Computational Neuroscience
Computational models of the brain, neural coding, and brain-computer interfaces.
q-bio.NC · 115 papersA foundation model of vision, audition, and language for in-silico neuroscience
TRIBE v2 is a tri-modal AI foundation model that accurately predicts human brain activity across vision, audition, and language, enabling in-silico neuroscience.
Neural Manifolds as Crystallized Embeddings: A Synthesis of the Free Energy Principle, Generalized Synchronization, and Hebbian Plasticity
This paper proposes that neural manifolds, as predicted by the Free Energy Principle, emerge from generalized synchronization and Hebbian plasticity.
Cusped singularities organize mixed-mode oscillations in mutually inhibitory slow-fast systems
Cusped singularities universally organize mixed-mode oscillations (MMOs) in mutually inhibitory slow-fast neural systems.
Learning reveals invisible structure in low-rank RNNs
A new theory for low-rank RNNs reveals 'loss-invisible' overlaps that govern learning dynamics and store training history, offering testable predictions.
NeuralSet: A High-Performing Python Package for Neuro-AI
NeuralSet is a Python package that unifies diverse neural data and stimuli processing for neuro-AI research, scaling from local to cluster.
Inferring Active Neural Circuits Using Diffusion Scores
SBTG infers lag-specific neural circuit interactions from population activity using denoising score models, avoiding parametric assumptions and omitted-lag bias.
Online Generalised Predictive Coding
This paper introduces Online Dynamic Expectation Maximisation (ODEM), an online extension of generalized filtering for joint state, parameter, and uncertainty estimation.
Modeling sequential cognitive states via population level cortical dynamics
This paper models sequential cognitive states using a novel neural-field system that approximates heteroclinic dynamics for brain activity patterns.
Electroencephalography and Electromyography as a Non-Invasive Biomarker of Neural Regeneration: A Review of Central and Peripheral Nervous System Injury and Regeneration
This review explores EEG and EMG as promising non-invasive, real-time biomarkers for monitoring neural regeneration after injury in both CNS and PNS.
From Cortical Synchronous Rhythm to Brain Inspired Learning Mechanism: An Oscillatory Spiking Neural Network with Time-Delayed Coordination
This paper introduces S2-Net, an oscillatory spiking neural network using time-delayed synchronization for brain-inspired learning and efficient information processing.
Measuring Understanding Through Discrete Compositional Knowledge Structures in Hierarchical Automata
This paper proposes hierarchical automata with discrete compositional knowledge structures to measure genuine understanding in AI systems.
Observable Performance Does Not Fully Reflect System Organization: A Multi-Level Analysis of Gait Dynamics Under Occlusal Constraint
Observable performance does not fully reflect system organization, as shown by multi-level analysis of gait dynamics under occlusal constraint.
Functional Connectivity-Guided Band Selection for Motor Imagery Brain-Computer Interfaces
This paper introduces a functional connectivity-guided method for selecting optimal frequency bands in motor imagery BCIs, improving decoding efficiency.
Robust volatility updates for Hierarchical Gaussian Filtering
This paper introduces a robust method for updating volatility in Hierarchical Gaussian Filtering, preventing negative posterior precision errors.
Intrinsic Brain Networks Underlying the Experience and Expression of Subclinical Anxiety
This paper shows that behavioral, physiological, and subjective subclinical anxiety map to distinct intrinsic brain networks.
SIMON: Saliency-aware Integrative Multi-view Object-centric Neural Decoding
SIMON introduces a saliency-aware multi-view framework for zero-shot EEG-to-image retrieval, achieving state-of-the-art performance by focusing on salient object regions.
CTM-AI: A Blueprint for General AI Inspired by a Model of Consciousness
CTM-AI is a blueprint for general AI, integrating a Conscious Turing Machine model with foundation models to achieve flexible, adaptive intelligence.
Multisensory learning recruits visual neurons into an olfactory memory engram
Multisensory learning recruits visual neurons into olfactory memory engrams, enhancing memory recall in Drosophila.
On Agentic Behavioral Modeling
Introduces Agentic Behavioral Modeling (ABM) to bridge AI agents and human behavior analysis, evaluating AI models as cognitive hypotheses.
Simulating Infant First-Person Sensorimotor Experience via Motion Retargeting from Babies to Humanoids
This paper introduces a framework to simulate infant first-person sensorimotor experiences by retargeting their motion from videos onto humanoids.
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