pwc-archive/papers-with-abstracts huggingface dataset:
Characterizing Neural Manifolds' Properties and Curvatures using Normalizing Flows
Authors: Peter Bouss, Sandra Nestler, Kirsten Fischer, Claudia Merger, Alexandre René, Moritz Helias
Date: 2025-06-13 00:00:00

Neuronal activity is found to lie on low-dimensional manifolds embedded within the high-dimensional neuron space. Variants of principal component analysis are frequently employed to assess these manifolds. These methods are, however, limited by assuming a Gaussian data distribution and a flat manifold. In this study, we introduce a method designed to satisfy three core objectives: (1) extract coordinated activity across neurons, described either statistically as correlations or geometrically as manifolds; (2) identify a small number of latent variables capturing these structures; and (3) offer an analytical and interpretable framework characterizing statistical properties by a characteristic function and describing manifold geometry through a collection of charts. To this end, we employ Normalizing Flows (NFs), which learn an underlying probability distribution of data by an invertible mapping between data and latent space. Their simplicity and ability to compute exact likelihoods distinguish them from other generative networks. We adjust the NF's training objective to distinguish between relevant (in manifold) and noise dimensions (out of manifold). Additionally, we find that different behavioral states align with the components of the latent Gaussian mixture model, enabling their treatment as distinct curved manifolds. Subsequently, we approximate the network for each mixture component with a quadratic mapping, allowing us to characterize both neural manifold curvature and non-Gaussian correlations among recording channels. Applying the method to recordings in macaque visual cortex, we demonstrate that state-dependent manifolds are curved and exhibit complex statistical dependencies. Our approach thus enables an expressive description of neural population activity, uncovering non-linear interactions among groups of neurons.
PaceLLM: Brain-Inspired Large Language Models for Long-Context Understanding
Authors: Kangcong Li, Peng Ye, Chongjun Tu, Lin Zhang, Chunfeng Song, Jiamin Wu, Tao Yang, Qihao Zheng, Tao Chen
Date: 2025-06-18 00:00:00
Tasks: Long-Context Understanding

While Large Language Models (LLMs) demonstrate strong performance across domains, their long-context capabilities are limited by transient neural activations causing information decay and unstructured feed-forward network (FFN) weights leading to semantic fragmentation. Inspired by the brain's working memory and cortical modularity, we propose PaceLLM, featuring two innovations: (1) a Persistent Activity (PA) Mechanism that mimics prefrontal cortex (PFC) neurons' persistent firing by introducing an activation-level memory bank to dynamically retrieve, reuse, and update critical FFN states, addressing contextual decay; and (2) Cortical Expert (CE) Clustering that emulates task-adaptive neural specialization to reorganize FFN weights into semantic modules, establishing cross-token dependencies and mitigating fragmentation. Extensive evaluations show that PaceLLM achieves 6% improvement on LongBench's Multi-document QA and 12.5-17.5% performance gains on Infinite-Bench tasks, while extending measurable context length to 200K tokens in Needle-In-A-Haystack (NIAH) tests. This work pioneers brain-inspired LLM optimization and is complementary to other works. Besides, it can be generalized to any model and enhance their long-context performance and interpretability without structural overhauls.
Learning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors
Authors: Jiyi Wang, Jingyang Ke, Bo Dai, Anqi Wu
Date: 2025-06-18 00:00:00
Tasks: Imitation Learning, Representation Learning

Animals flexibly recombine a finite set of core motor primitives to meet diverse task demands, but existing behavior-segmentation methods oversimplify this process by imposing discrete syllables under restrictive generative assumptions. To reflect the animal behavior generation procedure, we introduce skill-based imitation learning (SKIL) for behavior understanding, a reinforcement learning-based imitation framework that (1) infers interpretable skill sets, i.e., latent basis functions of behavior, by leveraging representation learning on transition probabilities, and (2) parameterizes policies as dynamic mixtures of these skills. We validate our approach on a simple grid world, a discrete labyrinth, and unconstrained videos of freely moving animals. Across tasks, it identifies reusable skill components, learns continuously evolving compositional policies, and generates realistic trajectories beyond the capabilities of traditional discrete models. By exploiting generative behavior modeling with compositional representations, our method offers a concise, principled account of how complex animal behaviors emerge from dynamic combinations of fundamental motor primitives.
POCO: Scalable Neural Forecasting through Population Conditioning
Authors: Yu Duan, Hamza Tahir Chaudhry, Misha B. Ahrens, Christopher D Harvey, Matthew G Perich, Karl Deisseroth, Kanaka Rajan
Date: 2025-06-17 00:00:00

