Neural dynamics and brain function

Neueste Paper

• Association profile conditioning in a set-temporal transformer for cross-session intracortical motor decoding

  Xinyuan Zhang, Handong Mo, Pengfei Wen, Shuang Liang, Jichang Yang, Yan Zeng, Zhongrui Wang, Han Wang · 1. Oktober 2026

  Intracortical motor decoders degrade across sessions because the set of recorded units changes and persisting units can alter how their firing relates to behavior. Most existing methods update network weights on each new session or rely on unlabeled activity, which does not directly reveal such chan…

• Which the Eye Fears: Writing with Read-Blindness Explains Massive Activations in Transformers

  Swagatam Mukhopadhyay, Vishal Vivek Saley, Vraj Parikh, Mausam · 30. September 2026

  Massive activation features (MAs) in Transformers are extreme-value residual-stream features that persist across layers despite the model's ability to suppress them. Why do they survive? Our investigation using an operator-level mechanistic analysis of attention and feed-forward (FFN) blocks reveals…

• Transformer MLP Gate Thresholds Are Couplings to a Carried Reference Direction

  Olli Tuomi · 30. September 2026

  The corpus-mean direction of a transformer's residual stream is a component shared across all inputs, and is commonly removed by mean-centering before representational analysis. We present evidence that it is a functional component: the reference against which the MLP gate population sets its operat…

• Causal pieces: analysing and improving spiking neural networks piece by piece

  Dominik Dold, Philipp Christian Petersen · 30. September 2026

  We introduce "causal pieces", a novel concept for analysing spiking neural networks (SNNs), inspired by "linear pieces" used to study expressivity and trainability in artificial neural networks (ANNs). Causal pieces partition the input and parameter space of a feedforward SNN with single-spike codin…

• Language as the Interface: Foundation-Model Contrastive Learning Links Transcriptomes and Electrophysiology

  Junbo Shen, Jinying Gao, Bo Lei · 30. September 2026

  Integrating transcriptomic and electrophysiological data is essential for building multimodal foundation models for neuroscience. Patch-seq provides paired measurements of gene expression and intrinsic electrophysiology from the same neuron, establishing a basis for training cross-modal models. Here…

• ADPTNet: Adaptive with Prescriptive Timescales Non-Linear SSM for Sequence Modelling

  Matei-Ioan Stan, Oliver Rhodes · 29. September 2026

  A central aim of neuromorphic computing is to provide a viable alternative to highly energy-intensive Transformer-based AI. However, efficient alternatives struggle to capture the set of qualities that have secured the Transformer's status as the de facto standard in sequence modelling. Any realisti…

• Purin: A Biology-inspired Mechanism for Artificial Neural Networks

  Zishu Liu, Chunbo Luo, Christos Grecos · 28. September 2026

  Artificial neural networks (ANNs) usually represent neural transmission with fixed trainable weights during a training batch, which omits short-term changes in synaptic efficacy. In addition, the discrete time-step simulation requires additional temporal processing that many conventional ANN archite…

• Error- and Prediction-Driven Motor Learning in the Cortico-Cerebellar Loop

  Ana Carolina Filipe, Rui Ponte Costa, Cl\'audia Soares · 25. September 2026

  Robust control under delayed sensory feedback remains a key challenge in both robotics and neuroscience. Classical cerebellar models explain delay compensation through forward prediction but fail to account for fast online corrections and rapid adaptation observed in biological systems. We propose…

• Nonequilibrium Phases of Repulsive Self-Attention: Chaos, Attention Condensation, and Emergent Locality

  Qucheng Gao, Zuyi Yang, Xiao Chen · 24. September 2026

  We study the nonequilibrium dynamics of a minimal recurrent transformer with $N$ normalized tokens, $Q=K=I$, and a negative value map $V=-I$. Similarity-based attention selects nearby representations, while the negative value map drives tokens away from the selected field. This feedback can continua…

• The Computational Value of Sensory-Aligned Receptive Fields Depends on Neuronal Expressivity

  Agnese Adorante, Aaron Spieler, Anna Levina · 24. September 2026

  Biological sensory neurons have selective receptive fields organized along meaningful stimulus coordinates, such as frequency, motion direction, or retinotopic position. Such structure may arise from efficient coding and biological constraints on activity, connectivity, and wiring, as computational …

• TNLearn: An Open Source Python Package for Task-based Neurons

  Meng Wang, Tieyun Li, Juntong Fan, Hanyu Pei, Jing-Xiao Liao, Yaodong Yang, Jianwei Ma, Fenglei Fan · 24. September 2026

  The brain does not rely on a single type of neuron to perform all kinds of tasks; instead, it designs different neurons for different tasks. The concept of task-based neurons represents a paradigm shift compared to task-based architectures. It argues that solving a specific problem requires customiz…

• Stable Neural Decoding Across Sessions via Task-Conditioned Latent Alignment for Brain-Machine Interfaces

  Canyang Zhao, Bolin Peng, J. Patrick Mayo, Ce Ju, Bing Liu · 24. September 2026

  Achieving stable long-term neural decoding in invasive brain-machine interfaces (BMIs) remains challenging due to variations in recorded neural populations across sessions. Current latent alignment approaches may overlook task-dependent structure during cross-session adaptation. We propose Task-Cond…

• Stable Unsupervised Continual Chunking with Sheaf SyncMap

  Xueyuan Li, Danilo Vasconcellos Vargas · 23. September 2026

  Unsupervised Continual chunking is a fundamental problem in machine learning and neuroscience, where the goal is to identify groups of states that frequently co-occur in temporal sequences. A key challenge is to form accurate chunks while maintaining their stability over time. In this work, we propo…

