to four modalities, capturing higher-order semantic alignment beyond conventional pairwise approaches. To handle modality informativeness, we propose adaptive modality dropout, dynamically regulating each modality’s contribution during pretraining. Additionally, IC50 activity measurements, when available, are incorporated as weak supervision to ground representations in biologically meaningful interaction strengths. Experiments on four publicly available datasets demonstrate that GRAM-DTI consistently outperforms state-of-the-art baselines. Our results highlight the benefits of higher-order multimodal alignment, adaptive modality utilization, and auxiliary supervision for robust and generalizable DTI prediction. Our code is available at https://github.com/uta-smile/GRAM-DTI.

Optimization · Everything Else

Hongzheng Chen, Yingheng Wang, Yaohui Cai, Hins Hu, Jiajie Li, Shirley Huang, Chenhui Deng, Rongjian Liang, Shufeng Kong, Haoxing Ren 等

While Large Language Models (LLMs) have demonstrated significant advancements in reasoning and agent-based problem-solving, current evaluation methodologies fail to adequately assess their capabilities: existing benchmarks either rely on closed-ended questions prone to saturation and memorization, or subjective comparisons that lack consistency and rigor. In this work, we introduce HeuriGym, an agentic framework designed for evaluating heuristic algorithms generated by LLMs for combinatorial optimization problems, characterized by clearly defined objectives and expansive solution spaces. HeuriGym empowers LLMs to propose heuristics, receive evaluative feedback via code execution, and iteratively refine their solutions. We evaluate nine state-of-the-art models on various problems across domains such as computer systems, logistics, and biology, exposing persistent limitations in tool use, planning, and adaptive reasoning. To quantify performance, we propose the Quality-Yield Index (QYI), a metric that captures both solution pass rate and quality. Even top models like GPT-o4-mini-high and Gemini-2.5-Pro attain QYI scores of only 0.6, well below the expert baseline of 1. Our open-source benchmark aims to guide the development of LLMs toward more effective and realistic problem-solving in scientific and engineering domains.

Theory · Learning Theory

Zhuo Liu, Tao Chen

Brain-like intelligent systems need brain-like learning methods. Equilibrium Propagation (EP) is a biologically plausible learning framework with strong potential for brain-inspired computing hardware. However, existing implementations of EP suffer from instability and prohibitively high computational costs. Inspired by the structure and dynamics of the brain, we propose a biologically plausible Feedback-regulated REsidual recurrent neural network (FRE-RNN) and study its learning performance in EP framework. Feedback regulation enables rapid convergence by attenuating feedback signals and reducing the disturbance of feedback path to feedforward path. The improvement in convergence property reduces the computational cost and training time of EP by orders of magnitude, delivering performance on par with backpropagation (BP) in benchmark tasks. Meanwhile, residual connections with brain-inspired topologies help alleviate the vanishing gradient problem that arises when feedback pathways are weak in deep RNNs. Our approach substantially enhances the applicability and practicality of EP. The techniques developed here also offer guidance to implementing in-situ learning in physical neural networks.

Theory · Interpretability and Visualization

Giordano Ramos-Traslosheros, Carlos Ponce

Signed connectivity is fundamental to neural computation in both brains (excitatory/inhibitory) and machines (positive/negative). Yet the role of signed weights in shaping visual representations in object recognition remains unclear. Dale's Law, the biological principle that neurons send exclusively excitatory or inhibitory outputs, is typically not enforced in artificial neural networks (ANNs). Here, we find that accuracy in ImageNet-trained ANNs correlates with the spontaneous emergence of sign-specific "Dale-like" segregation in their output layers. Ablation and feature visualization reveal a functional segregation in ANNs: removing positive inputs primarily disrupts localized, object-related structure, while removing negative inputs alters mainly dispersed background textures. This segregation is more pronounced in adversarially robust models, persists with unsupervised learning, and vanishes with non-rectified activation functions. We validate these observations in the macaque ventral visual cortex (V1, V4, and IT) using encoding models and in vivo feature visualization. The features recovered by encoding models qualitatively matched those identified in vivo. Model representations changed more upon positive than negative input ablations. We analyzed the most Dale-like units across neuron models, positive units showed localized features, while negative units showed larger, more dispersed features. Consistent with this, experimentally clearing the background around a neuron's preferred feature enhanced its response, likely by reducing inhibitory drive. Our results suggest that both artificial and biological vision systems segregate features by weight sign: positive weights emphasize object-related features, while negative weights refine context. This highlights a convergent representational strategy in brains and machines, yielding predictions for visual neuroscience.