pwc-archive/papers-with-abstracts huggingface dataset:
POINT: a web-based platform for pharmacological investigation enhanced by multi-omics networks and knowledge graphs
Authors: Zihao He, Liu Liu, Dongchen Han, Kai Gao, Lei Dong, Dechao Bu, Peipei Huo, Zhihao Wang, Wenxin Deng, Jingjia Liu, Jin-cheng Guo, Yi Zhao, Yang Wu
Date: 2025-03-10 00:00:00
Tasks: Drug Discovery, Knowledge Graphs

Network pharmacology (NP) explores pharmacological mechanisms through biological networks. Multi-omics data enable multi-layer network construction under diverse conditions, requiring integration into NP analyses. We developed POINT, a novel NP platform enhanced by multi-omics biological networks, advanced algorithms, and knowledge graphs (KGs) featuring network-based and KG-based analytical functions. In the network-based analysis, users can perform NP studies flexibly using 1,158 multi-omics biological networks encompassing proteins, transcription factors, and non-coding RNAs across diverse cell line-, tissue- and disease-specific conditions. Network-based analysis-including random walk with restart (RWR), GSEA, and diffusion profile (DP) similarity algorithms-supports tasks such as target prediction, functional enrichment, and drug screening. We merged networks from experimental sources to generate a pre-integrated multi-layer human network for evaluation. RWR demonstrated superior performance with a 33.1% average ranking improvement over the second-best algorithm, PageRank, in identifying known targets across 2,002 drugs. Additionally, multi-layer networks significantly improve the ability to identify FDA-approved drug-disease pairs compared to the single-layer network. For KG-based analysis, we compiled three high-quality KGs to construct POINT KG, which cross-references over 90% of network-based predictions. We illustrated the platform's capabilities through two case studies. POINT bridges the gap between multi-omics networks and drug discovery; it is freely accessible at http://point.gene.ac/.
Exact first passage time distribution for nonlinear chemical reaction networks II: monomolecular reactions and a A + B - C type of second-order reaction with arbitrary initial conditions
Authors: Changqian Rao, David Waxman, Wei Lin, Zhuoyi Song
Date: 2025-03-06 00:00:00

In biochemical reaction networks, the first passage time (FPT) of a reaction quantifies the time it takes for the reaction to first occur, from the initial state. While the mean FPT historically served as a summary metric, a far more comprehensive characterization of the dynamics of the network is contained within the complete FPT distribution. The relatively uncommon theoretical treatments of the FPT distribution that have been given in the past have been confined to linear systems, with zero and first-order processes. Recently, we presented theoretically exact solutions for the FPT distribution, within nonlinear systems involving two-particle collisions, such as A+B - C. Although this research yielded invaluable results, it was based upon the assumption of initial conditions in the form of a Poisson distribution. This somewhat restricts its relevance to real-world biochemical systems, which frequently display intricate behaviour and initial conditions that are non-Poisson in nature. Our current study extends prior analyses to accommodate arbitrary initial conditions, thereby expanding the applicability of our theoretical framework and providing a more adaptable tool for capturing the dynamics of biochemical reaction networks.
Integrating network pharmacology, metabolomics, and gut microbiota analysis to explore the effects of Jinhong tablets on chronic superficial gastritis
Authors: Lihao Xiao, Tingyu Zhang, Yun Liu, Chayanis Sutcharitchan, Qingyuan Liu, Xiaoxue Fan, Jian Feng, Huifang Gao, Tong Zhang, Shao Li
Date: 2025-03-06 00:00:00

Chronic superficial gastritis (CSG) severely affects quality of life and can progress to worse gastric pathologies. Traditional Chinese Medicine (TCM) effectively treats CSG, as exemplified by Jinhong Tablets (JHT) with known anti-inflammatory properties, though their mechanism remains unclear. This study integrated network pharmacology, untargeted metabolomics, and gut microbiota analyses to investigate how JHT alleviates CSG. A rat CSG model was established and evaluated via H&E staining. We identified JHT's target profiles and constructed a multi-layer biomolecular network. Differential metabolites in plasma were determined by untargeted metabolomics, and gut microbiota diversity/composition in fecal and cecal samples was assessed via 16S rRNA sequencing. JHT markedly reduced gastric inflammation. Network pharmacology highlighted metabolic pathways, particularly lipid and nitric oxide metabolism, as essential to JHT's therapeutic effect. Metabolomics identified key differential metabolites including betaine (enhancing gut microbiota), phospholipids, and citrulline (indicating severity of CSG). Pathway enrichment supported the gut microbiota's involvement. Further microbiota analysis showed that JHT increased betaine abundance, improved short-chain fatty acid production, and elevated Faecalibaculum and Bifidobacterium, thereby alleviating gastric inflammation. In conclusion, JHT alleviates CSG via diverse metabolic processes, especially lipid and energy metabolism, and influences metabolites like betaine alongside gut microbes such as Faecalibaculum and Bifidobacterium. These findings underscore JHT's therapeutic potential and deepen our understanding of TCM's role in CSG management.
The Signed Two-Space Proximity Model for Learning Representations in Protein-Protein Interaction Networks
Authors: Nikolaos Nakis, Chrysoula Kosma, Anastasia Brativnyk, Michail Chatzianastasis, Iakovos Evdaimon, Michalis Vazirgiannis
Date: 2025-03-05 00:00:00
Tasks: Link Prediction

Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates both types of interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes representing extreme protein profiles. S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns.
Fluctuation-response relations and response-response relations for membrane voltage and spike train of stochastic integrate-and-fire neurons
Authors: Kolja Klett, Benjamin Lindner
Date: 2025-03-10 00:00:00
Tasks: Relation

Neurons display spontaneous spiking (in the absence of stimulus signals) as well as a characteristic response to time-dependent external stimuli. In a simple but important class of stochastic neuron models, the integrate-and-fire model with Gaussian current noise, both aspects can mathematically be related via fluctuation-response relations (FRRs) as has been shown recently. Here we extend the class of FRRs to include the susceptibilities of the membrane voltage and subthreshold voltage nonlinearity as well as the power spectrum of the membrane voltage. For a simple but often considered IF model, the leaky IF model with white Gaussian noise, we exploit the FRRs and derive explicit expressions for the power spectrum and susceptibility of the subthreshold membrane voltage. We also put forward a relation between the response functions of the spike train and membrane voltage, a response-response relation (RRR) that holds true for a more general setting than considered in most parts of the paper. For the generalized IF model with an adaptation current and colored Gaussian noise we derive an FRR and an RRR. We briefly discuss useful applications of the derived FRRs and RRRs.
On Questions of Predictability and Control of an Intelligent System Using Probabilistic State-Transitions
Authors: Jayanth R Taranath
Date: 2025-03-09 00:00:00
Tasks: Brain Computer Interface

One of the central aims of neuroscience is to reliably predict the behavioral response of an organism using its neural activity. If possible, this implies we can causally manipulate the neural response and design brain-computer-interface systems to alter behavior, and vice-versa. Hence, predictions play an important role in both fundamental neuroscience and its applications. Can we predict the neural and behavioral states of an organism at any given time? Can we predict behavioral states using neural states, and vice-versa, and is there a memory-component required to reliably predict such states? Are the predictions computable within a given timescale to meaningfully stimulate and make the system reach the desired states? Through a series of mathematical treatments, such conjectures and questions are discussed. Answering them might be key for future developments in understanding intelligence and designing brain-computer-interfaces.
A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision
Authors: Alessandro T. Gifford, Radoslaw M. Cichy, Thomas Naselaris, Kendrick Kay
Date: 2025-03-08 00:00:00

Large-scale visual neural datasets such as the Natural Scenes Dataset (NSD) are boosting NeuroAI research by enabling computational models of the brain with performances beyond what was possible just a decade ago. However, these datasets lack out-of-distribution (OOD) components, which are crucial for the development of more robust models. Here, we address this limitation by releasing NSD-synthetic, a dataset consisting of 7T fMRI responses from the eight NSD subjects for 284 carefully controlled synthetic images. We show that NSD-synthetic's fMRI responses reliably encode stimulus-related information and are OOD with respect to NSD. Furthermore, OOD generalization tests on NSD-synthetic reveal differences between models of the brain that are not detected with NSD - specifically, self-supervised deep neural networks better explain neural responses than their task-supervised counterparts. These results showcase how NSD-synthetic enables OOD generalization tests that facilitate the development of more robust models of visual processing, and the formulation of more accurate theories of human vision.
The Role of Affective States in Computational Psychiatry
Authors: David Benrimoh, Ryan Smith, Andreea O. Diaconescu, Timothy Friesen, Sara Jalali, Nace Mikus, Laura Gschwandtner, Jay Gandhi, Guillermo Horga, Albert Powers
Date: 2025-03-08 00:00:00

