Tootfinder

⏱️ Researchers discover how the human brain organizes its visual memories through precise neural timing
https://medicalxpress.com/news/2025-07-human-brain-visual-memories-precise.html

Researchers discover how the human brain organizes its visual memories through precise neural timing
Researchers at the University of Southern California have made a significant breakthrough in understanding how the human brain forms, stores and recalls visual memories. A new study, published in Advanced Science, harnesses human patient brain recordings and a powerful machine learning model to shed new light on the brain's internal code that sorts memories of objects into categories—think of it as the brain's filing cabinet of imagery.

This fascinates me. Probably part of the same brain structures that inform my love of history, and my love of figuring out how to get certain old guitar tones
How to Emulate Film Grain in Your Digital Photos | PetaPixel https://petapixel.com/2025/09/19/how-t

How to Emulate Film Grain in Your Digital Photos
It's time to make this complicated topic easy to understand for all photographers chasing a nostalgic feel in their edits.

Language models align with brain regions that represent concepts across modalities
Maria Ryskina, Greta Tuckute, Alexander Fung, Ashley Malkin, Evelina Fedorenko
https://arxiv.org/abs/2508.11536

Language models align with brain regions that represent concepts across modalities
Cognitive science and neuroscience have long faced the challenge of disentangling representations of language from representations of conceptual meaning. As the same problem arises in today's language models (LMs), we investigate the relationship between LM--brain alignment and two neural metrics: (1) the level of brain activation during processing of sentences, targeting linguistic processing, and (2) a novel measure of meaning consistency across input modalities, which quantifies how consiste…

Multi-Sensory Cognitive Computing for Learning Population-level Brain Connectivity
Mayssa Soussia, Mohamed Ali Mahjoub, Islem Rekik
https://arxiv.org/abs/2508.11436 https://

Multi-Sensory Cognitive Computing for Learning Population-level Brain Connectivity
The generation of connectional brain templates (CBTs) has recently garnered significant attention for its potential to identify unique connectivity patterns shared across individuals. However, existing methods for CBT learning such as conventional machine learning and graph neural networks (GNNs) are hindered by several limitations. These include: (i) poor interpretability due to their black-box nature, (ii) high computational cost, and (iii) an exclusive focus on structure and topology, overlo…

Visual image reconstruction from brain activity via latent representation https://www.annualreviews.org/content/journals/10.1146/annurev-vision-110423-023616 by @…

Cyber Risks to Next-Gen Brain-Computer Interfaces: Analysis and Recommendations
Tyler Schroder, Renee Sirbu, Sohee Park, Jessica Morley, Sam Street, Luciano Floridi
https://arxiv.org/abs/2508.12571

Cyber Risks to Next-Gen Brain-Computer Interfaces: Analysis and Recommendations
Brain-computer interfaces (BCIs) show enormous potential for advancing personalized medicine. However, BCIs also introduce new avenues for cyber-attacks or security compromises. In this article, we analyze the problem and make recommendations for device manufacturers to better secure devices and to help regulators understand where more guidance is needed to protect patient safety and data confidentiality. Device manufacturers should implement the prior suggestions in their BCI products. These r…

Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
Zhuo Liu, Tao Chen
https://arxiv.org/abs/2508.11659

Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback Regulation and Residual Connections
Brain-like intelligent systems need brain-like learning methods. Equilibrium Propagation (EP) is a biologically plausible learning framework with strong potential for brain-inspired computing hardware. However, existing im-plementations of EP suffer from instability and prohibi-tively high computational costs. Inspired by the structure and dynamics of the brain, we propose a biologically plau-sible Feedback-regulated REsidual recurrent neural network (FRE-RNN) and study its learning performance…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomp...
Jaume Banus, Maxime Sermesant, Oscar Camara, Marco Lorenzi

UMind: A Unified Multitask Network for Zero-Shot M/EEG Visual Decoding
Chengjian Xu, Yonghao Song, Zelin Liao, Haochuan Zhang, Qiong Wang, Qingqing Zheng
https://arxiv.org/abs/2509.14772

UMind: A Unified Multitask Network for Zero-Shot M/EEG Visual Decoding
Decoding visual information from time-resolved brain recordings, such as EEG and MEG, plays a pivotal role in real-time brain-computer interfaces. However, existing approaches primarily focus on direct brain-image feature alignment and are limited to single-task frameworks or task-specific models. In this paper, we propose a Unified MultItask Network for zero-shot M/EEG visual Decoding (referred to UMind), including visual stimulus retrieval, classification, and reconstruction, where multiple t…

New shirt. (https://octophant.threadless.com/designs/destroy-a-i/mens/t-shirt/premium?variation=front&color=black )

A look at China's brain-computer interface industry as startups like BrainCo grow with state support; CCID says the sector is worth $450M, growing 20% annually (Bloomberg)

Biologically Plausible Online Hebbian Meta-Learning: Two-Timescale Local Rules for Spiking Neural Brain Interfaces
Sriram V. C. Nallani, Gautham Ramachandran, Sahil S. Shah
https://arxiv.org/abs/2509.14447

Biologically Plausible Online Hebbian Meta-Learning: Two-Timescale Local Rules for Spiking Neural Brain Interfaces
Brain-Computer Interfaces face challenges from neural signal instability and memory constraints for real-time implantable applications. We introduce an online SNN decoder using local three-factor learning rules with dual-timescale eligibility traces that avoid backpropagation through time while maintaining competitive performance. Our approach combines error-modulated Hebbian updates, fast/slow trace consolidation, and adaptive learning rate control, requiring only O(1) memory versus O(T) for B…

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis
Mia-Katrin Kvalsund, Mikkel Elle Lepper{\o}d
https://arxiv.org/abs/2507.12473

The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis
While modern AI continues to advance, the biological brain remains the pinnacle of neural networks in its robustness, adaptability, and efficiency. This review explores an AI architectural path inspired by the brain's structure, particularly the minicolumn hypothesis, which views the neocortex as a distributed system of repeated modules - a structure we connect to collective intelligence (CI). Despite existing work, there is a lack of comprehensive reviews connecting the cortical column to the …

How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots
Renyuan Liu, Haoting Zhou, Chuankai Fang, Qinbing Fu
https://arxiv.org/abs/2509.13827 https://

How Fly Neural Perception Mechanisms Enhance Visuomotor Control of Micro Robots
Anyone who has tried to swat a fly has likely been frustrated by its remarkable agility.This ability stems from its visual neural perception system, particularly the collision-selective neurons within its small brain.For autonomous robots operating in complex and unfamiliar environments, achieving similar agility is highly desirable but often constrained by the trade-off between computational cost and performance.In this context, insect-inspired intelligence offers a parsimonious route to low-p…

Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer
Augustine X. W. Lee, Pak-Hei Yeung, Jagath C. Rajapakse
https://arxiv.org/abs/2508.11450

Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer
Subcortical segmentation in neuroimages plays an important role in understanding brain anatomy and facilitating computer-aided diagnosis of traumatic brain injuries and neurodegenerative disorders. However, training accurate automatic models requires large amounts of labelled data. Despite the availability of publicly available subcortical segmentation datasets for Magnetic Resonance Imaging (MRI), a significant gap exists for Computed Tomography (CT). This paper proposes an automatic ensemble …

Control of a commercial vehicle by a tetraplegic human using a bimanual brain-computer interface
Xinyun Zou, Jorge Gamez, Meghna Menon, Phillip Ring, Chadwick Boulay, Likhith Chitneni, Jackson Brennecke, Shana R. Melby, Gracy Kureel, Kelsie Pejsa, Emily R. Rosario, Ausaf A. Bari, Aniruddh Ravindran, Tyson Aflalo, Spencer S. Kellis, Dimitar Filev, Florian Solzbacher, Richard A. Andersen

Control of a commercial vehicle by a tetraplegic human using a bimanual brain-computer interface
Brain-computer interfaces (BCIs) read neural signals directly from the brain to infer motor planning and execution. However, the implementation of this technology has been largely limited to laboratory settings, with few real-world applications. We developed a bimanual BCI system to drive a vehicle in both simulated and real-world environments. We demonstrate that an individual with tetraplegia, implanted with intracortical BCI electrodes in the posterior parietal cortex (PPC) and the hand knob…

Extracting Interpretable Higher-Order Topological Features across Multiple Scales for Alzheimer's Disease Classification
Dengyi Zhao, Shanyong Li, Yunping Wang, Chenfei Wang, Zhiheng Zhou, Guiying Yan, Xingqin Qi
https://arxiv.org/abs/2509.14634

Extracting Interpretable Higher-Order Topological Features across Multiple Scales for Alzheimer's Disease Classification
Brain network topology, derived from functional magnetic resonance imaging (fMRI), holds promise for improving Alzheimer's disease (AD) diagnosis. Current methods primarily focus on lower-order topological features, often overlooking the significance of higher-order features such as connected components, cycles, and cavities. These higher-order features are critical for understanding normal brain function and have been increasingly linked to the pathological mechanisms of AD. However, their qua…

budapest_connectome: Budapest Reference Connectome 3.0
A parameterizable consensus brain graph, derived from connectomes of 477 people, each computed from MRI datasets of the Human Connectome Project. Nodes are brain regions, and edges are weighted by the number of "tracks" that run between two nodes, as well as fiber length, fractional anisotropy and the number of occurrences in each of the 477 individuals.
This network has 1015 nodes and 121755 edges.
Tags: Biol…

'Pro-Life’ Groups Brag They Killed Funding for Children's Brain Cancer Research
https://jessica.substack.com/p/nih-brain-cancer-research-funding-pulled

Multi-Source Neural Activity Indices and Spatial Filters for EEG/MEG Inverse Problem: An Extension to MNE-Python
Julia Jurkowska, Joanna Dreszer, Monika Lewandowska, Krzysztof To{\l}pa, Tomasz Piotrowski
https://arxiv.org/abs/2509.14118

Multi-Source Neural Activity Indices and Spatial Filters for EEG/MEG Inverse Problem: An Extension to MNE-Python
Accurate EEG/MEG source localization is essential for understanding brain function, yet remains challenging because the inverse problem is inherently ill-posed. In spatial filtering (beamforming) approaches, single-source LCMV spatial filters, though widely used, suffer from source cancellation when sources are correlated - a common experimental scenario. Multi-source frameworks, such as the multi-source minimum-variance pseudo-unbiased reduced-rank (MV-PURE) method, offer improved reconstructi…

Estimating Covariate Effects on Functional Connectivity using Voxel-Level fMRI Data
Wei Zhao, Brian J. Reich, Emily C. Hector
https://arxiv.org/abs/2508.11213 https://

Estimating Covariate Effects on Functional Connectivity using Voxel-Level fMRI Data
Functional connectivity (FC) analysis of resting-state fMRI data provides a framework for characterizing brain networks and their association with participant-level covariates. Due to the high dimensionality of neuroimaging data, standard approaches often average signals within regions of interest (ROIs), which ignores the underlying spatiotemporal dependence among voxels and can lead to biased or inefficient inference. We propose to use a summary statistic -- the empirical voxel-wise correlati…

Brain Tumor Segmentation in Sub-Sahara Africa with Advanced Transformer and ConvNet Methods: Fine-Tuning, Data Mixing and Ensembling
Toufiq Musah, Chantelle Amoako-Atta, John Amankwaah Otu, Lukman E. Ismaila, Swallah Alhaji Suraka, Oladimeji Williams, Isaac Tigbee, Kato Hussein Wabbi, Samantha Katsande, Kanyiri Ahmed Yakubu, Adedayo Kehinde Lawal, Anita Nsiah Donkor, Naeem Mwinlanaah Adamu, Adebowale Akande, John Othieno, Prince Ebenezer Adjei, Zhang Dong, Confidence Raymond, Udunna C.…

Brain Tumor Segmentation in Sub-Sahara Africa with Advanced Transformer and ConvNet Methods: Fine-Tuning, Data Mixing and Ensembling
Brain tumors are among the deadliest cancers worldwide, with particularly devastating impact in Sub-Saharan Africa (SSA) where limited access to medical imaging infrastructure and expertise often delays diagnosis and treatment planning. Accurate brain tumor segmentation is crucial for treatment planning, surgical guidance, and monitoring disease progression, yet manual segmentation is time-consuming and subject to inter-observer variability. Recent advances in deep learning, based on Convolutio…

And off goes a $100 donation to @… in thanks to @… and @… who just gave my brain a 15-minute upgra…

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
Robiul Islam, Dmitry I. Ignatov, Karl Kaberg, Roman Nabatchikov
https://arxiv.org/abs/2509.14078

Exploring the Relationship between Brain Hemisphere States and Frequency Bands through Deep Learning Optimization Techniques
This study investigates classifier performance across EEG frequency bands using various optimizers and evaluates efficient class prediction for the left and right hemispheres. Three neural network architectures - a deep dense network, a shallow three-layer network, and a convolutional neural network (CNN) - are implemented and compared using the TensorFlow and PyTorch frameworks. Results indicate that the Adagrad and RMSprop optimizers consistently perform well across different frequency bands,…

Me:
My brain: Hey, remember this guy?
https://en.m.wikipedia.org/wiki/Horst_Wessel
My brain: Oh, no reason in particular.

Horst Wessel - Wikipedia

... We know from brain research that institutionally reared children have
enlarged, overly active amygdalas---an area of the brain involved in
emotional processing ---especially pay excessive attention to negative
information.  They are easily frightened.  Their emotion regulation
and mental health are permanently damaged, which is why the Romanian
orphanages were known as the "slaughterhouses of souls."
There are many parallels with animals reared in…

from my link log —
Cordoomceps: replacing an Amiga's brain with a Raspberry Pi running Doom.
https://mjg59.dreamwidth.org/73001.html
saved 2025-08-05

How does the #brain transfer #MotorSkills between hands?
This study reveals that transfer relies on re-expressing the neural patterns established during initial learning in distributed higher-order brain areas,
offering new insights into learning

Transfer of motor learning is associated with patterns of activity in the default mode network
How does the brain transfer motor skills between hands? This study reveals that transfer relies on re-expressing the neural patterns established during initial learning in distributed higher-order brain areas, offering new insights into learning generalization.

I’m not sleeping at the moment.
Maybe it’s because there’s just so much going on in my brain but I’m not sure.

From Mimicry to True Intelligence (TI) - A New Paradigm for Artificial General Intelligence
Meltem Subasioglu, Nevzat Subasioglu
https://arxiv.org/abs/2509.14474 https://…

From Mimicry to True Intelligence (TI) - A New Paradigm for Artificial General Intelligence
The debate around Artificial General Intelligence (AGI) remains open due to two fundamentally different goals: replicating human-like performance versus replicating human-like cognitive processes. We argue that current performance-based definitions are inadequate because they provide no clear, mechanism-focused roadmap for research, and they fail to properly define the qualitative nature of genuine intelligence. Drawing inspiration from the human brain, we propose a new paradigm that shifts the…

Neuralink plans a US clinical trial in October to test a brain implant that translates thoughts into text, hoping to put its device in a healthy person by 2030 (Ike Swetlitz/Bloomberg)
https://www.bloomberg.com/news/articles/202…

Sometimes I’m posting about Syndicalism. Suddenly, I’m deep into LGBTQIA rights, because yes, my brain’s got tabs open everywhere.
Mid-rant, autism brain hits and it’s like, nope, time to fire up YouTube on the Debian box, because special interests top all discourse. The playlist is always oddly specific.
Then, work panic: my boss exists, bills exist. Pretend to be productive, start the loop again.

Efficient Artifacts Removal for Adaptive Deep Brain Stimulation and a Temporal Event Localization Analysis
Tzu-Chi Liu, Po-Lin Chen, Yi-Chieh Chen, Po-Hsun Tu, Chih-Hua Yeh, Mun-Chun Yeap, Chiung-Chu Chen, Hau-Tieng Wu
https://arxiv.org/abs/2508.11459

Efficient Artifacts Removal for Adaptive Deep Brain Stimulation and a Temporal Event Localization Analysis
Adaptive deep brain stimulation (aDBS) leverages symptom-related biomarkers to deliver personalized neuromodulation therapy, with the potential to improve treatment efficacy and reduce power consumption compared to conventional DBS. However, stimulation-induced signal contamination remains a major technical barrier to advancing its clinical application. Existing artifact removal strategies, both front-end and back-end, face trade-offs between artifact suppression and algorithmic flexibility. Am…

#Multisensory vs. #unisensory learning: how they shape effective connectivity networks subserving unimodal and multimodal integration

Frontiers | Multisensory vs. unisensory learning: how they shape effective connectivity networks subserving unimodal and multimodal integration
The brain synthesizes meaningful interpretations out of the surrounding environment, by integrating sensory input collected by multiple senses. Learning base...

AI, AGI, and learning efficiency
An addendum to this: I'm someone who would accurately be called "anti-AI" in the modern age, yet I'm also an "AI researcher" in some ways (have only dabbled in neutral nets).
I don't like:
- AI systems that are the product of labor abuses towards the data workers who curate their training corpora.
- AI systems that use inordinate amounts of water and energy during an intensifying climate catastrophe.
- AI systems that are fundamentally untrustworthy and which reinforce and amplify human biases, *especially* when those systems are exposed in a way that invites harms.
- AI systems which are designed to "save" my attention or brain bandwidth but such my doing so cripple my understating of the things I might use them for when I fact that understanding was the thing I was supposed to be using my time to gain, and where the later lack of such understanding will be costly to me.
- AI systems that are designed by and whose hype fattens the purse of people who materially support genocide and the construction of concentration campus (a.k.a. fascists).
In other words, I do not like and except in very extenuating circumstances I will not use ChatGPT, Claude, Copilot, Gemini, etc.
On the other hand, I do like:
- AI research as an endeavor to discover new technologies.
- Generative AI as a research topic using a spectrum of different methods.
- Speculating about non-human intelligences, including artificial ones, and including how to behave ethically towards them.
- Large language models as a specific technique, and autoencoders and other neural networks, assuming they're used responsibly in terms of both resource costs & presentation to end users.
I write this because I think some people (especially folks without CS backgrounds) may feel that opposing AI for all the harms it's causing runs the risk of opposing technological innovation more broadly, and/or may feel there's a risk that they will be "left behind" as everyone else embraces the hype and these technologies inevitability become ubiquitous and essential (I know I feel this way sometimes). Just know that is entirely possible and logically consistent to both oppose many forms of modern AI while also embracing and even being optimistic about AI research, and that while LLMs are currently all the rage, they're not the endpoint of what AI will look like in the future, and their downsides are not inherent in AI development.

Performance of GPT-5 in Brain Tumor MRI Reasoning
Mojtaba Safari, Shansong Wang, Mingzhe Hu, Zach Eidex, Qiang Li, Xiaofeng Yang
https://arxiv.org/abs/2508.10865 https://…

Performance of GPT-5 in Brain Tumor MRI Reasoning
Accurate differentiation of brain tumor types on magnetic resonance imaging (MRI) is critical for guiding treatment planning in neuro-oncology. Recent advances in large language models (LLMs) have enabled visual question answering (VQA) approaches that integrate image interpretation with natural language reasoning. In this study, we evaluated GPT-4o, GPT-5-nano, GPT-5-mini, and GPT-5 on a curated brain tumor VQA benchmark derived from 3 Brain Tumor Segmentation (BraTS) datasets - glioblastoma (…

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Jiahui An, Bingjie Cheng, Dmitriy Rudyka, Elisa Donati, Sara Fabrikant
https://arxiv.org/abs/2509.14056

EEG-Based Cognitive Load Classification During Landmark-Based VR Navigation
Brain computer interfaces enable real-time monitoring of cognitive load, but their effectiveness in dynamic navigation contexts is not well established. Using an existing VR navigation dataset, we examined whether EEG signals can classify cognitive load during map-based wayfinding and whether classification accuracy depends more on task complexity or on individual traits. EEG recordings from forty-six participants navigating routes with 3, 5, or 7 map landmarks were analyzed with a nested cross…

A Noninvasive and Dispersive Framework for Estimating Nonuniform Conductivity of Brain Tumor in Patient-Specific Head Models
Yoshiki Kubota, Yosuke Nagata, Manabu Tamura, Akimasa Hirata
https://arxiv.org/abs/2509.14660

A Noninvasive and Dispersive Framework for Estimating Nonuniform Conductivity of Brain Tumor in Patient-Specific Head Models
We propose a noninvasive and dispersive framework for estimating the spatially nonuniform conductivity of brain tumors using MR images. The method consists of two components: (i) voxel-wise assignment of tumor conductivity based on reference values fitted to the Cole-Cole model using empirical data from the literature and (ii) fine-tuning of a deep learning model pretrained on healthy participants. A total of 67 cases, comprising both healthy participants and tumor patients and including 9,806 …

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
Zixia Zhou, Junyan Liu, Wei Emma Wu, Ruogu Fang, Sheng Liu, Qingyue Wei, Rui Yan, Yi Guo, Qian Tao, Yuanyuan Wang, Md Tauhidul Islam, Lei Xing
https://arxiv.org/abs/2508.11672

Revealing Neurocognitive and Behavioral Patterns by Unsupervised Manifold Learning from Dynamic Brain Data
Dynamic brain data, teeming with biological and functional insights, are becoming increasingly accessible through advanced measurements, providing a gateway to understanding the inner workings of the brain in living subjects. However, the vast size and intricate complexity of the data also pose a daunting challenge in reliably extracting meaningful information across various data sources. This paper introduces a generalizable unsupervised deep manifold learning for exploration of neurocognitive…

My wife, “my brain likes to play the word "fascism" to the tune of "biiiicycle" from queen's masterwork, bicycle race (1978)”

PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma
L. Zimmer, J. Weidner, M. Balcerak, F. Kofler, I. Ezhov, B. Menze, B. Wiestler
https://arxiv.org/abs/2509.13360

PREDICT-GBM: Platform for Robust Evaluation and Development of Individualized Computational Tumor Models in Glioblastoma
Glioblastoma is the most prevalent primary brain malignancy, distinguished by its highly invasive behavior and exceptionally high rates of recurrence. Conventional radiation therapy, which employs uniform treatment margins, fails to account for patient-specific anatomical and biological factors that critically influence tumor cell migration. To address this limitation, numerous computational models of glioblastoma growth have been developed, enabling generation of tumor cell distribution maps e…

Survived a week long ‘holiday’ with the blended family. The chaos completely drained me. Got back late Saturday night and was wiped out yesterday so didn’t get around to unpacking. Now I’m wiped out on Monday morning with the house turned upside down with holiday stuff, sat at my desk trying to work. Except I can’t work because the house is a shit tip. I need to get everything back in order to enable my brain to function. So I’ll creep away from my desk and get it sorted.

Multi-Plasticity Synergy with Adaptive Mechanism Assignment for Training Spiking Neural Networks
Yuzhe Liu, Xin Deng, Qiang Yu
https://arxiv.org/abs/2508.13673 https://

Multi-Plasticity Synergy with Adaptive Mechanism Assignment for Training Spiking Neural Networks
Spiking Neural Networks (SNNs) are promising brain-inspired models known for low power consumption and superior potential for temporal processing, but identifying suitable learning mechanisms remains a challenge. Despite the presence of multiple coexisting learning strategies in the brain, current SNN training methods typically rely on a single form of synaptic plasticity, which limits their adaptability and representational capability. In this paper, we propose a biologically inspired training…

Boosting the Robustness-Accuracy Trade-off of SNNs by Robust Temporal Self-Ensemble
Jihang Wang, Dongcheng Zhao, Ruolin Chen, Qian Zhang, Yi Zeng
https://arxiv.org/abs/2508.11279

Boosting the Robustness-Accuracy Trade-off of SNNs by Robust Temporal Self-Ensemble
Spiking Neural Networks (SNNs) offer a promising direction for energy-efficient and brain-inspired computing, yet their vulnerability to adversarial perturbations remains poorly understood. In this work, we revisit the adversarial robustness of SNNs through the lens of temporal ensembling, treating the network as a collection of evolving sub-networks across discrete timesteps. This formulation uncovers two critical but underexplored challenges-the fragility of individual temporal sub-networks a…

Contrastive Network Representation Learning
Zihan Dong, Xin Zhou, Ryumei Nakada, Lexin Li, Linjun Zhang
https://arxiv.org/abs/2509.11316 https://arxiv.org/…

Contrastive Network Representation Learning
Network representation learning seeks to embed networks into a low-dimensional space while preserving the structural and semantic properties, thereby facilitating downstream tasks such as classification, trait prediction, edge identification, and community detection. Motivated by challenges in brain connectivity data analysis that is characterized by subject-specific, high-dimensional, and sparse networks that lack node or edge covariates, we propose a novel contrastive learning-based statistic…

Pain Chain and Releasing the Brain
- changing the stories our bodies tell us -
#health #MentalHealth
https://

Pain Chain and Releasing the Brain
Stan Stewart - @muz4now

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

I hate how I started to look for signs of so-called generative AI in videos. First, it wastes CPU cycles in data centers and then it wastes energy in my brain.

💫 Inside the search for a universal signature of unconsciousness
#brain

Inside the search for a universal signature of unconsciousness
A universal marker of unconsciousness could transform both medicine and our understanding of the mind.

