pwc-archive/papers-with-abstracts huggingface dataset:
Towards a Molecular Computer: Enabling Arithmetic Operations in Molecular Communication
Authors: Jianqiao Long, Lei Zhang, Miaowen Wen, Kezhi Wang, Natalio Krasnogor, Jichun Li
Date: 2025-02-27 00:00:00

In current molecular communication (MC) systems, performing computational operations at the nanoscale remains challenging, restricting their applicability in complex scenarios such as adaptive biochemical control and advanced nanoscale sensing. To overcome this challenge, this paper proposes a novel framework that seamlessly integrates computation into the molecular communication process. The system enables arithmetic operations, namely addition, subtraction, multiplication, and division, by encoding numerical values into two types of molecules emitted by each transmitter to represent positive and negative values, respectively. Specifically, addition is achieved by transmitting non-reactive molecules, while subtraction employs reactive molecules that interact during propagation. The receiver demodulates molecular counts to directly compute the desired results. Theoretical analysis for an upper bound on the bit error rate (BER), and computational simulations confirm the system's robustness in performing complex arithmetic tasks. Compared to conventional MC methods, the proposed approach not only enables fundamental computational operations at the nanoscale but also lays the groundwork for intelligent, autonomous molecular networks.
Tuning-Free Structured Sparse PCA via Deep Unfolding Networks
Authors: Long Chen, Xianchao Xiu
Date: 2025-02-28 00:00:00
Tasks: Bayesian Optimization, Dimensionality Reduction, feature selection

Sparse principal component analysis (PCA) is a well-established dimensionality reduction technique that is often used for unsupervised feature selection (UFS). However, determining the regularization parameters is rather challenging, and conventional approaches, including grid search and Bayesian optimization, not only bring great computational costs but also exhibit high sensitivity. To address these limitations, we first establish a structured sparse PCA formulation by integrating $\ell_1$-norm and $\ell_{2,1}$-norm to capture the local and global structures, respectively. Building upon the off-the-shelf alternating direction method of multipliers (ADMM) optimization framework, we then design an interpretable deep unfolding network that translates iterative optimization steps into trainable neural architectures. This innovation enables automatic learning of the regularization parameters, effectively bypassing the empirical tuning requirements of conventional methods. Numerical experiments on benchmark datasets validate the advantages of our proposed method over the existing state-of-the-art methods. Our code will be accessible at https://github.com/xianchaoxiu/SPCA-Net.
Efficient Risk-sensitive Planning via Entropic Risk Measures
Authors: Alexandre Marthe, Samuel Bounan, Aurélien Garivier, Claire Vernade
Date: 2025-02-27 00:00:00
Tasks: Decision Making

Risk-sensitive planning aims to identify policies maximizing some tail-focused metrics in Markov Decision Processes (MDPs). Such an optimization task can be very costly for the most widely used and interpretable metrics such as threshold probabilities or (Conditional) Values at Risk. Indeed, previous work showed that only Entropic Risk Measures (EntRM) can be efficiently optimized through dynamic programming, leaving a hard-to-interpret parameter to choose. We show that the computation of the full set of optimal policies for EntRM across parameter values leads to tight approximations for the metrics of interest. We prove that this optimality front can be computed effectively thanks to a novel structural analysis and smoothness properties of entropic risks. Empirical results demonstrate that our approach achieves strong performance in a variety of decision-making scenarios.
Minimax Optimal Kernel Two-Sample Tests with Random Features
Authors: Soumya Mukherjee, Bharath K. Sriperumbudur
Date: 2025-02-28 00:00:00
Tasks: Computational Efficiency

