pwc-archive/papers-with-abstracts huggingface dataset:
State- versus Reaction-Based Information Processing in Biochemical Networks
Authors: Anne-Lena Moor, Age Tjalma, Manuel Reinhardt, Pieter Rein ten Wolde, Christoph Zechner
Date: 2025-05-19 00:00:00

Trajectory mutual information is frequently used to quantify information transfer in biochemical systems. Tractable solutions of the trajectory mutual information can be obtained via the widely used Linear-Noise Approximation (LNA) using Gaussian channel theory. This approach is expected to be accurate for sufficiently large systems. However, recent observations show that there are cases, where the mutual information obtained this way differs qualitatively from results derived using an exact Markov jump process formalism, and that the differences persist even for large systems. In this letter, we show that these differences can be explained by introducing the notion of reaction- versus state-based descriptions of trajectories. In chemical systems, the information is encoded in the sequence of reaction events, and the reaction-based trajectories of Markov jump processes capture this information. In contrast, the commonly used form of the LNA uses a state (concentration) based description of trajectories, which contains, in general, less information than a reaction-based description. Here, we show that an alternative formulation of the LNA that retains the reaction-specific information of trajectories can accurately describe the trajectory mutual information for large systems. We illustrate the consequences of different trajectory descriptions for two common cellular reaction motifs and discuss the connection with Berg-Purcell and Maximum-Likelihood sensing.
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery
Authors: Yifan Dai, Xuanbai Ren, Tengfei Ma, Qipeng Yan, Yiping Liu, Yuansheng Liu, Xiangxiang Zeng
Date: 2025-05-17 00:00:00
Tasks: Drug Discovery, Few-Shot Learning, Molecular Property Prediction, molecular representation, Property Prediction

Accurate molecular property prediction (MPP) is a critical step in modern drug development. However, the scarcity of experimental validation data poses a significant challenge to AI-driven research paradigms. Under few-shot learning scenarios, the quality of molecular representations directly dictates the theoretical upper limit of model performance. We present AdaptMol, a prototypical network integrating Adaptive multimodal fusion for Molecular representation. This framework employs a dual-level attention mechanism to dynamically integrate global and local molecular features derived from two modalities: SMILES sequences and molecular graphs. (1) At the local level, structural features such as atomic interactions and substructures are extracted from molecular graphs, emphasizing fine-grained topological information; (2) At the global level, the SMILES sequence provides a holistic representation of the molecule. To validate the necessity of multimodal adaptive fusion, we propose an interpretable approach based on identifying molecular active substructures to demonstrate that multimodal adaptive fusion can efficiently represent molecules. Extensive experiments on three commonly used benchmarks under 5-shot and 10-shot settings demonstrate that AdaptMol achieves state-of-the-art performance in most cases. The rationale-extracted method guides the fusion of two modalities and highlights the importance of both modalities.
From Static to Dynamic: Exploring Temporal Networks in Systems Biology
Authors: Abir Khazaal, Fatemeh Vafaee
Date: 2025-05-15 00:00:00

Network science has become an essential interdisciplinary tool for understanding complex biological systems. However, because these systems undergo continuous, often stimulus-driven changes in both structure and function, traditional static network approaches frequently fall short in capturing their dynamic nature. Dynamic network analysis (DNA) addresses this limitation and offers a powerful framework to investigate these evolving relationships. This work focuses on temporal networks, a central paradigm within DNA, as an effective approach for modelling time-resolved changes in biological systems. While DNA has gained traction in domains like social and communication sciences, its integration in biology has been more gradual, hindered by data limitations and the need for domain-specific adaptations. Aimed at supporting researchers, particularly those new to the field, the review offers an integrative overview of the diverse and multidisciplinary landscape of DNA, with a focus on temporal networks in systems biology. I begin by clarifying foundational terminology and concepts, then present a multi-scale perspective spanning microscale (nodes and edges), mesoscale (motifs and communities), and macroscale (global topology) analyses. Finally, I explore analytical strategies and computational tools suited to various research objectives, including methods for detecting structural shifts, assessing network similarity, tracking module evolution, and predictive modelling of future network states.
Inference with correlated priors using sisters cells
Authors: Sina Tootoonian, Andreas T. Schaefer
Date: 2025-05-20 00:00:00

A common view of sensory processing is as probabilistic inference of latent causes from receptor activations. Standard approaches often assume these causes are a priori independent, yet real-world generative factors are typically correlated. Representing such structured priors in neural systems poses architectural challenges, particularly when direct interactions between units representing latent causes are biologically implausible or computationally expensive. Inspired by the architecture of the olfactory bulb, we propose a novel circuit motif that enables inference with correlated priors without requiring direct interactions among latent cause units. The key insight lies in using sister cells: neurons receiving shared receptor input but connected differently to local interneurons. The required interactions among latent units are implemented indirectly through their connections to the sister cells, such that correlated connectivity implies anti-correlation in the prior and vice versa. We use geometric arguments to construct connectivity that implements a given prior and to bound the number of causes for which such priors can be constructed. Using simulations, we demonstrate the efficacy of such priors for inference in noisy environments and compare the inference dynamics to those experimentally observed. Finally, we show how, under certain assumptions on latent representations, the prior used can be inferred from sister cell activations. While biologically grounded in the olfactory system, our mechanism generalises to other natural and artificial sensory systems and may inform the design of architectures for efficient inference under correlated latent structure.
The nature of quantum parallel processing and its implications for coding in brain neural networks: a novel computational mechanism
Authors: Andrew S Johnson, William Winlow
Date: 2025-05-20 00:00:00

Conventionally it is assumed that the nerve impulse is an electrical process based upon the observation that electrical stimuli produce an action potential as defined by Hodgkin Huxley (1952) (HH). Consequently, investigations into the computation of nerve impulses have almost universally been directed to electrically observed phenomenon. However, models of computation are fundamentally flawed and assume that an undiscovered timing system exists within the nervous system. In our view it is synchronisation of the action potential pulse (APPulse) that effects computation. The APPulse, a soliton pulse, is a novel purveyor of computation and is a quantum mechanical pulse: i.e. It is a non-Turing synchronised computational event. Furthermore, the APPulse computational interactions change frequencies measured in microseconds, rather than milliseconds, producing effective efficient computation. However, the HH action potential is a necessary component for entropy equilibrium, providing energy to open ion channels, but it is too slow to be functionally computational in a neural network. Here, we demonstrate that only quantum non-electrical soliton pulses converging to points of computation are the main computational structure with synaptic transmission occurring at slower millisecond speeds. Thus, the APPulse accompanying the action potential is the purveyor of computation; a novel computational mechanism, that is incompatible with Turing timed computation and artificial intelligence (AI).
Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment
Authors: Yang Hu, Runchen Wang, Stephen Chong Zhao, Xuhui Zhan, Do Hun Kim, Mark Wallace, David A. Tovar
Date: 2025-05-20 00:00:00
Tasks: Representation Learning, Retrieval, Self-Supervised Learning, Triplet, zero-shot-classification, Zero-Shot Learning

We introduce Perceptual-Initialization (PI), a paradigm shift in visual representation learning that incorporates human perceptual structure during the initialization phase rather than as a downstream fine-tuning step. By integrating human-derived triplet embeddings from the NIGHTS dataset to initialize a CLIP vision encoder, followed by self-supervised learning on YFCC15M, our approach demonstrates significant zero-shot performance improvements, without any task-specific fine-tuning, across 29 zero shot classification and 2 retrieval benchmarks. On ImageNet-1K, zero-shot gains emerge after approximately 15 epochs of pretraining. Benefits are observed across datasets of various scales, with improvements manifesting at different stages of the pretraining process depending on dataset characteristics. Our approach consistently enhances zero-shot top-1 accuracy, top-5 accuracy, and retrieval recall (e.g., R@1, R@5) across these diverse evaluation tasks, without requiring any adaptation to target domains. These findings challenge the conventional wisdom of using human-perceptual data primarily for fine-tuning and demonstrate that embedding human perceptual structure during early representation learning yields more capable and vision-language aligned systems that generalize immediately to unseen tasks. Our work shows that "beginning with you", starting with human perception, provides a stronger foundation for general-purpose vision-language intelligence.
Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning
Authors: Viet Anh Khoa Tran, Emre Neftci, Willem. A. M. Wybo
Date: 2025-05-20 00:00:00
Tasks: Continual Learning, Contrastive Learning, Transfer Learning

Biological brains learn continually from a stream of unlabeled data, while integrating specialized information from sparsely labeled examples without compromising their ability to generalize. Meanwhile, machine learning methods are susceptible to catastrophic forgetting in this natural learning setting, as supervised specialist fine-tuning degrades performance on the original task. We introduce task-modulated contrastive learning (TMCL), which takes inspiration from the biophysical machinery in the neocortex, using predictive coding principles to integrate top-down information continually and without supervision. We follow the idea that these principles build a view-invariant representation space, and that this can be implemented using a contrastive loss. Then, whenever labeled samples of a new class occur, new affine modulations are learned that improve separation of the new class from all others, without affecting feedforward weights. By co-opting the view-invariance learning mechanism, we then train feedforward weights to match the unmodulated representation of a data sample to its modulated counterparts. This introduces modulation invariance into the representation space, and, by also using past modulations, stabilizes it. Our experiments show improvements in both class-incremental and transfer learning over state-of-the-art unsupervised approaches, as well as over comparable supervised approaches, using as few as 1% of available labels. Taken together, our work suggests that top-down modulations play a crucial role in balancing stability and plasticity.
Language Models Are Capable of Metacognitive Monitoring and Control of Their Internal Activations
Authors: Li Ji-An, Hua-Dong Xiong, Robert C. Wilson, Marcelo G. Mattar, Marcus K. Benna
Date: 2025-05-19 00:00:00
Tasks: Sentence

Large language models (LLMs) can sometimes report the strategies they actually use to solve tasks, but they can also fail to do so. This suggests some degree of metacognition -- the capacity to monitor one's own cognitive processes for subsequent reporting and self-control. Metacognitive abilities enhance AI capabilities but raise safety concerns, as models might obscure their internal processes to evade neural-activation-based oversight mechanisms designed to detect harmful behaviors. Given society's increased reliance on these models, it is critical that we understand the limits of their metacognitive abilities, particularly their ability to monitor their internal activations. To address this, we introduce a neuroscience-inspired neurofeedback paradigm designed to quantify the ability of LLMs to explicitly report and control their activation patterns. By presenting models with sentence-label pairs where labels correspond to sentence-elicited internal activations along specific directions in the neural representation space, we demonstrate that LLMs can learn to report and control these activations. The performance varies with several factors: the number of example pairs provided, the semantic interpretability of the target neural direction, and the variance explained by that direction. These results reveal a "metacognitive space" with dimensionality much lower than the model's neural space, suggesting LLMs can monitor only a subset of their neural mechanisms. Our findings provide empirical evidence quantifying metacognitive capabilities in LLMs, with significant implications for AI safety.
ReBaCCA-ss: Relevance-Balanced Continuum Correlation Analysis with Smoothing and Surrogating for Quantifying Similarity Between Population Spiking Activities
Authors: Xiang Zhang, Chenlin Xu, Zhouxiao Lu, Haonan Wang, Dong Song
Date: 2025-05-19 00:00:00

Quantifying similarity between population spike patterns is essential for understanding how neural dynamics encode information. Traditional approaches, which combine kernel smoothing, PCA, and CCA, have limitations: smoothing kernel bandwidths are often empirically chosen, CCA maximizes alignment between patterns without considering the variance explained within patterns, and baseline correlations from stochastic spiking are rarely corrected. We introduce ReBaCCA-ss (Relevance-Balanced Continuum Correlation Analysis with smoothing and surrogating), a novel framework that addresses these challenges through three innovations: (1) balancing alignment and variance explanation via continuum canonical correlation; (2) correcting for noise using surrogate spike trains; and (3) selecting the optimal kernel bandwidth by maximizing the difference between true and surrogate correlations. ReBaCCA-ss is validated on both simulated data and hippocampal recordings from rats performing a Delayed Nonmatch-to-Sample task. It reliably identifies spatio-temporal similarities between spike patterns. Combined with Multidimensional Scaling, ReBaCCA-ss reveals structured neural representations across trials, events, sessions, and animals, offering a powerful tool for neural population analysis.
Learning Dynamics of RNNs in Closed-Loop Environments
Authors: Yoav Ger, Omri Barak
Date: 2025-05-19 00:00:00

Recurrent neural networks (RNNs) trained on neuroscience-inspired tasks offer powerful models of brain computation. However, typical training paradigms rely on open-loop, supervised settings, whereas real-world learning unfolds in closed-loop environments. Here, we develop a mathematical theory describing the learning dynamics of linear RNNs trained in closed-loop contexts. We first demonstrate that two otherwise identical RNNs, trained in either closed- or open-loop modes, follow markedly different learning trajectories. To probe this divergence, we analytically characterize the closed-loop case, revealing distinct stages aligned with the evolution of the training loss. Specifically, we show that the learning dynamics of closed-loop RNNs, in contrast to open-loop ones, are governed by an interplay between two competing objectives: short-term policy improvement and long-term stability of the agent-environment interaction. Finally, we apply our framework to a realistic motor control task, highlighting its broader applicability. Taken together, our results underscore the importance of modeling closed-loop dynamics in a biologically plausible setting.
High-dimensional structure underlying individual differences in naturalistic visual experience
Authors: Chihye Han, Michael F. Bonner
Date: 2025-05-19 00:00:00