Predicting future neural activity is a core challenge in modeling brain dynamics, with applications ranging from scientific investigation to closed-loop neurotechnology. While recent models of population activity emphasize interpretability and behavioral decoding, neural forecasting-particularly across multi-session, spontaneous recordings-remains underexplored. We introduce POCO, a unified forecasting model that combines a lightweight univariate forecaster with a population-level encoder to capture both neuron-specific and brain-wide dynamics. Trained across five calcium imaging datasets spanning zebrafish, mice, and C. elegans, POCO achieves state-of-the-art accuracy at cellular resolution in spontaneous behaviors. After pre-training, POCO rapidly adapts to new recordings with minimal fine-tuning. Notably, POCO's learned unit embeddings recover biologically meaningful structure-such as brain region clustering-without any anatomical labels. Our comprehensive analysis reveals several key factors influencing performance, including context length, session diversity, and preprocessing. Together, these results position POCO as a scalable and adaptable approach for cross-session neural forecasting and offer actionable insights for future model design. By enabling accurate, generalizable forecasting models of neural dynamics across individuals and species, POCO lays the groundwork for adaptive neurotechnologies and large-scale efforts for neural foundation models.
Learning From the Past with Cascading Eligibility Traces
Authors: Tokiniaina Raharison Ralambomihanta, Ivan Anokhin, Roman Pogodin, Samira Ebrahimi Kahou, Jonathan Cornford, Blake Aaron Richards
Date: 2025-06-17 00:00:00

Animals often receive information about errors and rewards after a significant delay. For example, there is typically a delay of tens to hundreds of milliseconds between motor actions and visual feedback. The standard approach to handling delays in models of synaptic plasticity is to use eligibility traces. However, standard eligibility traces that decay exponentially mix together any events that happen during the delay, presenting a problem for any credit assignment signal that occurs with a significant delay. Here, we show that eligibility traces formed by a state-space model, inspired by a cascade of biochemical reactions, can provide a temporally precise memory for handling credit assignment at arbitrary delays. We demonstrate that these cascading eligibility traces (CETs) work for credit assignment at behavioral time-scales, ranging from seconds to minutes. As well, we can use CETs to handle extremely slow retrograde signals, as have been found in retrograde axonal signaling. These results demonstrate that CETs can provide an excellent basis for modeling synaptic plasticity.
Exploring EEG Indicators to Evaluate Listening Difficulties in Noisy Environments
Authors: Azuki Onaya, Hiroki Tanaka
Date: 2025-06-17 00:00:00
Tasks: Diagnostic, EEG

Auditory processing difficulties involve challenges in understanding speech in noisy environments despite normal hearing. However, the neural mechanisms remain unclear, and standardized diagnostic criteria are lacking. This study examined neural indicators using EEG under realistic noisy conditions. Ten Japanese-speaking university students participated in auditory tasks, including a resting state, a lecture attention task with background noise, and a task requiring attention to background noise. The study analyzed the peak frequency and power of alpha waves, the long-range temporal correlation of alpha oscillations, and the absolute power of delta waves. Results showed a significant reduction in the power of alpha waves during the background noise attention task, suggesting increased cognitive load. In contrast, the peak frequency of alpha waves remained stable, indicating limited sensitivity to cognitive demand changes. Long-range temporal correlation increased under tasks requiring auditory attention, reflecting sustained attention-related neural dynamics, while the absolute power of delta waves showed no significant variation across tasks. Regression analysis revealed a significant negative correlation between the power of alpha waves in noisy conditions and screening scores for auditory processing difficulties, suggesting its potential as a neural indicator.
The Sex-Dependent Effects of Psychedelics on Myelination in APOE4 Mice
Authors: Sanjana Shankar
Date: 2025-06-16 00:00:00

Several studies have linked myelin abnormalities with neuropsychiatric disorders; others have implicated psychedelics as a potential therapeutic for such conditions. One risk factor for these demyelinating disorders is a mutation in the Apolipoprotein E gene known as APOE4. This variant impedes the cholesterol regulation of oligodendrocytes responsible for the myelination, or insulation, of neurons when compared to the wild-type phenotype. In this work, I advance knowledge of cellular pathways involved in the progression of APOE4-related diseases and elucidate the effects of psychedelics on the brain. Myelin sheaths are vital for maintaining neural pathways, and healthy oligodendrocytes serve as a prerequisite for axonal integrity. Further, the Kaufer Lab has observed significant behavioral differences between male and female APOE4 mice following psychedelic treatment with 2,5-Dimethoxy-4-iodoamphetamine, or DOI, a serotonin receptor ligand. The sex-dependent mechanisms influencing symptom differences and treatment outcomes in AD are unclear, and could be key to developing successful therapeutics for myelin-related issues. I hypothesize that administration of DOI will increase the myelination activity of oligodendrocytes in female APOE4 mice compared with their male counterparts or controls. Preliminary results show a significant increase in MBP in the CA1, or short-term, and CA2, or long term, areas in only female APOE4 mice post-introduction of DOI to the system. This aligns with behavioral data indicating fewer anxiety-related behaviors in female APOE4 mice after DOI administration. These findings reveal distinct biological mechanisms in male and female brain degeneration and suggest potential for sex-specific therapeutics.
Metric Framework of Coherent Activity Patterns Identification in Spiking Neuronal Networks
Authors: Daniil Radushev, Olesia Dogonasheva, Boris Gutkin, Denis Zakharov
Date: 2025-06-14 00:00:00

Partial synchronization plays a crucial role in the functioning of neuronal networks: selective, coordinated activation of neurons enables information processing that flexibly adapts to a changing computational context. Since the structure of coherent activity patterns reflects the network's current state, developing automated tools to identify them is a key challenge in neurodynamics. Existing methods for analyzing neuronal dynamics tend to focus on global characteristics of the network, such as its aggregated synchrony level. While this approach can distinguish between the network's main dynamical states, it cannot reveal the localization or properties of distinct coherent patterns. In this work, we propose a new perspective on neural dynamics analysis that enables the study of network coherence at the single-neuron scale. We interpret the network as a metric space of neurons and represent its instantaneous state as an activity function on that space. We identify specific coherent activity clusters as regions where the activity function exhibits spatial continuity. Each cluster's activity is further characterized using the analytical properties of the activity function within that region. This approach yields a concise yet detailed algorithmic profile of the network's activity patterns.
Mapping Neural Theories of Consciousness onto the Common Model of Cognition
Authors: Paul S. Rosenbloom, John E. Laird, Christian Lebiere, Andrea Stocco
Date: 2025-06-13 00:00:00

A beginning is made at mapping four neural theories of consciousness onto the Common Model of Cognition. This highlights how the four jointly depend on recurrent local modules plus a cognitive cycle operating on a global working memory with complex states, and reveals how an existing integrative view of consciousness from a neural perspective aligns with the Com-mon Model.
Scale-Invariance Drives Convergence in AI and Brain Representations
Authors: Junjie Yu, Wenxiao Ma, Jianyu Zhang, Haotian Deng, Zihan Deng, Yi Guo, Quanying Liu
Date: 2025-06-13 00:00:00