Perception of Brain-Computer Interface Implantation Surgery for Motor, Sensory, and Autonomic Restoration in Spinal Cord Injury and Stroke
Derrick Lin, Tracie Tran, Shravan Thaploo, Jose Gabrielle E. Matias, Joy E. Pixley, Zoran Nenadic, An H. Do
https://arxiv.org/abs/2507.11572

Perception of Brain-Computer Interface Implantation Surgery for Motor, Sensory, and Autonomic Restoration in Spinal Cord Injury and Stroke
(Abridged) Stroke and SCI are conditions that can significantly impact the QoL of survivors in both the physical and psychosocial domains. Both diseases often result in significant motor and sensory impairments that are not fully reversible despite current available therapies. Invasive BCIs have emerged as a promising means to bypass the site of injury and potentially restore motor and sensory function. However, to maximize the utility and participant satisfaction with such technology, particip…

This article is a good example of why I’m fed up with the “if used responsibly” argument:
https://thejournal.com/articles/2025/08/07/the-brain-drain-how-overreliance-on-ai-may-erode-creativity-and-critical-thin…

Each week, Metacurity offers our free and paid subscribers a run-down of the best infosec-related long reads.
This week's selection covers
--A twisted tale of how two men tortured someone for his crypto account passwords,
--Russia's cyber sector supports Putin's Ukraine war,
--A brain-reading implant requires a password,
--Social media algorithms didn't cause America's woes,
--The internet is really bad for children,
--More
…

Best infosec-related long reads for the week of 8/9/25
A twisted tale of how two men tortured someone for his crypto account passwords, Russia's cyber sector supports Putin's Ukraine war, A brain-reading implant requires a password, Social media algorithms didn't cause America's woes, The internet is really bad for children, more

Today I wrote asciidoc and restructuredtext and now I'm trying to edit a markdown file and my brain doesn't know what formatting is any more.

PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding
Changhong Jing, Yan Liu, Shuqiang Wang, Bruce X. B. Yu, Gong Chen, Zhejing Hu, Zhi Zhang, Yanyan Shen
https://arxiv.org/abs/2508.11357

PTSM: Physiology-aware and Task-invariant Spatio-temporal Modeling for Cross-Subject EEG Decoding
Cross-subject electroencephalography (EEG) decoding remains a fundamental challenge in brain-computer interface (BCI) research due to substantial inter-subject variability and the scarcity of subject-invariant representations. This paper proposed PTSM (Physiology-aware and Task-invariant Spatio-temporal Modeling), a novel framework for interpretable and robust EEG decoding across unseen subjects. PTSM employs a dual-branch masking mechanism that independently learns personalized and shared spat…

Segmenting Thalamic Nuclei: T1 Maps Provide a Reliable and Efficient Solution
Anqi Feng, Zhangxing Bian, Samuel W. Remedios, Savannah P. Hays, Blake E. Dewey, Jiachen Zhuo, Dan Benjamini, Jerry L. Prince
https://arxiv.org/abs/2508.12508

Segmenting Thalamic Nuclei: T1 Maps Provide a Reliable and Efficient Solution
Accurate thalamic nuclei segmentation is crucial for understanding neurological diseases, brain functions, and guiding clinical interventions. However, the optimal inputs for segmentation remain unclear. This study systematically evaluates multiple MRI contrasts, including MPRAGE and FGATIR sequences, quantitative PD and T1 maps, and multiple T1-weighted images at different inversion times (multi-TI), to determine the most effective inputs. For multi-TI images, we employ a gradient-based salien…

Discerning and quantifying high frequency activities in EEG under normal and epileptic conditions
Jyotiraj Nath, Shreya Banerjee, Bhaswati Singha Deo, Mayukha Pal, Prasanta K. Panigrahi
https://arxiv.org/abs/2508.12670

Discerning and quantifying high frequency activities in EEG under normal and epileptic conditions
We investigate the nature of the modifications in the temporal dynamics manifested in the high-frequency EEG spectra of the normal human brain in comparison to the diseased brain undergoing epilepsy. For this purpose, the Fourier reconstruction is efficaciously made use of after Welch's transform, which helped identify the relevant frequency components undergoing significant changes in the case of epilepsy. The temporal dynamics involved in the EEG signals and their associated variations showed…

Rest2Visual: Predicting Visually Evoked fMRI from Resting-State Scans
Chuyang Zhou, Ziao Ji, Daochang Liu, Dongang Wang, Chenyu Wang, Chang Xu
https://arxiv.org/abs/2509.13612 h…

Rest2Visual: Predicting Visually Evoked fMRI from Resting-State Scans
Understanding how spontaneous brain activity relates to stimulus-driven neural responses is a fundamental challenge in cognitive neuroscience. While task-based functional magnetic resonance imaging (fMRI) captures localized stimulus-evoked brain activation, its acquisition is costly, time-consuming, and difficult to scale across populations. In contrast, resting-state fMRI (rs-fMRI) is task-free and abundant, but lacks direct interpretability. We introduce Rest2Visual, a conditional generative …

Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception
Mathis Rezzouk, Fabrice Gagnon, Alyson Champagne, Mathieu Roy, Philippe Albouy, Michel-Pierre Coll, Cem Subakan
https://arxiv.org/abs/2508.11691

Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception
EEG-based analysis of pain perception, enhanced by machine learning, reveals how the brain encodes pain by identifying neural patterns evoked by noxious stimulation. However, a major challenge that remains is the generalization of machine learning models across individuals, given the high cross-participant variability inherent to EEG signals and the limited focus on direct pain perception identification in current research. In this study, we systematically evaluate the performance of cross-part…

Bloomberg, September 18, 2025: China's brain implant startups take on Musk's Neuralink in new tech race https://archive.ph/wsvBh "America’s leadership in the cutting-edge field of brain technology is being challenged as Chinese startups rise with the support of a full-throttle policy drive."

Design and Validation of Metasurface Transmitarrays for Time-Reversal Microwave Hyperthermia in Deep Brain Tumors
Alireza Rahmani, Mohammad Javad Hajiahmadi, Reza Faraji-Dana
https://arxiv.org/abs/2509.14429

Design and Validation of Metasurface Transmitarrays for Time-Reversal Microwave Hyperthermia in Deep Brain Tumors
This work presents a systematic design of a metasurface-based microwave hyperthermia system, leveraging the time-reversal technique to achieve precise focusing of electromagnetic energy at deep-seated brain tumors. The manuscript includes detailed numerical simulations on realistic and spherical head models, as well as the experimental realization of a quad-layer transmissive metasurface and an electrothermal head phantom. This represents the first experimental demonstration of metasurface-base…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Automatic brain tumor segmentation in 2D intra-operative ultrasound images using magnetic resonan...
Mathilde Faanes, Ragnhild Holden Helland, Ole Solheim, S\'ebastien Muller, Ingerid Reinertsen

Brain-Robot Interface for Exercise Mimicry
Carl Bettosi, Emilyann Nault, Lynne Baillie, Markus Garschall, Marta Romeo, Beatrix Wais-Zechmann, Nicole Binderlehner, Theodoros Georgio
https://arxiv.org/abs/2509.11306

Brain-Robot Interface for Exercise Mimicry
For social robots to maintain long-term engagement as exercise instructors, rapport-building is essential. Motor mimicry--imitating one's physical actions--during social interaction has long been recognized as a powerful tool for fostering rapport, and it is widely used in rehabilitation exercises where patients mirror a physiotherapist or video demonstration. We developed a novel Brain-Robot Interface (BRI) that allows a social robot instructor to mimic a patient's exercise movements in real-t…

fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting
Alicia Durrer, Florentin Bieder, Paul Friedrich, Bjoern Menze, Philippe C. Cattin, Florian Kofler
https://arxiv.org/abs/2507.13146

fastWDM3D: Fast and Accurate 3D Healthy Tissue Inpainting
Healthy tissue inpainting has significant applications, including the generation of pseudo-healthy baselines for tumor growth models and the facilitation of image registration. In previous editions of the BraTS Local Synthesis of Healthy Brain Tissue via Inpainting Challenge, denoising diffusion probabilistic models (DDPMs) demonstrated qualitatively convincing results but suffered from low sampling speed. To mitigate this limitation, we adapted a 2D image generation approach, combining DDPMs w…

"I deleted my second brain" by Joan Westenberg is very worth reading. I am on a similar path right now, removing a lot of digital cruft and structures I had applied to my life in the belief that I could gain more control.
https://www.joanwestenberg.com/p/i-deleted

I Deleted My Second Brain
Why I Erased 10,000 Notes, 7 Years of Ideas, and Every Thought I Tried to Save

Robustness and Diagnostic Performance of Super-Resolution Fetal Brain MRI
Ema Masterl, Tina Vipotnik Vesnaver, \v{Z}iga \v{S}piclin
https://arxiv.org/abs/2509.10257 https://

Robustness and Diagnostic Performance of Super-Resolution Fetal Brain MRI
Fetal brain MRI relies on rapid multi-view 2D slice acquisitions to reduce motion artifacts caused by fetal movement. However, these stacks are typically low resolution, may suffer from motion corruption, and do not adequately capture 3D anatomy. Super-resolution reconstruction (SRR) methods aim to address these limitations by combining slice-to-volume registration and super-resolution techniques to generate high-resolution (HR) 3D volumes. While several SRR methods have been proposed, their co…

Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural Networks
Yuki Tomoda, Ichiro Tsuda, Yutaka Yamaguti
https://arxiv.org/abs/2507.12858

Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural Networks
Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system. Previous research has modeled this process using artificial neural networks with specific constraints. Here, we propose a novel approach that induces functional differentiation in recurrent neural networks by minimizing mutual information between neural subgroups via mutual information neural estimation. We apply our method to a 2-bit working memory task…

AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
Xiaoqing Chen, Siyang Li, Dongrui Wu
https://arxiv.org/abs/2507.11911 https://

AFPM: Alignment-based Frame Patch Modeling for Cross-Dataset EEG Decoding
Electroencephalogram (EEG) decoding models for brain-computer interfaces (BCIs) struggle with cross-dataset learning and generalization due to channel layout inconsistencies, non-stationary signal distributions, and limited neurophysiological prior integration. To address these issues, we propose a plug-and-play Alignment-Based Frame-Patch Modeling (AFPM) framework, which has two main components: 1) Spatial Alignment, which selects task-relevant channels based on brain-region priors, aligns EEG…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Simulated Language Acquisition in a Biologically Realistic Model of the Brain
Daniel Mitropolsky, Christos Papadimitriou
https://arxiv.org/abs/2507.11788 h…

Simulated Language Acquisition in a Biologically Realistic Model of the Brain
Despite tremendous progress in neuroscience, we do not have a compelling narrative for the precise way whereby the spiking of neurons in our brain results in high-level cognitive phenomena such as planning and language. We introduce a simple mathematical formulation of six basic and broadly accepted principles of neuroscience: excitatory neurons, brain areas, random synapses, Hebbian plasticity, local inhibition, and inter-area inhibition. We implement a simulated neuromorphic system based on t…

Sex-Specific Vascular Score: A Novel Perfusion Biomarker from Supervoxel Analysis of 3D pCASL MRI
Sneha Noble, Neelam Sinha, Vaanathi Sundareshan, Thomas Gregor Issac
https://arxiv.org/abs/2508.13173

Sex-Specific Vascular Score: A Novel Perfusion Biomarker from Supervoxel Analysis of 3D pCASL MRI
We propose a novel framework that leverages 3D pseudo-continuous arterial spin labeling (3D pCASL) MRI to compute sex-specific vascular scores that quantify cerebrovascular health and potential disease susceptibility. The brain is parcellated into spatially contiguous regions of homogeneous perfusion using supervoxel clustering, capturing both microvascular and macrovascular contributions. Mean cerebral blood flow (CBF) values are extracted from 186 cognitively healthy participants and used to …

Neuromorphic Intelligence
Marcel van Gerven
https://arxiv.org/abs/2509.11940 https://arxiv.org/pdf/2509.11940

Neuromorphic Intelligence
Neuromorphic computing seeks to replicate the remarkable efficiency, flexibility, and adaptability of the human brain in artificial systems. Unlike conventional digital approaches, which depend on massive computational and energy resources, neuromorphic systems exploit brain-inspired principles of computation to achieve orders of magnitude greater energy efficiency. By drawing on insights from artificial intelligence, neuroscience, physics, chemistry, and materials science, neuromorphic computi…

Crosslisted article(s) found for q-bio.NC. https://arxiv.org/list/q-bio.NC/new
[1/1]:
- Toward Practical Equilibrium Propagation: Brain-inspired Recurrent Neural Network with Feedback R...
Zhuo Liu, Tao Chen

fly_hemibrain: Fly hemibrain (2020)
A synaptic map of the hemibrain connectome of fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using EM reconstruction techniques. Neurons are labeled by their type. Edges are annotated by the connection strength between the neurons.
This network has 21739 nodes and 4259624 edges.
Tags: Biological, Connectome, Weighted, Metadata

Should the Neuralink Blindsight Informed Consent Form for a brain implant mention noninvasive alternatives such as The vOICe vision BCI? #BCI #ethics #NeuroEthics