How do different brains create unique visual experiences from identical sensory input? While neural representations vary across individuals, the fundamental architecture underlying these differences remains poorly understood. Here, we reveal that individual visual experience emerges from a high-dimensional neural geometry across the visual cortical hierarchy. Using spectral decomposition of fMRI responses during naturalistic movie viewing, we find that idiosyncratic neural patterns persist across multiple orders of magnitude of latent dimensions. Remarkably, each dimensional range encodes qualitatively distinct aspects of individual processing, and this multidimensional neural geometry predicts subsequent behavioral differences in memory recall. These fine-grained patterns of inter-individual variability cannot be reduced to those detected by conventional intersubject correlation measures. Our findings demonstrate that subjective visual experience arises from information integrated across an expansive multidimensional manifold. This geometric framework offers a powerful new lens for understanding how diverse brains construct unique perceptual worlds from shared experiences.
Learning High-Order Relationships with Hypergraph Attention-based Spatio-Temporal Aggregation for Brain Disease Analysis
Authors: Wenqi Hu, Xuerui Su, Guanliang Li, Yidi Pan, Aijing Lin
Date: 2025-05-17 00:00:00
Tasks: Functional Connectivity

Traditional functional connectivity based on functional magnetic resonance imaging (fMRI) can only capture pairwise interactions between brain regions. Hypergraphs, which reveal high-order relationships among multiple brain regions, have been widely used for disease analysis. However, existing methods often rely on predefined hypergraph structures, limiting their ability to model complex patterns. Moreover, temporal information, an essential component of brain high-order relationships, is frequently overlooked. To address these limitations, we propose a novel framework that jointly learns informative and sparse high-order brain structures along with their temporal dynamics. Inspired by the information bottleneck principle, we introduce an objective that maximizes information and minimizes redundancy, aiming to retain disease-relevant high-order features while suppressing irrelevant signals. Our model comprises a multi-hyperedge binary mask module for hypergraph structure learning, a hypergraph self-attention aggregation module that captures spatial features through adaptive attention across nodes and hyperedges, and a spatio-temporal low-dimensional network for extracting discriminative spatio-temporal representations for disease classification. Experiments on benchmark fMRI datasets demonstrate that our method outperforms the state-of-the-art approaches and successfully identifies meaningful high-order brain interactions. These findings provide new insights into brain network modeling and the study of neuropsychiatric disorders.
BrainNetMLP: An Efficient and Effective Baseline for Functional Brain Network Classification
Authors: Jiacheng Hou, Zhenjie Song, Ercan Engin Kuruoglu
Date: 2025-05-14 00:00:00
Tasks: Classification

Recent studies have made great progress in functional brain network classification by modeling the brain as a network of Regions of Interest (ROIs) and leveraging their connections to understand brain functionality and diagnose mental disorders. Various deep learning architectures, including Convolutional Neural Networks, Graph Neural Networks, and the recent Transformer, have been developed. However, despite the increasing complexity of these models, the performance gain has not been as salient. This raises a question: Does increasing model complexity necessarily lead to higher classification accuracy? In this paper, we revisit the simplest deep learning architecture, the Multi-Layer Perceptron (MLP), and propose a pure MLP-based method, named BrainNetMLP, for functional brain network classification, which capitalizes on the advantages of MLP, including efficient computation and fewer parameters. Moreover, BrainNetMLP incorporates a dual-branch structure to jointly capture both spatial connectivity and spectral information, enabling precise spatiotemporal feature fusion. We evaluate our proposed BrainNetMLP on two public and popular brain network classification datasets, the Human Connectome Project (HCP) and the Autism Brain Imaging Data Exchange (ABIDE). Experimental results demonstrate pure MLP-based methods can achieve state-of-the-art performance, revealing the potential of MLP-based models as more efficient yet effective alternatives in functional brain network classification. The code will be available at https://github.com/JayceonHo/BrainNetMLP.
LLMs Do Not Have Human-Like Working Memory
Authors: Jen-tse Huang, Kaiser Sun, Wenxuan Wang, Mark Dredze
Date: 2025-04-30 00:00:00
Tasks: Math

Human working memory is an active cognitive system that enables not only the temporary storage of information but also its processing and utilization. Without working memory, individuals may produce unreal conversations, exhibit self-contradiction, and struggle with tasks requiring mental reasoning. In this paper, we demonstrate that Large Language Models (LLMs) lack this human-like cognitive ability, posing a significant challenge to achieving artificial general intelligence. We validate this claim through three experiments: (1) Number Guessing Game, (2) Yes or No Game, and (3) Math Magic. Experimental results on several model families indicate that current LLMs fail to exhibit human-like cognitive behaviors in these scenarios. By highlighting this limitation, we aim to encourage further research in developing LLMs with improved working memory capabilities.