Cover Image

Need is All You Need: Homeostatic Neural Networks Adapt to Concept Shift

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Man, Kingson, Damasio, Antonio, Neven, Hartmut
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Artificial Intelligence Neural and Evolutionary Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Empathic Coupling of Homeostatic States for Intrinsic Prosociality
by: Yoshida, Naoto, et al.
Published: (2024)

Cooperation Is All You Need
by: Adeel, Ahsan, et al.
Published: (2023)

From Worms to Mice: Homeostasis Maybe All You Need
by: de Lucas, Jesus Marco
Published: (2024)

Cycle is All You Need: More Is Different
by: Li, Xin
Published: (2025)

Direct Training Needs Regularisation: Anytime Optimal Inference Spiking Neural Network
by: Wu, Dengyu, et al.
Published: (2024)

Recursive Dynamics in Fast-Weights Homeostatic Reentry Networks: Toward Reflective Intelligence
by: Chae, B. G.
Published: (2025)

SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN
by: You, Kang, et al.
Published: (2024)

Position: Biology is the Challenge Physics-Informed ML Needs to Evolve
by: Martinelli, Julien
Published: (2025)

Interpretable Neural-Symbolic Concept Reasoning
by: Barbiero, Pietro, et al.
Published: (2023)

No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks
by: Fan, Feng-Lei, et al.
Published: (2024)

Artificial Neural Microcircuits as Building Blocks: Concept and Challenges
by: Walter, Andrew, et al.
Published: (2024)

Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
by: Mazandarani, Mehran, et al.
Published: (2025)

Your Network May Need to Be Rewritten: Network Adversarial Based on High-Dimensional Function Graph Decomposition
by: Su, Xiaoyan, et al.
Published: (2024)

Prototype-Grounded Concept Models for Verifiable Concept Alignment
by: Colamonaco, Stefano, et al.
Published: (2026)

Attention via Synaptic Plasticity is All You Need: A Biologically Inspired Spiking Neuromorphic Transformer
by: Mondal, Kallol, et al.
Published: (2025)

Polysemanticity and Capacity in Neural Networks
by: Scherlis, Adam, et al.
Published: (2022)

Restricted Bayesian Neural Network
by: Ganguly, Sourav, et al.
Published: (2024)

Deep Oscillatory Neural Network
by: Rohan, Nurani Rajagopal, et al.
Published: (2024)

Time Elastic Neural Networks
by: Marteau, Pierre-François
Published: (2024)

A Little Rank Goes a Long Way: Random Scaffolds with LoRA Adapters Are All You Need
by: Hazan, Hananel, et al.
Published: (2026)

Sheaf Neural Networks and biomedical applications
by: Mehrab, Aneeqa, et al.
Published: (2026)

Spark: Modular Spiking Neural Networks
by: Franco, Mario, et al.
Published: (2026)

Continuous Spiking Graph Neural Networks
by: Yin, Nan, et al.
Published: (2024)

Sleep-Based Homeostatic Regularization for Stabilizing Spike-Timing-Dependent Plasticity in Recurrent Spiking Neural Networks
by: Massey, Andreas, et al.
Published: (2026)

Relational Concept Bottleneck Models
by: Barbiero, Pietro, et al.
Published: (2023)

Invariant Representations with Stochastically Quantized Neural Networks
by: Cerrato, Mattia, et al.
Published: (2022)

Half-Space Feature Learning in Neural Networks
by: Yadav, Mahesh Lorik, et al.
Published: (2024)

Mechanistic Neural Networks for Scientific Machine Learning
by: Pervez, Adeel, et al.
Published: (2024)

Machine Unlearning using Forgetting Neural Networks
by: Hatua, Amartya, et al.
Published: (2024)

Geometry-Aware Spiking Graph Neural Network
by: Zhang, Bowen, et al.
Published: (2025)

Dynamic Spiking Framework for Graph Neural Networks
by: Yin, Nan, et al.
Published: (2023)

Structure of Artificial Neural Networks -- Empirical Investigations
by: Stier, Julian
Published: (2024)

The Enduring Dominance of Deep Neural Networks: A Critical Analysis of the Fundamental Limitations of Quantum Machine Learning and Spiking Neural Networks
by: Ishikawa, Takehiro
Published: (2025)

Adapting Rule Representation With Four-Parameter Beta Distribution for Learning Classifier Systems
by: Shiraishi, Hiroki, et al.
Published: (2025)

LLS: Local Learning Rule for Deep Neural Networks Inspired by Neural Activity Synchronization
by: Apolinario, Marco Paul E., et al.
Published: (2024)

Brain-Model Evaluations Need the NeuroAI Turing Test
by: Feather, Jenelle, et al.
Published: (2025)

DL101 Neural Network Outputs and Loss Functions
by: Berzal, Fernando
Published: (2025)

Adversarially Robust Spiking Neural Networks Through Conversion
by: Özdenizci, Ozan, et al.
Published: (2023)

Meta-Learning Loss Functions for Deep Neural Networks
by: Raymond, Christian
Published: (2024)

Uncertainty Aware Neural Network from Similarity and Sensitivity
by: Kabir, H M Dipu, et al.
Published: (2023)