CATVis: Context-Aware Thought Visualization
Tariq Mehmood, Hamza Ahmad, Muhammad Haroon Shakeel, Murtaza Taj
https://arxiv.org/abs/2507.11522 https://

CATVis: Context-Aware Thought Visualization
EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant challenge due to their complex and noisy nature. We thus propose a novel 5-stage framework for decoding visual representations from EEG signals: (1) an EEG encoder for concept classification, (2) cross-modal alignment of EEG and text embeddings in CLIP feature space, (3) captio…

Sensitivity of literature $T_1$ mapping methods to the underlying magnetization transfer parameters
Jakob Assl\"ander
https://arxiv.org/abs/2509.13644 https://

Sensitivity of literature $T_1$ mapping methods to the underlying magnetization transfer parameters
Purpose: Magnetization transfer (MT) has been identified as the principal source of $T_1$ variability in the MRI literature. This study assesses the sensitivity of established $T_1$ mapping techniques to variations in the underlying MT parameters. Methods: For each $T_1$-mapping method, the observed $T_1$ was simulated as a function of the underlying MT parameters $p_i^\text{MT}$, corresponding to different brain regions of interest (ROIs) at 3T. As measures of sensitivity, the derivatives $\…

Mapping Emotions in the Brain: A Bi-Hemispheric Neural Model with Explainable Deep Learning
David Freire-Obreg\'on, Agnieszka Dubiel, Prasoon Kumar Vinodkumar, Gholamreza Anbarjafari, Dorota Kami\'nska, Modesto Castrill\'on-Santana
https://arxiv.org/abs/2507.12625

Mapping Emotions in the Brain: A Bi-Hemispheric Neural Model with Explainable Deep Learning
Recent advances have shown promise in emotion recognition from electroencephalogram (EEG) signals by employing bi-hemispheric neural architectures that incorporate neuroscientific priors into deep learning models. However, interpretability remains a significant limitation for their application in sensitive fields such as affective computing and cognitive modeling. In this work, we introduce a post-hoc interpretability framework tailored to dual-stream EEG classifiers, extending the Local Interp…

Neuroaesthetics and the Science of Visual Experience
Harish Vijayakumar
https://arxiv.org/abs/2507.11599 https://arxiv.org/pdf/2507.1…

Neuroaesthetics and the Science of Visual Experience
Neuroaesthetics is an interdisciplinary field that brings together neuroscience, psychology, and the arts to explore how the human brain perceives and responds to visual beauty. This paper examines the neural mechanisms behind aesthetic experiences, aiming to explain why certain designs or artworks feel emotionally or cognitively "right." By analyzing the interaction between perception, emotion, and cognition, neuroaesthetics reveals how beauty is constructed in the brain and how this understan…

HUN-REN RCNS and its consortium receive prestigious European funding for the first human testing of a prosthesis designed to restore vision https://www.ttk.hun-ren.hu/en/recent-news/

HUN-REN RCNS and its Consortium Receive Prestigious European Funding for the First Human Testing of a Prosthesis Designed to Restore Vision
Over the past five years, the Belgian startup ReVision Implant has been developing a groundbreaking brain prosthesis that aims to partially restore vision to the blind. A consortium was formed to carry out the first human testing of the technology, consisting of the manufacturing startup and three research groups, including the

Why all roads don't lead to Rome: Representation geometry varies across the human visual cortical hierarchy
Arna Ghosh, Zahraa Chorghay, Shahab Bakhtiari, Blake A. Richards
https://arxiv.org/abs/2509.13459

Why all roads don't lead to Rome: Representation geometry varies across the human visual cortical hierarchy
Biological and artificial intelligence systems navigate the fundamental efficiency-robustness tradeoff for optimal encoding, i.e., they must efficiently encode numerous attributes of the input space while also being robust to noise. This challenge is particularly evident in hierarchical processing systems like the human brain. With a view towards understanding how systems navigate the efficiency-robustness tradeoff, we turned to a population geometry framework for analyzing representations in t…

3DViT-GAT: A Unified Atlas-Based 3D Vision Transformer and Graph Learning Framework for Major Depressive Disorder Detection Using Structural MRI Data
Nojod M. Alotaibi, Areej M. Alhothali, Manar S. Ali
https://arxiv.org/abs/2509.12143

3DViT-GAT: A Unified Atlas-Based 3D Vision Transformer and Graph Learning Framework for Major Depressive Disorder Detection Using Structural MRI Data
Major depressive disorder (MDD) is a prevalent mental health condition that negatively impacts both individual well-being and global public health. Automated detection of MDD using structural magnetic resonance imaging (sMRI) and deep learning (DL) methods holds increasing promise for improving diagnostic accuracy and enabling early intervention. Most existing methods employ either voxel-level features or handcrafted regional representations built from predefined brain atlases, limiting their a…

Sources say Meta's chaotic culture and lack of vision have led to AI brain drain; Meta strongly denies it has had issues with talent and retention (Rashi Shrivastava/Forbes)
https://www.forbes.com/sites/rashishrivast

How Meta Lost Its Way With AI Talent
At Meta, a chaotic culture and lack of vision have led to brain drain, with rivals saying its AI talent is lackluster. But Zuckerberg’s frenzied hiring spree hasn’t stopped the departures.

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Full-Wave Modeling of Transcranial Ultrasound using Volume-Surface Integral Equations and CT-Derived Heterogeneous Skull Data
Alberto Almuna-Morales, Danilo Aballay, Pierre G\'elat, Reza Haqshenas, Elwin van 't Wout
https://arxiv.org/abs/2508.11100

Full-Wave Modeling of Transcranial Ultrasound using Volume-Surface Integral Equations and CT-Derived Heterogeneous Skull Data
Transcranial ultrasound therapy uses focused acoustic energy to induce therapeutic bioeffects in the brain. Ultrasound is transmitted through the skull, which is highly attenuating and heterogeneous, causing beam distortion, reducing focal pressure, and shifting the target location. Computational models are frequently used for predicting beam aberration, assessing cranial heating, and correcting the phase of ultrasound transducers. These models often rely on computed tomography (CT) images to b…

Foundation Models for Brain Signals: A Critical Review of Current Progress and Future Directions
Gayal Kuruppu, Neeraj Wagh, Yogatheesan Varatharajah
https://arxiv.org/abs/2507.11783

Foundation Models for Brain Signals: A Critical Review of Current Progress and Future Directions
Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised EEG encoders to learn robust EEG patterns and their over-reliance on expensive signal annotations have sparked a transition towards general-purpose self-supervised EEG encoders, i.e., EEG foundation models (EEG-FMs), for robust and scalable EEG feature extraction. However, the real-world readiness of early EEG-FMs and the rub…

DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification
Fazle Rafsani, Jay Shah, Catherine D. Chong, Todd J. Schwedt, Teresa Wu
https://arxiv.org/abs/2509.12512

DinoAtten3D: Slice-Level Attention Aggregation of DinoV2 for 3D Brain MRI Anomaly Classification
Anomaly detection and classification in medical imaging are critical for early diagnosis but remain challenging due to limited annotated data, class imbalance, and the high cost of expert labeling. Emerging vision foundation models such as DINOv2, pretrained on extensive, unlabeled datasets, offer generalized representations that can potentially alleviate these limitations. In this study, we propose an attention-based global aggregation framework tailored specifically for 3D medical image anoma…

Sam Altman says OpenAI "totally screwed up some things" on the GPT-5 rollout, confirms plans to fund a brain-computer interface startup to rival Neuralink (Alex Heath/The Verge)
https://www.theverge.com/command-line-news

I talked to Sam Altman about the GPT-5 launch fiasco
OpenAI CEO Sam Altman addressed criticism of GPT-5’s rollout, the AI bubble, launching a social network and brain-computer interface, buying Google Chrome, and more.

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration
Ziyi He, Albert C. S. Chung

Built to learn - How the brain learns to see #neuroscience
Development of coherent cortical responses…

EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios
Yubo Hou, Zhengxin Zhang, Ziyi Wang, Wenlian Lu, Jianfeng Feng, Taiping Zeng
https://arxiv.org/abs/2507.12263

EEG-fused Digital Twin Brain for Autonomous Driving in Virtual Scenarios
Current methodologies typically integrate biophysical brain models with functional magnetic resonance imaging(fMRI) data - while offering millimeter-scale spatial resolution (0.5-2 mm^3 voxels), these approaches suffer from limited temporal resolution (>0.5 Hz) for tracking rapid neural dynamics during continuous tasks. Conversely, Electroencephalogram (EEG) provides millisecond-scale temporal precision (<=1 ms sampling rate) for real-time guidance of continuous task execution, albeit constrain…

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Hanxue Gu, Yaqian Chen, Nicholas Konz, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2507.11569

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Foundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration. However, their performance has mostly been tested in the context of rigid or less complex structures, such as the brain or abdominal organs, and it remains unclear whether these models can handle more challenging, deformable anatomy. Breast MRI registration is particularly difficult due to significant anatomical variation b…

A transformation from vision to imagery in the human brain https://www.biorxiv.org/content/10.1101/2025.09.02.672180v1 "Extensive work has shown that the visual cortex is reactivated during mental imagery";

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networks
Mujie Liu, Mengchu Zhu, Qichao Dong, Ting Dang, Jiangang Ma, Jing Ren, Feng Xia
https://arxiv.org/abs/2509.10524

Data-Efficient Psychiatric Disorder Detection via Self-supervised Learning on Frequency-enhanced Brain Networks
Psychiatric disorders involve complex neural activity changes, with functional magnetic resonance imaging (fMRI) data serving as key diagnostic evidence. However, data scarcity and the diverse nature of fMRI information pose significant challenges. While graph-based self-supervised learning (SSL) methods have shown promise in brain network analysis, they primarily focus on time-domain representations, often overlooking the rich information embedded in the frequency domain. To overcome these lim…

Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
Gianluca Gaglioti, Alessandra Cardinale, Cosimo Lupo, Thierry Nieus, Federico Marmoreo, Robin Gutzen, Michael Denker, Andrea Pigorini, Marcello Massimini, Simone Sarasso, Pier Stanislao Paolucci, Giulia De Bonis
https://

Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
The simulation of whole-brain dynamics should reproduce realistic spontaneous and evoked neural activity across different scales, including emergent rhythms, spatio-temporal activation patterns, and macroscale complexity. Once a mathematical model is selected, its configuration must be determined by properly setting its parameters. A critical preliminary step in this process is defining an appropriate set of observables to guide the selection of model configurations (parameter tuning), laying t…

Transcranial focused ultrasound for identifying the neural substrate of conscious perception https://arxiv.org/abs/2507.08517 Indeed I see long-term potential for phased-array tFUS as a visual prosthesis for the late-blind. The company Nudge is developing a phased-array tFUS helmet.

Transcranial Focused Ultrasound for Identifying the Neural Substrate of Conscious Perception
Identifying what aspects of brain activity are responsible for conscious perception remains one of the most challenging problems in science. While progress has been made through psychophysical studies employing EEG and fMRI, research would greatly benefit from improved methods for stimulating the brain in healthy human subjects. Traditional techniques for neural stimulation through the skull, including electrical or magnetic stimulation, suffer from coarse spatial resolution and have limited ab…

Biomarkers of brain diseases
Pascal Helson, Arvind Kumar
https://arxiv.org/abs/2509.10547 https://arxiv.org/pdf/2509.10547…

Biomarkers of brain diseases
Despite the diversity of brain data acquired and advanced AI-based algorithms to analyze them, brain features are rarely used in clinics for diagnosis and prognosis. Here we argue that the field continues to rely on cohort comparisons to seek biomarkers, despite the well-established degeneracy of brain features. Using a thought experiment, we show that more data and more powerful algorithms will not be sufficient to identify biomarkers of brain diseases. We argue that instead of comparing patie…

(video) NOVA: Artificial vision using brain implants https://www.pbslearningmedia.org/resource/nvbs-sci-artificialvision/artificial-vision-using-brain-implants-nova/nova-premium-collection/ on the I…

Spontaneous Spatial Cognition Emerges during Egocentric Video Viewing through Non-invasive BCI
Weichen Dai, Yuxuan Huang, Li Zhu, Dongjun Liu, Yu Zhang, Qibin Zhao, Andrzej Cichocki, Fabio Babiloni, Ke Li, Jianyu Qiu, Gangyong Jia, Wanzeng Kong, Qing Wu
https://arxiv.org/abs/2507.12417

Spontaneous Spatial Cognition Emerges during Egocentric Video Viewing through Non-invasive BCI
Humans possess a remarkable capacity for spatial cognition, allowing for self-localization even in novel or unfamiliar environments. While hippocampal neurons encoding position and orientation are well documented, the large-scale neural dynamics supporting spatial representation, particularly during naturalistic, passive experience, remain poorly understood. Here, we demonstrate for the first time that non-invasive brain-computer interfaces (BCIs) based on electroencephalography (EEG) can decod…