Copertina

Learn to integrate parts for whole through correlated neural variability

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhu, Zhichao, Qi, Yang, Lu, Wenlian, Feng, Jianfeng
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Neurons and Cognition Neural and Evolutionary Computing Biological Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Toward stochastic neural computing
di: Qi, Yang, et al.
Pubblicazione: (2023)

Slow Transition to Low-Dimensional Chaos in Heavy-Tailed Recurrent Neural Networks
di: Xie, Yi, et al.
Pubblicazione: (2025)

Modeling Bioelectric State Transitions in Glial Cells: An ASAL-Inspired Computational Approach to Glioblastoma Initiation
di: Pawlak, Wiktoria Agata
Pubblicazione: (2025)

Artificial intelligence as a surrogate brain: Bridging neural dynamical models and data
di: Zhang, Yinuo, et al.
Pubblicazione: (2025)

Statistics of correlations in nonlinear recurrent neural networks
di: Mato, German, et al.
Pubblicazione: (2025)

Teaching signal synchronization in deep neural networks with prospective neurons
di: Zucchet, Nicoas, et al.
Pubblicazione: (2025)

Heterogeneous quantization regularizes spiking neural network activity
di: Moyal, Roy, et al.
Pubblicazione: (2024)

Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks
di: Gygax, Julia, et al.
Pubblicazione: (2024)

Discovering alternative solutions beyond the simplicity bias in recurrent neural networks
di: Qian, William, et al.
Pubblicazione: (2025)

Emergence of robust looming selectivity via coordinated inhibitory neural computations
di: Fu, Qinbing, et al.
Pubblicazione: (2025)

A flexible framework for structural plasticity in GPU-accelerated sparse spiking neural networks
di: Knight, James C., et al.
Pubblicazione: (2025)

Unsupervised representation learning with Hebbian synaptic and structural plasticity in brain-like feedforward neural networks
di: Ravichandran, Naresh, et al.
Pubblicazione: (2024)

Adaptive Decoding via Hierarchical Neural Information Gradients in Mouse Visual Tasks
di: Feng, Jingyi, et al.
Pubblicazione: (2025)

Von Economo neurons enable reliable social skill acquisition in recurrent spiking neural networks: a computational account with clinical predictions
di: Keskin, Esila
Pubblicazione: (2026)

BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology
di: Chen, Baiyu, et al.
Pubblicazione: (2026)

The Role of Excitatory Parvalbumin-positive Neurons in the Tectofugal Pathway of Pigeon (Columba livia) Hierarchical Visual Processing
di: Lu, Shan, et al.
Pubblicazione: (2025)

A Variational Latent Equilibrium for Learning in Neuronal Circuits
di: Brandt, Simon, et al.
Pubblicazione: (2026)

Hierarchy of chaotic dynamics in random modular networks
di: Kuśmierz, Łukasz, et al.
Pubblicazione: (2024)

The Influence of Initial Connectivity on Biologically Plausible Learning
di: Liu, Weixuan, et al.
Pubblicazione: (2024)

Coin-Flipping In The Brain: Statistical Learning with Neuronal Assemblies
di: Dabagia, Max, et al.
Pubblicazione: (2024)

Smooth Exact Gradient Descent Learning in Spiking Neural Networks
di: Klos, Christian, et al.
Pubblicazione: (2023)

Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features Learning Efficacy
di: Lee, Hyunsu
Pubblicazione: (2021)

Time-Evolving Dynamical System for Learning Latent Representations of Mouse Visual Neural Activity
di: Huang, Liwei, et al.
Pubblicazione: (2024)

Biologically-Plausible Topology Improved Spiking Actor Network for Efficient Deep Reinforcement Learning
di: Zhang, Duzhen, et al.
Pubblicazione: (2024)

Embedded Silicon-Organic Integrated Neuromorphic System
di: Zheng, Shengjie, et al.
Pubblicazione: (2022)

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems
di: Shankar, Sohan, et al.
Pubblicazione: (2025)

When and Where: A Model Hippocampal Network Unifies Formation of Time Cells and Place Cells
di: Yu, Qiaorong S., et al.
Pubblicazione: (2026)

"What" x "When" working memory representations using Laplace Neural Manifolds
di: Sarkar, Aakash, et al.
Pubblicazione: (2024)

Exploring the role of structure in a time constrained decision task
di: Chaix-Eichel, Naomi, et al.
Pubblicazione: (2024)

Reply to: Limitations in odour recognition and generalisation in a neuromorphic olfactory circuit
di: Moyal, Roy, et al.
Pubblicazione: (2024)

A Spatiotemporal Perspective on Dynamical Computation in Neural Information Processing Systems
di: Keller, T. Anderson, et al.
Pubblicazione: (2024)

Evolving choice hysteresis in reinforcement learning: comparing the adaptive value of positivity bias and gradual perseveration
di: Hoxha, Isabelle, et al.
Pubblicazione: (2024)

Designing an adaptive room for captivating the collective consciousness from internal states
di: Flores-Ramírez, Adán, et al.
Pubblicazione: (2024)

A Computational Framework for Modeling Emergence of Color Vision in the Human Brain
di: Kotani, Atsunobu, et al.
Pubblicazione: (2024)

Nonlinear Neural Dynamics and Classification Accuracy in Reservoir Computing
di: Metzner, Claus, et al.
Pubblicazione: (2024)

Lu.i -- A low-cost electronic neuron for education and outreach
di: Stradmann, Yannik, et al.
Pubblicazione: (2024)

A Differentiable Model for Optimizing the Genetic Drivers of Synaptogenesis
di: Boccato, Tommaso, et al.
Pubblicazione: (2024)

Computational models of learning and synaptic plasticity
di: Tyulmankov, Danil
Pubblicazione: (2024)

Spatial embedding promotes a specific form of modularity with low entropy and heterogeneous spectral dynamics
di: Sheeran, Cornelia, et al.
Pubblicazione: (2024)

Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps
di: Kymn, Christopher J., et al.
Pubblicazione: (2024)