Reproducing Kernel Hilbert Space (RKHS) embedding of probability distributions has proved to be an effective approach, via MMD (maximum mean discrepancy) for nonparametric hypothesis testing problems involving distributions defined over general (non-Euclidean) domains. While a substantial amount of work has been done on this topic, only recently, minimax optimal two-sample tests have been constructed that incorporate, unlike MMD, both the mean element and a regularized version of the covariance operator. However, as with most kernel algorithms, the computational complexity of the optimal test scales cubically in the sample size, limiting its applicability. In this paper, we propose a spectral regularized two-sample test based on random Fourier feature (RFF) approximation and investigate the trade-offs between statistical optimality and computational efficiency. We show the proposed test to be minimax optimal if the approximation order of RFF (which depends on the smoothness of the likelihood ratio and the decay rate of the eigenvalues of the integral operator) is sufficiently large. We develop a practically implementable permutation-based version of the proposed test with a data-adaptive strategy for selecting the regularization parameter and the kernel. Finally, through numerical experiments on simulated and benchmark datasets, we demonstrate that the proposed RFF-based test is computationally efficient and performs almost similar (with a small drop in power) to the exact test.
BAnG: Bidirectional Anchored Generation for Conditional RNA Design
Authors: Roman Klypa, Alberto Bietti, Sergei Grudinin
Date: 2025-02-28 00:00:00

Designing RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Existing computational approaches require a substantial amount of previously known interacting RNA sequences for each specific protein or a detailed knowledge of RNA structure, restricting their utility in practice. To address this limitation, we develop RNA-BAnG, a deep learning-based model designed to generate RNA sequences for protein interactions without these requirements. Central to our approach is a novel generative method, Bidirectional Anchored Generation (BAnG), which leverages the observation that protein-binding RNA sequences often contain functional binding motifs embedded within broader sequence contexts. We first validate our method on generic synthetic tasks involving similar localized motifs to those appearing in RNAs, demonstrating its benefits over existing generative approaches. We then evaluate our model on biological sequences, showing its effectiveness for conditional RNA sequence design given a binding protein.
Eukaryotes evade information storage-replication rate trade-off with endosymbiont assistance leading to larger genomes
Authors: Parthasarathi Sahu, Sashikanta Barik, Koushik Ghosh, Hemachander Subramanian
Date: 2025-02-28 00:00:00

Genome length varies widely among organisms, from compact genomes of prokaryotes to vast and complex genomes of eukaryotes. In this study, we theoretically identify the evolutionary pressures that may have driven this divergence in genome length. We use a parameter-free model to study genome length evolution under selection pressure to minimize replication time and maximize information storage capacity. We show that prokaryotes tend to reduce genome length, constrained by a single replication origin, while eukaryotes expand their genomes by incorporating multiple replication origins. We propose a connection between genome length and cellular energetics, suggesting that endosymbiotic organelles, mitochondria and chloroplasts, evolutionarily regulate the number of replication origins, thereby influencing genome length in eukaryotes. We show that the above two selection pressures also lead to strict equalization of the number of purines and their corresponding base-pairing pyrimidines within a single DNA strand, known as Chagraff's second parity rule, a hitherto unexplained observation in genomes of nearly all known species. This arises from the symmetrization of replichore length, another observation that has been shown to hold across species, which our model reproduces. The model also reproduces other experimentally observed phenomena, such as a general preference for deletions over insertions, and elongation and high variance of genome lengths under reduced selection pressure for replication rate, termed the C-value paradox. We highlight the possibility of regulation of the firing of latent replication origins in response to cues from the extracellular environment leading to the regulation of cell cycle rates in multicellular eukaryotes.
ALVI Interface: Towards Full Hand Motion Decoding for Amputees Using sEMG
Authors: Aleksandr Kovalev, Anna Makarova, Petr Chizhov, Matvey Antonov, Gleb Duplin, Vladislav Lomtev, Viacheslav Gostevskii, Vladimir Bessonov, Andrey Tsurkan, Mikhail Korobok, Aleksejs Timčenko
Date: 2025-02-28 00:00:00

