Neuroscience
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Related Articles from SNS
Inside the Visual Mind: Neuroscience-Motivated Concept Circuits for Interpreting and Steering Vision Transformers
new Abstract: Despite high accuracy, Vision Transformer (ViT) predictions can be driven by spurious cues, raising the need to understand their inner workings before safe deployment. Sparse autoencoders (SAEs) provide a promising lens for decomposing model representations into human-interpretable concepts, yet adapting SAE-based interpretation to ViTs remains challenging due to limited control over concept coverage and subjective, non-scalable feature interpretation. To fill the gaps,...
A Monosemantic Attribution Framework for Stable Interpretability in Clinical Neuroscience Transformer-Based Language Models
arXiv:2601.17952v2 Announce Type: replace Abstract: Interpretability remains a key challenge for deploying language models (LM) in clinical settings such as progression diagnosis of Alzheimer disease, where early and trustworthy predictions are essential. Existing attribution methods exhibit high inter-method variability and unstable explanations due to the polysemantic nature of Transformer-Based LM and LLM representations, while mechanistic interpretability approaches lack direct alignment...
Updating the standard neuron model in artificial neural networks
Announce Type: replace Abstract: From their inception in the 1950s, artificial neural networks (ANNs) started using the so-called point neuron model then prevalent in neuroscience, hoping that this analogy would allow for a better emulation of brain function. Over the years the neuroscience literature has shown that the point neuron model is too simplistic to properly represent many fundamental neural processes; however, the standard neuron model in ANNs still remains the same. Here we...
Updating the standard neuron model in artificial neural networks
Announce Type: new Abstract: From their inception in the 1950s, artificial neural networks (ANNs) started using the so-called point neuron model then prevalent in neuroscience, hoping that this analogy would allow for a better emulation of brain function. Over the years the neuroscience literature has shown that the point neuron model is too simplistic to properly represent many fundamental neural processes; however, the standard neuron model in ANNs still remains the same. Here we...
CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Data
arXiv:2604.04958v3 Announce Type: replace-cross Abstract: Recent work suggests that large-scale, multi-animal modeling can significantly improve neural recording analysis. However, for functional calcium traces, existing approaches remain task-specific, limiting transfer across common neuroscience objectives.
Brain2Text Decoding Model Reveals the Neural Mechanisms of Visual Semantic Processing
arXiv:2503.22697v3 Announce Type: replace-cross Abstract: Decoding sensory experiences from neural activity to reconstruct human-perceived visual stimuli and semantic content remains a challenge in neuroscience and artificial intelligence. Despite notable progress in current brain decoding models, a critical gap still persists in their systematic integration with established neuroscientific theories and the exploration of underlying neural mechanisms. Here, we present a novel framework that...
Mapping Whisper Representations to Human ECoG Responses with Interpretable Time-Resolved Neural Encoding
cross Abstract: Understanding how speech foundation models relate to human cortical activity is a key challenge for computational neuroscience. Here, we investigate how internal representations from Whisper predict intracranial ECoG responses during naturalistic speech perception. We introduce a time-resolved neural encoder that combines speech embeddings with a recurrent temporal model and soft attention, allowing us to examine layer-wise brain alignment.
'US war on Iran not about the Iranian people: Europe can put the issue of human rights on the table'
Nadia Massih is pleased to welcome Mahmood Amiry-Moghaddam, President of Iran Human Rights and Professor of Neuroscience at University of Oslo. He offers a stark assessment of the Iranian regime's response to recent unrest and wartime conditions. Speaking from Oslo after the partial lifting of a prolonged internet blackout, Professor Amiry-Moghaddam argues that the authorities have exploited international attention on regional conflict to intensify domestic repression.
A hitchhiker's guide to Poisson gradient estimation
arXiv:2602.03896v2 Announce Type: replace-cross Abstract: Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two approaches address this: *Exponential Arrival Time* (EAT) simulation and *Gumbel-SoftMax* (GSM) relaxation. We provide the first systematic comparison of these methods, along with practical guidance for practitioners.
Flexible neural encoding predicts the comprehension of degraded speech
How listeners track a variable and continuous acoustic speech signal and parse it into meaningful linguistic representations is a question central to auditory neuroscience. Moreover, the resilience of this process to acoustic signal degradation is not fully understood. The current study consists of a listening task wherein participants (n = 38) were presented with a naturalistic story whilst undergoing continuous electroencephalography (EEG).