Despite variations in architecture and pretraining strategies, recent studies indicate that large-scale AI models often converge toward similar internal representations that also align with neural activity. We propose that scale-invariance, a fundamental structural principle in natural systems, is a key driver of this convergence. In this work, we propose a multi-scale analytical framework to quantify two core aspects of scale-invariance in AI representations: dimensional stability and structural similarity across scales. We further investigate whether these properties can predict alignment performance with functional Magnetic Resonance Imaging (fMRI) responses in the visual cortex. Our analysis reveals that embeddings with more consistent dimension and higher structural similarity across scales align better with fMRI data. Furthermore, we find that the manifold structure of fMRI data is more concentrated, with most features dissipating at smaller scales. Embeddings with similar scale patterns align more closely with fMRI data. We also show that larger pretraining datasets and the inclusion of language modalities enhance the scale-invariance properties of embeddings, further improving neural alignment. Our findings indicate that scale-invariance is a fundamental structural principle that bridges artificial and biological representations, providing a new framework for evaluating the structural quality of human-like AI systems.
Differences in Neurovascular Coupling in Patients with Major Depressive Disorder: Evidence from Simultaneous Resting-State EEG-fNIRS
Authors: Feng Yan, Xiaobin Wang, Yao Zhao, Shuyi Yang, Zhiren Wang
Date: 2025-06-13 00:00:00
Tasks: EEG

Neurovascular coupling (NVC) refers to the process by which local neural activity, through energy consumption, induces changes in regional cerebral blood flow to meet the metabolic demands of neurons. Event-related studies have shown that the hemodynamic response typically lags behind neural activation by 4-6 seconds. However, little is known about how NVC is altered in patients with major depressive disorder (MDD) and throughout the recovery process. In this study, we employed simultaneous resting-state electroencephalography (rsEEG) and functional near-infrared spectroscopy (fNIRS) to monitor neural and hemodynamic signals. Twelve patients with MDD during the acute phase, ten patients in the maintenance or consolidation phase, and six healthy controls were involved. We calculated the differences in coherence and temporal delay between spontaneous peak electrophysiological activity and hemodynamic responses across groups during the resting state in the prefrontal cortex (PFC). We found that the neural activity and its subsequent correlation with hemodynamic responses were significantly higher in patients during the maintenance phase. The rise time from the lowest to the highest point of correlation was shorter in healthy individuals than in patients in the acute phase, and gradually recovered during remission. By leveraging wearable neuroimaging techniques, this study reveals alterations in neurovascular coupling in depression and offers novel multimodal insights into potential biomarkers for MDD and its recovery process.
Voxel-Level Brain States Prediction Using Swin Transformer
Authors: Yifei Sun, Daniel Chahine, Qinghao Wen, Tianming Liu, Xiang Li, Yixuan Yuan, Fernando Calamante, Jinglei Lv
Date: 2025-06-13 00:00:00
Tasks: Prediction

Understanding brain dynamics is important for neuroscience and mental health. Functional magnetic resonance imaging (fMRI) enables the measurement of neural activities through blood-oxygen-level-dependent (BOLD) signals, which represent brain states. In this study, we aim to predict future human resting brain states with fMRI. Due to the 3D voxel-wise spatial organization and temporal dependencies of the fMRI data, we propose a novel architecture which employs a 4D Shifted Window (Swin) Transformer as encoder to efficiently learn spatio-temporal information and a convolutional decoder to enable brain state prediction at the same spatial and temporal resolution as the input fMRI data. We used 100 unrelated subjects from the Human Connectome Project (HCP) for model training and testing. Our novel model has shown high accuracy when predicting 7.2s resting-state brain activities based on the prior 23.04s fMRI time series. The predicted brain states highly resemble BOLD contrast and dynamics. This work shows promising evidence that the spatiotemporal organization of the human brain can be learned by a Swin Transformer model, at high resolution, which provides a potential for reducing the fMRI scan time and the development of brain-computer interfaces in the future.
Convolutional method for data assimilation An improved method on neuronal electrophysiological data
Authors: Dawei Li, Henry D. I. Abarbanel
Date: 2025-06-12 00:00:00
Tasks: parameter estimation

We present a convolution-based data assimilation method tailored to neuronal electrophysiology, addressing the limitations of traditional value-based synchronization approaches. While conventional methods rely on nudging terms and pointwise deviation metrics, they often fail to account for spike timing precision, a key feature in neural signals. Our approach applies a Gaussian convolution to both measured data and model estimates, enabling a cost function that evaluates both amplitude and timing alignment via spike overlap. This formulation remains compatible with gradient-based optimization. Through twin experiments and real hippocampal neuron recordings, we demonstrate improved parameter estimation and prediction quality, particularly in capturing sharp, time-sensitive dynamics.
Wanting to Be Understood Explains the Meta-Problem of Consciousness
Authors: Chrisantha Fernando, Dylan Banarse, Simon Osindero
Date: 2025-06-10 00:00:00

Because we are highly motivated to be understood, we created public external representations -- mime, language, art -- to externalise our inner states. We argue that such external representations are a pre-condition for access consciousness, the global availability of information for reasoning. Yet the bandwidth of access consciousness is tiny compared with the richness of `raw experience', so no external representation can reproduce that richness in full. Ordinarily an explanation of experience need only let an audience `grasp' the relevant pattern, not relive the phenomenon. But our drive to be understood, and our low level sensorimotor capacities for `grasping' so rich, that the demand for an explanation of the feel of experience cannot be ``satisfactory''. That inflated epistemic demand (the preeminence of our expectation that we could be perfectly understood by another or ourselves) rather than an irreducible metaphysical gulf -- keeps the hard problem of consciousness alive. But on the plus side, it seems we will simply never give up creating new ways to communicate and think about our experiences. In this view, to be consciously aware is to strive to have one's agency understood by oneself and others.
Sparse Autoencoders Bridge The Deep Learning Model and The Brain
Authors: Ziming Mao, Jia Xu, Zeqi Zheng, Haofang Zheng, Dabing Sheng, Yaochu Jin, Guoyuan Yang
Date: 2025-06-10 00:00:00
Tasks: Deep Learning

We present SAE-BrainMap, a novel framework that directly aligns deep learning visual model representations with voxel-level fMRI responses using sparse autoencoders (SAEs). First, we train layer-wise SAEs on model activations and compute the correlations between SAE unit activations and cortical fMRI signals elicited by the same natural image stimuli with cosine similarity, revealing strong activation correspondence (maximum similarity up to 0.76). Depending on this alignment, we construct a voxel dictionary by optimally assigning the most similar SAE feature to each voxel, demonstrating that SAE units preserve the functional structure of predefined regions of interest (ROIs) and exhibit ROI-consistent selectivity. Finally, we establish fine-grained hierarchical mapping between model layers and the human ventral visual pathway, also by projecting voxel dictionary activations onto individual cortical surfaces, we visualize the dynamic transformation of the visual information in deep learning models. It is found that ViT-B/16$_{CLIP}$ tends to utilize low-level information to generate high-level semantic information in the early layers and reconstructs the low-dimension information later. Our results establish a direct, downstream-task-free bridge between deep neural networks and human visual cortex, offering new insights into model interpretability.
Automatic Depression Assessment using Machine Learning: A Comprehensive Survey
Authors: Siyang Song, Yupeng Huo, Shiqing Tang, Jiaee Cheong, Rui Gao, Michel Valstar, Hatice Gunes
Date: 2025-06-09 00:00:00
Tasks: Survey

Depression is a common mental illness across current human society. Traditional depression assessment relying on inventories and interviews with psychologists frequently suffer from subjective diagnosis results, slow and expensive diagnosis process as well as lack of human resources. Since there is a solid evidence that depression is reflected by various human internal brain activities and external expressive behaviours, early traditional machine learning (ML) and advanced deep learning (DL) models have been widely explored for human behaviour-based automatic depression assessment (ADA) since 2012. However, recent ADA surveys typically only focus on a limited number of human behaviour modalities. Despite being used as a theoretical basis for developing ADA approaches, existing ADA surveys lack a comprehensive review and summary of multi-modal depression-related human behaviours. To bridge this gap, this paper specifically summarises depression-related human behaviours across a range of modalities (e.g. the human brain, verbal language and non-verbal audio/facial/body behaviours). We focus on conducting an up-to-date and comprehensive survey of ML-based ADA approaches for learning depression cues from these behaviours as well as discussing and comparing their distinctive features and limitations. In addition, we also review existing ADA competitions and datasets, identify and discuss the main challenges and opportunities to provide further research directions for future ADA researchers.