Computational Neuroscience
Computational models of the brain, neural coding, and brain-computer interfaces.
q-bio.NC · 115 papersLetting the neural code speak: Automated characterization of monkey visual neurons through human language
A novel framework uses generative models and neural digital twins to characterize monkey visual neurons with concise, verifiable natural language descriptions.
Empirical scaling laws in balanced networks with conductance-based synapses
Recurrent neural networks with conductance-based synapses and spike correlations produce realistic membrane potential variability.
Self-organized MT Direction Maps Emerge from Spatiotemporal Contrastive Optimization
A spatiotemporal TDANN model, trained with self-supervised learning, spontaneously generates brain-like direction maps in the visual cortex.
Accounting for Missed Events in the Bayesian Modeling of IP3R Multimodal Gating
A Bayesian approach with missed event correction refines IP3R channel gating models, clarifying its multimodal behavior and kinetic parameters.
Positive Alignment: Artificial Intelligence for Human Flourishing
This paper introduces "Positive Alignment," an AI research agenda focused on developing systems that actively support human and ecological flourishing beyond just safety.
Predictive and feedback signals differently shape the formation of group-level and individualized language representations
This study shows that prediction shapes group-level language learning, while feedback explains individual differences in adult language acquisition.
How Much is Brain Data Worth for Machine Learning?
This paper quantifies the value of brain data for machine learning, deriving scaling laws and exchange rates between brain and task samples.
Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology
Meow-Omni 1 is the first quad-modal MLLM for feline ethology, fusing video, audio, physiology, and text to achieve SOTA intent recognition.
Automated Optical Density Normalization for Myelin Quantification: Cross-Modal Validation with 7T Ex Vivo MRI
This paper introduces an automated pipeline for normalizing optical density in myelin histopathology, improving quantitative analysis and cross-modal correlation with MRI.
NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
NeuralBench is a unified open-source framework for systematically benchmarking AI models of brain activity, including a large EEG benchmark.
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
Frontier Large Reasoning Models (LRMs) align with human game learning behavior and brain activity, outperforming deep reinforcement learning.
Dynamical mechanisms of flexible phase-locking in cortical theta oscillators
This paper reveals how multi-timescale inhibitory currents enable cortical theta oscillators to flexibly phase-lock to a wide range of rhythmic inputs.
Learning Cross-Atlas Consistent Brain Disorder Representations via Disentangled Multi-Atlas Functional Connectivity Learning
MADCLE learns cross-atlas consistent brain disorder representations from fMRI functional connectivity by disentangling disease-related factors from atlas-specific and covariate noise.
Partitioning Neural Co-Variability
Introduces PMNLV, a novel model to partition neural co-variability, revealing shared population gain covariance peaks in primary visual cortex.
Beyond Object-Level Alignment: Do Brains and DNNs Preserve the Same Transformations?
A new method, Naturality Violation Score (NVS), assesses if brains and DNNs preserve the same transformations among stimuli, revealing hierarchical alignment.
A multi-scale information geometry reveals the structure of mutual information in neural populations
A multi-scale information geometry framework reveals how neural populations encode sensory information, linking directly to mutual information.
Decoding Alignment without Encoding Alignment: A critique of similarity analysis in neuroscience
Decoding alignment metrics can be misleading, as similar representations may arise from small neural subsets; encoding analysis offers a more robust comparison.
Think-Aloud Reshapes Automated Cognitive Model Discovery Beyond Behavior
This paper shows that using think-aloud data significantly improves automated cognitive model discovery, revealing mechanisms beyond behavioral data alone.
A Generalized Framework of Antisymmetric Polyspectral Indices for Identifying High-Order Neural Interactions
This paper introduces novel antisymmetric polyspectral indices to accurately identify high-order neural interactions, overcoming issues like volume conduction.
Dissociating spatial frequency reliance from adversarial robustness advantages in neurally guided deep convolutional neural networks
Neurally aligned DCNNs' adversarial robustness isn't primarily driven by spatial frequency reliance, but by learning more human-like representations.
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