Neuroscience · Life Sciences
Neurons & Cognition
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163 papers, from 31 August 2026 to 5 November 2026: 140 arXiv preprints and 23 OpenAlex records.
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Newest papers, page 5
NeuroLens: Learning Latent Embeddings of Neural Semantics from Chronic Recordings
Understanding how neural activity represents higher-order cognition and how these representations evolve over time has long been a central pursuit in neuroscience.Response Variability and Stability in Human Reasoning
Understanding how humans reason -- and how reasoning responses vary across tasks and individuals -- remains a core challenge for modeling and explanation in cognitive science.Contrastive Neural Embeddings Reveal Individual Traits Beyond Conversational Role
Contrastive representation learning is increasingly used to recover low-dimensional structure from neural recordings, but its output is typically validated by decoding accuracy rather than by the…Broken scale symmetries in undercomplete linear autoencoders
Neural network loss landscapes have many symmetries, which are preserved by gradient flow but broken by finite-stepsize stochastic gradient descent (SGD).The Score Is Not the Structure: Brain Alignment and Cross-Lingual Transfer
Researchers often support the claim that a model shares structure with the brain, or across languages, by reporting a similarity score.Inferring Multi-Timescale Neural Dynamics with Switching Linear Dynamical Systems
Neural activity often exhibits multiple timescales that can vary with behavioral states and task conditions.A foundation for systematic analysis of transformers and RNNs for tractography
Machine learning (ML) has emerged as a promising approach for improving diffusion MRI (dMRI) tractography, a task that remains limited by the intrinsic tension between local diffusion information…Future Video Generation Better Aligns with the Human Visual Cortex than Observed Video
Studying the alignment between the internal representations of vision models and the responses of the visual cortex to the same observed visual stimuli has enabled us to better understand human…Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks
A foundational principle of connectionism is that perception, action, and cognition emerge from parallel computations among simple, interconnected units that generate and rely on neural…Association profile conditioning in a set-temporal transformer for cross-session intracortical motor decoding
Intracortical motor decoders degrade across sessions because the set of recorded units changes and persisting units can alter how their firing relates to behavior.Null-model treatment of the sensory-motor boundary changes an evolutionary connectome comparison
Randomised copies of a connectome are the usual baseline for asking whether measured wiring matters, and the answer depends on what the randomisation preserves.Belief-Based Maximum Occupancy Principle and Active Inference
Intrinsic motivation plays a central role in adaptive and goal-directed behavior by conferring agents reward-independent objectives and biases useful to act in noisy and uncertain environments.Disentangling Computation in Multi-Task Neural Networks with the Green's Operator
How is computation organized and reused across tasks and time in a trained recurrent network?Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text
We find that major reported improvements in decoding words from non-invasive brain recordings are largely reproducible without any brain data.Neuromorphic Pseudo-Random Number Generators with a Low Power Hardware Implementation
Pseudo-random number generation often requires trade-offs among quality, power consumption, and bandwidth to produce unpredictable sequences of numbers.Increasing Width Allows Greedy Layer-wise Training to Rival End-to-End Backpropagation in Self-Supervised Learning
End-to-end backpropagation has been the dominant mode of training in deep learning, allowing for the coordination of parameter updates across layers of a neural network.Large-scale factor analysis shows machine intelligence is only partially interpretable
A common assumption in language model development is that cognitive abilities are organized around a general, domain-free intelligence factor, like fluid intelligence in humans.Neural Structural Reasoner: A Brain-inspired Architecture for Reasoning over Structured Knowledge
Structural reasoning, the ability to recognize and make inferences over the relational structure between objects and concepts, is a hallmark of human cognition, yet prevailing methods often collapse…From Neurons to Conversation: Speech Brain-Computer Interfaces
Speech brain-computer interfaces (BCIs) aim to restore communication by transforming neural activity related to speech, language, or communicative intent into external outputs such as text…Flattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders
Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise.