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 7
AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets
In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or an overly salient memory.The Computational Value of Sensory-Aligned Receptive Fields Depends on Neuronal Expressivity
Biological sensory neurons have selective receptive fields organized along meaningful stimulus coordinates, such as frequency, motion direction, or retinotopic position.A discrete generative model of neuronal spiking activity on microelectrode arrays
Generative models of neural activity could help characterize tissue dynamics, compare experimental conditions, and simulate population activity for applications ranging from disease and…Matched-Input Estimates Differ in Sign Across Architectures: Auditing EEG Foundation Models on Motor Imagery
Pretrained EEG foundation models are increasingly proposed as general-purpose encoders for brain-computer interfaces, yet recent benchmarks disagree about when their representations transfer to…BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings
Advances in large-scale neural recording have made it possible to collect data across many animals and distributed brain regions, raising the question of whether this scale can be exploited to learn…A Mathematical Model of Motivated Emotional Mind - Cognitive Embodied System
This article presents a mathematical model of the Motivated Emotional Mind cognitive architecture developed for embodied intelligent systems.Neural noise enables accurate internal simulation of rare events
The brain needs an accurate internal model of the world to generate predictions and guide behavior.Learning Options for Compositional Motor Control with Adapter Banks
Learning flexible motor primitives is a hallmark of skilled motor control.A neural-astrocyte architecture implements a hybrid automaton for evidence accumulation
Astrocytes are non-neuronal glial cells that are receiving widespread attention due to their emerging role in neural computation.Nonlinear dynamics of random neural networks with second-order synaptic motifs
Classical theories of random neural networks typically assume independent connectivity, overlooking the local motif structures prevalent in biological circuits.URCHIN: A Horizontal Spiking Language Model for Data-Constrained Pretraining
The BabyLM challenge measures how much language a model can learn from developmentally-plausible, child-scale data rather than internet-scale corpora, yet prior language models forgo the biological…Pretraining for Sample-Efficient Neural Interfaces
Brain-computer interfaces (BCIs) decode neural activity to restore lost function.The Platonic brain bridge hypothesis: human brain networks as an architectural prior for multimodal large language models
Multimodal large language models predict brain activity, but brain alignment has been a measurement, not a design tool.EEGBind: Detecting Source-Level Interictal Epileptiform Discharges via EEG-Centric Multimodal Binding
Source-level analysis of interictal epileptiform discharges (IEDs) is relevant to presurgical evaluation and treatment planning because it helps characterize where epileptiform activity is likely to…A Bio-Plausible Visual Neural Network for Locust-Inspired Collision Perception
Locust visual systems have long served as an important biological paradigm for studying looming perception and collision avoidance.BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models
fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream…A Gradient-based yet Spike-Timing-Dependent Solution to the Feedback Learning Problem in Neural Microcircuits
The brain uses discrete spikes for dynamic computation, yet, how neural microcircuits (NMCs) solve temporal credit assignment using local spike timing remains a fundamental open question.Why shared attention vectors fail: a case for outcome-indexed tuning
Dimensional attention in learning is often implemented as a globally shared attention vector, where each stimulus dimension corresponds to a single scalar.XAI-Refine: An Automated Explanation-Knowledge Loop for Brain-Age Prediction
Brain-age prediction models are commonly evaluated by predictive accuracy, yet accurate predictions alone do not establish that a model relies on reproducible or neurobiologically supported…Phase oscillator networks with multiple and state-dependent delays: A framework for exploring white matter plasticity in neurodynamics
Network science is increasingly focused on how node dynamics influence emergent phenomena such as oscillations, waves, chimeras, and turbulence.