Studying psychiatric illness has often been limited by difficulties in connecting symptoms and behavior to neurobiology. Computational psychiatry approaches promise to bridge this gap by providing formal accounts of the latent information processing changes that underlie the development and maintenance of psychiatric phenomena. Models based on these theories generate individual-level parameter estimates which can then be tested for relationships to neurobiology. In this review, we explore computational modelling approaches to one key aspect of health and illness: affect. We discuss strengths and limitations of key approaches to modelling affect, with a focus on reinforcement learning, active inference, the hierarchical gaussian filter, and drift-diffusion models. We find that, in this literature, affect is an important source of modulation in decision making, and has a bidirectional influence on how individuals infer both internal and external states. Highlighting the potential role of affect in information processing changes underlying symptom development, we extend an existing model of psychosis, where affective changes are influenced by increasing cortical noise and consequent increases in either perceived environmental instability or expected noise in sensory input, becoming part of a self-reinforcing process generating negatively valenced, over-weighted priors underlying positive symptom development. We then provide testable predictions from this model at computational, neurobiological, and phenomenological levels of description.
The Amplitude Modulation Structure of Japanese Infant- and Child-Directed Speech: Longitudinal Data Reveal Universal Acoustic Physical Structures Underpinning Moraic Timing
Authors: Tatsuya Daikoku, Usha Goswami
Date: 2025-03-07 00:00:00
Tasks: Rhythm

Infant-directed speech (IDS) is highly rhythmic, and in European languages IDS is dominated by patterns of amplitude modulation (AM) at ~2Hz (reflecting prosody) and ~5Hz (reflecting individual syllables). The rhythm structure of spoken Japanese is thought to differ from European stress-timed and syllable-timed languages, depending on moraic units. Morae comprise any onset phoneme and vowel phonemes within a syllable, PA-N-DA. Arguably, initial speech encoding via cortical tracking by infants is likely to utilize language-universal physical acoustic structures in speech rather than language-specific structures like morae, since the infant brain must be prepared to acquire any human language. Here a language-blind computational model of linguistic rhythm based on features of the amplitude envelope is used to compute these physical acoustic stimulus characteristics for Japanese. Using ~18,000 samples of natural IDS and child-directed speech (CDS) recorded longitudinally from 6 parents while speaking to their children over the ages 0-5 years, we find that the temporal modulation patterns that characterise the amplitude envelope of Japanese are highly similar to those found for stress-timed and syllable-timed European languages. However, the AM band corresponding to the syllabic level in CDS/IDS in European languages (~2-12Hz, theta-rate cortical tracking) was elongated in Japanese (2.5-17Hz). Further, the phase synchronization ratios between the two slowest AM bands were as likely to be 1:3 as 1:2, which differs from European languages where 1:2 ratios are dominant. Accordingly, the language-universal amplitude-driven physical acoustic structures important for cortical speech tracking flexibly accommodate language-specific differences in core rhythmic units.
Phase Alignment Enhances Oscillatory Power in Neural Mass Models Optimized for Class Encoding
Authors: Alexander Pei
Date: 2025-03-07 00:00:00

Neural encoding of objects and cognitive states remains an elusive yet crucial aspect of brain function. While traditional feed-forward machine learning neural networks have enormous potential to encode information, modern architectures provide little insight into the brain's mechanisms. In this work, a Jansen and Rit neural mass model was constructed to encode different sets of inputs, aiming to understand how simple neural circuits can represent information. A genetic algorithm was used to optimize parameters that maximized the differences in responses to particular inputs. These differences in responses manifested as phase-shifted oscillations across the set of inputs. By delivering impulses of excitation synchronized with a particular phase-shifted oscillation, we demonstrated that the encoded phase could be decoded by measuring oscillatory power. These findings demonstrate the capability of neural dynamical circuits to encode and decode information through phase.
Language-specific Tonal Features Drive Speaker-Listener Neural Synchronization
Authors: Chen Hong, Xiangbin Teng, Yu Li, Shen-Mou Hsu, Feng-Ming Tsao, Patrick C. M. Wong, Gangyi Feng
Date: 2025-03-07 00:00:00

Verbal communication transmits information across diverse linguistic levels, with neural synchronization (NS) between speakers and listeners emerging as a putative mechanism underlying successful exchange. However, the specific speech features driving this synchronization and how language-specific versus universal characteristics facilitate information transfer remain poorly understood. We developed a novel content-based interbrain encoding model to disentangle the contributions of acoustic and linguistic features to speaker-listener NS during Mandarin storytelling and listening, as measured via magnetoencephalography (MEG). Results revealed robust NS throughout frontotemporal-parietal networks with systematic time lags between speech production and perception. Crucially, suprasegmental lexical tone features (tone categories, pitch height, and pitch contour), essential for lexical meaning in Mandarin, contributed more significantly to NS than either acoustic elements or universal segmental units (consonants and vowels). These tonal features generated distinctive spatiotemporal NS patterns, creating language-specific neural "communication channels" that facilitated efficient representation sharing between interlocutors. Furthermore, the strength and patterns of NS driven by these language-specific features predicted communication success. These findings demonstrate the neural mechanisms underlying shared representations during verbal exchange and highlight how language-specific features can shape neural coupling to optimize information transfer during human communication.
Three tiers of computation in transformers and in brain architectures
Authors: E Graham, R Granger
Date: 2025-03-05 00:00:00
Tasks: Logical Reasoning

Human language and logic abilities are computationally quantified within the well-studied grammar-automata hierarchy. We identify three hierarchical tiers and two corresponding transitions and show their correspondence to specific abilities in transformer-based language models (LMs). These emergent abilities have often been described in terms of scaling; we show that it is the transition between tiers, rather than scaled size itself, that determines a system's capabilities. Specifically, humans effortlessly process language yet require critical training to perform arithmetic or logical reasoning tasks; and LMs possess language abilities absent from predecessor systems, yet still struggle with logical processing. We submit a novel benchmark of computational power, provide empirical evaluations of humans and fifteen LMs, and, most significantly, provide a theoretically grounded framework to promote careful thinking about these crucial topics. The resulting principled analyses provide explanatory accounts of the abilities and shortfalls of LMs, and suggest actionable insights into the expansion of their logic abilities.
Neural Models of Task Adaptation: A Tutorial on Spiking Networks for Executive Control
Authors: Ashwin Viswanathan Kannan, Madhumitha Ganesan
Date: 2025-03-05 00:00:00
Tasks: Experimental Design

Understanding cognitive flexibility and task-switching mechanisms in neural systems requires biologically plausible computational models. This tutorial presents a step-by-step approach to constructing a spiking neural network (SNN) that simulates task-switching dynamics within the cognitive control network. The model incorporates biologically realistic features, including lateral inhibition, adaptive synaptic weights through unsupervised Spike Timing-Dependent Plasticity (STDP), and precise neuronal parameterization within physiologically relevant ranges. The SNN is implemented using Leaky Integrate-and-Fire (LIF) neurons, which represent excitatory (glutamatergic) and inhibitory (GABAergic) populations. We utilize two real-world datasets as tasks, demonstrating how the network learns and dynamically switches between them. Experimental design follows cognitive psychology paradigms to analyze neural adaptation, synaptic weight modifications, and emergent behaviors such as Long-Term Potentiation (LTP), Long-Term Depression (LTD), and Task-Set Reconfiguration (TSR). Through a series of structured experiments, this tutorial illustrates how variations in task-switching intervals affect performance and multitasking efficiency. The results align with empirically observed neuronal responses, offering insights into the computational underpinnings of executive function. By following this tutorial, researchers can develop and extend biologically inspired SNN models for studying cognitive processes and neural adaptation.
I Think, Therefore I Hallucinate: Minds, Machines, and the Art of Being Wrong
Authors: Sebastian Barros
Date: 2025-03-04 00:00:00

This theoretical work examines 'hallucinations' in both human cognition and large language models, comparing how each system can produce perceptions or outputs that deviate from reality. Drawing on neuroscience and machine learning research, we highlight the predictive processes that underlie human and artificial thought. In humans, complex neural mechanisms interpret sensory information under uncertainty, sometimes filling in gaps and creating false perceptions. This inference occurs hierarchically: higher cortical levels send top-down predictions to lower-level regions, while mismatches (prediction errors) propagate upward to refine the model. LLMs, in contrast, rely on auto-regressive modeling of text and can generate erroneous statements in the absence of robust grounding. Despite these different foundations - biological versus computational - the similarities in their predictive architectures help explain why hallucinations occur. We propose that the propensity to generate incorrect or confabulated responses may be an inherent feature of advanced intelligence. In both humans and AI, adaptive predictive processes aim to make sense of incomplete information and anticipate future states, fostering creativity and flexibility, but also introducing the risk of errors. Our analysis illuminates how factors such as feedback, grounding, and error correction affect the likelihood of 'being wrong' in each system. We suggest that mitigating AI hallucinations (e.g., through improved training, post-processing, or knowledge-grounding methods) may also shed light on human cognitive processes, revealing how error-prone predictions can be harnessed for innovation without compromising reliability. By exploring these converging and divergent mechanisms, the paper underscores the broader implications for advancing both AI reliability and scientific understanding of human thought.
Toward a General Theory for the Scaling and Universality of Thermal Responses in Biology
Authors: Jose Ignacio Arroyo, Pablo A. Marquet, Christopher P. Kempes, Geoffrey West
Date: 2025-03-07 00:00:00

We developed a theory showing that under appropriate normalizations and rescalings, temperature response curves show a remarkably regular behavior and follow a general, universal law. The impressive universality of temperature response curves remained hidden due to various curve-fitting models not well-grounded in first principles. In addition, this framework has the potential to explain the origin of different scaling relationships in thermal performance in biology, from molecules to ecosystems. Here, we summarize the background, principles and assumptions, predictions, implications, and possible extensions of this theory.