We present a system for decoding hand movements using surface EMG signals. The interface provides real-time (25 Hz) reconstruction of finger joint angles across 20 degrees of freedom, designed for upper limb amputees. Our offline analysis shows 0.8 correlation between predicted and actual hand movements. The system functions as an integrated pipeline with three key components: (1) a VR-based data collection platform, (2) a transformer-based model for EMG-to-motion transformation, and (3) a real-time calibration and feedback module called ALVI Interface. Using eight sEMG sensors and a VR training environment, users can control their virtual hand down to finger joint movement precision, as demonstrated in our video: youtube link.
Dynamic Markov Blanket Detection for Macroscopic Physics Discovery
Authors: Jeff Beck, Maxwell J. D. Ramstead
Date: 2025-02-28 00:00:00
Tasks: Object

The free energy principle (FEP), along with the associated constructs of Markov blankets and ontological potentials, have recently been presented as the core components of a generalized modeling method capable of mathematically describing arbitrary objects that persist in random dynamical systems; that is, a mathematical theory of ``every'' ``thing''. Here, we leverage the FEP to develop a mathematical physics approach to the identification of objects, object types, and the macroscopic, object-type-specific rules that govern their behavior. We take a generative modeling approach and use variational Bayesian expectation maximization to develop a dynamic Markov blanket detection algorithm that is capable of identifying and classifying macroscopic objects, given partial observation of microscopic dynamics. This unsupervised algorithm uses Bayesian attention to explicitly label observable microscopic elements according to their current role in a given system, as either the internal or boundary elements of a given macroscopic object; and it identifies macroscopic physical laws that govern how the object interacts with its environment. Because these labels are dynamic or evolve over time, the algorithm is capable of identifying complex objects that travel through fixed media or exchange matter with their environment. This approach leads directly to a flexible class of structured, unsupervised algorithms that sensibly partition complex many-particle or many-component systems into collections of interacting macroscopic subsystems, namely, ``objects'' or ``things''. We derive a few examples of this kind of macroscopic physics discovery algorithm and demonstrate its utility with simple numerical experiments, in which the algorithm correctly labels the components of Newton's cradle, a burning fuse, the Lorenz attractor, and a simulated cell.
Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning
Authors: Léopold Maytié, Roland Bertin Johannet, Rufin VanRullen
Date: 2025-02-28 00:00:00
Tasks: Model-based Reinforcement Learning, Reinforcement Learning (RL)

Humans leverage rich internal models of the world to reason about the future, imagine counterfactuals, and adapt flexibly to new situations. In Reinforcement Learning (RL), world models aim to capture how the environment evolves in response to the agent's actions, facilitating planning and generalization. However, typical world models directly operate on the environment variables (e.g. pixels, physical attributes), which can make their training slow and cumbersome; instead, it may be advantageous to rely on high-level latent dimensions that capture relevant multimodal variables. Global Workspace (GW) Theory offers a cognitive framework for multimodal integration and information broadcasting in the brain, and recent studies have begun to introduce efficient deep learning implementations of GW. Here, we evaluate the capabilities of an RL system combining GW with a world model. We compare our GW-Dreamer with various versions of the standard PPO and the original Dreamer algorithms. We show that performing the dreaming process (i.e., mental simulation) inside the GW latent space allows for training with fewer environment steps. As an additional emergent property, the resulting model (but not its comparison baselines) displays strong robustness to the absence of one of its observation modalities (images or simulation attributes). We conclude that the combination of GW with World Models holds great potential for improving decision-making in RL agents.
Triple Phase Transitions: Understanding the Learning Dynamics of Large Language Models from a Neuroscience Perspective
Authors: Yuko Nakagi, Keigo Tada, Sota Yoshino, Shinji Nishimoto, Yu Takagi
Date: 2025-02-28 00:00:00
Tasks: Instruction Following

Large language models (LLMs) often exhibit abrupt emergent behavior, whereby new abilities arise at certain points during their training. This phenomenon, commonly referred to as a ''phase transition'', remains poorly understood. In this study, we conduct an integrative analysis of such phase transitions by examining three interconnected perspectives: the similarity between LLMs and the human brain, the internal states of LLMs, and downstream task performance. We propose a novel interpretation for the learning dynamics of LLMs that vary in both training data and architecture, revealing that three phase transitions commonly emerge across these models during training: (1) alignment with the entire brain surges as LLMs begin adhering to task instructions Brain Alignment and Instruction Following, (2) unexpectedly, LLMs diverge from the brain during a period in which downstream task accuracy temporarily stagnates Brain Detachment and Stagnation, and (3) alignment with the brain reoccurs as LLMs become capable of solving the downstream tasks Brain Realignment and Consolidation. These findings illuminate the underlying mechanisms of phase transitions in LLMs, while opening new avenues for interdisciplinary research bridging AI and neuroscience.
Deviance Detection and Regularity Sensitivity in Dissociated Neuronal Cultures
Authors: Zhuo Zhang, Amit Yaron, Dai Akita, Tomoyo Isoguchi Shiramatsu, Zenas C. Chao, Hirokazu Takahashi
Date: 2025-02-28 00:00:00
Tasks: Sensitivity

Understanding how neural networks process complex patterns of information is crucial for advancing both neuroscience and artificial intelligence. To investigate fundamental principles of neural computation, we studied dissociated neuronal cultures, one of the most primitive living neural networks, on high-resolution CMOS microelectrode arrays and tested whether the dissociated culture exhibits regularity sensitivity beyond mere stimulus-specific adaptation and deviance detection. In oddball electrical stimulation paradigms, we confirmed that the neuronal culture produced mismatch responses (MMRs) with true deviance detection beyond mere adaptation. These MMRs were dependent on the N-methyl-D-aspartate (NMDA) receptors, similar to mismatch negativity (MMN) in humans, which is known to have true deviance detection properties. Crucially, we also showed sensitivity to the statistical regularity of stimuli, a phenomenon previously observed only in intact brains: the MMRs in a predictable, periodic sequence were smaller than those in a commonly used sequence in which the appearance of the deviant stimulus was random and unpredictable. These results challenge the traditional view that a hierarchically structured neural network is required to process complex temporal patterns, suggesting instead that deviant detection and regularity sensitivity are inherent properties arising from the primitive neural network. They also suggest new directions for the development of neuro-inspired artificial intelligence systems, emphasizing the importance of incorporating adaptive mechanisms and temporal dynamics in the design of neural networks.
Training Large Neural Networks With Low-Dimensional Error Feedback
Authors: Maher Hanut, Jonathan Kadmon
Date: 2025-02-27 00:00:00

Training deep neural networks typically relies on backpropagating high dimensional error signals a computationally intensive process with little evidence supporting its implementation in the brain. However, since most tasks involve low-dimensional outputs, we propose that low-dimensional error signals may suffice for effective learning. To test this hypothesis, we introduce a novel local learning rule based on Feedback Alignment that leverages indirect, low-dimensional error feedback to train large networks. Our method decouples the backward pass from the forward pass, enabling precise control over error signal dimensionality while maintaining high-dimensional representations. We begin with a detailed theoretical derivation for linear networks, which forms the foundation of our learning framework, and extend our approach to nonlinear, convolutional, and transformer architectures. Remarkably, we demonstrate that even minimal error dimensionality on the order of the task dimensionality can achieve performance matching that of traditional backpropagation. Furthermore, our rule enables efficient training of convolutional networks, which have previously been resistant to Feedback Alignment methods, with minimal error. This breakthrough not only paves the way toward more biologically accurate models of learning but also challenges the conventional reliance on high-dimensional gradient signals in neural network training. Our findings suggest that low-dimensional error signals can be as effective as high-dimensional ones, prompting a reevaluation of gradient-based learning in high-dimensional systems. Ultimately, our work offers a fresh perspective on neural network optimization and contributes to understanding learning mechanisms in both artificial and biological systems.
Brain-Inspired Exploration of Functional Networks and Key Neurons in Large Language Models
Authors: Yiheng Liu, Xiaohui Gao, Haiyang Sun, Bao Ge, Tianming Liu, Junwei Han, Xintao Hu
Date: 2025-02-13 00:00:00

In recent years, the rapid advancement of large language models (LLMs) in natural language processing has sparked significant interest among researchers to understand their mechanisms and functional characteristics. Although existing studies have attempted to explain LLM functionalities by identifying and interpreting specific neurons, these efforts mostly focus on individual neuron contributions, neglecting the fact that human brain functions are realized through intricate interaction networks. Inspired by cognitive neuroscience research on functional brain networks (FBNs), this study introduces a novel approach to investigate whether similar functional networks exist within LLMs. We use methods similar to those in the field of functional neuroimaging analysis to locate and identify functional networks in LLM. Experimental results show that, similar to the human brain, LLMs contain functional networks that frequently recur during operation. Further analysis shows that these functional networks are crucial for LLM performance. Masking key functional networks significantly impairs the model's performance, while retaining just a subset of these networks is adequate to maintain effective operation. This research provides novel insights into the interpretation of LLMs and the lightweighting of LLMs for certain downstream tasks. Code is available at https://github.com/WhatAboutMyStar/LLM_ACTIVATION.
Evidence for strong modality-dependence of chronotype assessment from real world calendar app data
Authors: Sourabh Gapate, Royan Kamyar, Benjamin Smarr
Date: 2025-02-28 00:00:00
Tasks: Rhythm

Chronotypes allow for comparisons of one individual's daily rhythms to that of others and the environment. Mismatch between an individual's chronotype and the timing constraints of their social environment create social jet lag, which is correlated with mental and physical health risks. The concept of chronotype implicitly supposes that a single phase applies to an individual, whereas the circadian rhythms of different internal systems entrain to or have their outputs masked by different environmental inputs. If the modern environment interferes with internal synchrony or generates different masking for different internal system's outputs, then real world data reflecting these outputs ought to reveal relatively low correlations, reflecting environmental interference. At the other extreme, if there is no behavior or tissue specific interference, then different internal system outputs should all be equally predicted from data modalities capturing their different outputs. Here we explore multimodal behavioral rhythm data from the Owaves calendaring app, focusing on behavioral outputs logged as: Sleep, Exercise, Eat, Work, Love, Play, Relax, Misc. We find that individuals show daily rhythms within each behavior type from which chronotypes can be assigned, but that chronotypes derived from different behaviors (or combinations of behaviors) lead to nearly independent sorting of individual's by phase. This suggests that if real world data are used to assign an individuals chronotype, then that chronotype may be specific to the internal system the output of which is related to each specific data modality assessed. Our findings suggest that researchers cannot confidently assume to account for outputs from other internal systems in real world settings without additional observations or controls.
Stochastic dynamics at the back of a gene drive propagation wave
Authors: Léna Kläy, Léo Girardin, Florence Débarre, Vincent Calvez
Date: 2025-02-28 00:00:00

Gene drive alleles bias their own inheritance to offspring. They can fix in a wild-type population in spite of a fitness cost, and even lead to the eradication of the target population if the fitness cost is high. However, this outcome may be prevented or delayed if areas previously cleared by the drive are recolonised by wild-type individuals. Here, we investigate the conditions under which these stochastic wild-type recolonisation events are likely and when they are unlikely to occur in one spatial dimension. More precisely, we examine the conditions ensuring that the last individual carrying a wild-type allele is surrounded by a large enough number of drive homozygous individuals, resulting in a very low chance of wild-type recolonisation. To do so, we make a deterministic approximation of the distribution of drive alleles within the wave, and we split the distribution of wild-type alleles into a deterministic part and a stochastic part. Our analytical and numerical results suggest that the probability of wild-type recolonisation events increases with lower fitness of drive individuals, with smaller migration rate, and also with smaller local carrying capacity. Numerical simulations show that these results extend to two spatial dimensions. We also demonstrate that, if a wild-type recolonisation event were to occur, the probability of a following drive reinvasion event decreases with smaller values of the intrinsic growth rate of the population. Overall, our study paves the way for further analysis of wild-type recolonisation at the back of eradication traveling waves.