• A discrete generative model of neuronal spiking activity on microelectrode arrays

  Md Sayed Tanveer, Mohammed A. Mostajo-Radji, Ge Wang · 22. September 2026

  Generative models of neural activity could help characterize tissue dynamics, compare experimental conditions, and simulate population activity for applications ranging from disease and drug-response studies to closed-loop experimentation. Existing approaches, however, typically assume a fixed set o…

• BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings

  Alexandre Andre, Shivashriganesh P. Mahato, Vinam Arora, Keshav Balaji, Divyansha Lachi, Nanda H. Krishna, Jingyun Xiao, Yizi Zhang, Ximeng Mao, Wenrui Ma, Han Yu, International Brain Laboratory, Daniel Birman, Niccol\`o Bonacchi, Gaelle A. Chapuis, Joana A. Catarino, Felicia Davatolhagh, Mayo Faulkner, Laura Freitas-Silva, Fei Hu, Julia M. Huntenburg, Anup Khanal, In\^es Laranjeira, Petrina Lau, Guido T. Meijer, Nathaniel J. Miska, Jean-Paul Noel, Alejandro Pan-Vazquez, Georg Raiser, Cyrille Rossant, Karolina Z. Socha, Anne E. Urai, Miles J. Wells, Steven J. West, Olivier Winter, Blake Richards, Guillaume Lajoie, Cole Hurwitz, Mehdi Azabou, Matthew R. Whiteway, Liam Paninski, Eva L. Dyer · 21. September 2026

  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 general-purpose neural representations transferable across diverse downstream tasks. Yet, progress to…

• Big Brains and Changing Environments: Cause or Consequence?

  Sian Heesom-Green, Jonathan Shock, Geoff Nitschke · 16. September 2026

  Large brains are metabolically costly, and associations with changing environments do not imply they evolved there, as the Cognitive Buffer Hypothesis (CBH) would suggest. They may instead evolve in stable conditions and later facilitate colonization of changing environments. Using neuro-evolution i…

• Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1

  Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado, Devendra Vyas, Tommaso Salvatori · 11. September 2026

  Learning and decision-making in animals are often modeled as Bayesian processes, where sensory evidence is integrated with prior beliefs to guide behavior in the face of uncertainty. But what are the inherent neural dynamics that give rise to this ability, and how could they be replicated in computi…

• Formation of structural attractors in neuromorphic systems

  Yurii Parzhyn, Alexander Schwarzmann, Mykyta Lapin, Kostiantyn Bokhan · 9. September 2026

  This paper examines the theory of Invariant Structural Learning (ISL), which proposes a non-optimization approach to concept formation. Learning is interpreted as convergence to structural attractors in a hypergraph space, rather than as the minimization of a global loss function. The paper presents…

• Biologically Inspired Mechanisms for Facilitating Grokking in Multilayer Perceptrons

  Florin Leon · 31. August 2026

  Grokking is a delayed transition from memorization to generalization that is often accompanied by substantial reorganization of internal representations. This paper studies whether biologically inspired mechanisms, many of which are not commonly incorporated into artificial neural networks, can acti…

• The Von-Neumann State-Space Transformer for neural decoding

  Morteza Sarafyazd · 27. August 2026

  Cortical computation is strikingly low-dimensional: a handful of latent variables, carried in a neural population's activity, steer the higher-dimensional responses of individual neurons. Our aim is sample efficiency-models that decode well from limited data and at small parameter budgets. In a stan…

• RACE: Scalable Statistical Estimation of Functional Consistency in LLM Neurons

  Runyu Wang, Bo Liu, Xiaxin Zhang, Yu Han, Jiawei Cao, Xiaoye Zhang, Zhe Zhang, Yifan Yang, Peng Ping · 26. August 2026

  Discovering stable neuron behavior across entire domains remains a challenge in mechanistic interpretability. Existing methods often rely on instance-level point estimates or computationally expensive procedures, which either obscure population-level variability or limit scalable domain-wide analysi…

• Functional compatibility as a determinant of persistent neural learning

  Hossein Javidnia · 25. August 2026

  Artificial neural networks can acquire new capabilities but often damage existing ones when they continue to learn. This stability-plasticity problem has motivated replay, regularization and constrained-update methods, yet it remains unclear whether a property of incoming learning itself determines …

• Spike-based Belief Propagation in Nonlinear Dynamical Systems

  Sepideh Adamiat, Hongye Wang, Wouter M. Kouw, Bert de Vries · 21. August 2026

  This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Bayesian inference is widely regarded as a core computational principle of brain function, providing a normative framework for perception, decision-making, and lea…

• Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics

  Anima Kujur, Zahra Monfared · 18. August 2026

  Accurate reconstruction of long-duration neural recordings is challenging because local field potentials (LFPs) are high-resolution, multichannel, transient, and variable across subjects. We present PCA-DMD, a scalable operator-theoretic framework that segments LFP recordings into overlapping window…

• Measuring Structured Predictability in Neural Training Dynamics: A Cross-Regime Study

  Fanqi Wang, Weisheng Tang, Hairong Qi · 18. August 2026

  Modern deep networks are trained through long update trajectories, yet their temporal organization remains less systematically characterized than architectures, losses, or optimizers. We study short-horizon predictability as a measure of temporal redundancy: where, when, and under which training con…