Neuroplasticity in Artificial Intelligence -- An Overview and Inspirations on Drop In & Out Learning

• URL: http://arxiv.org/abs/2503.21419v3
• Date: Fri, 25 Apr 2025 07:54:59 GMT
• Title: Neuroplasticity in Artificial Intelligence -- An Overview and Inspirations on Drop In & Out Learning
• Authors: Yupei Li, Manuel Milling, Björn W. Schuller,
• Abstract summary: We explore how neurogenesis, neuroapoptosis, and neuroplasticity can inspire future AI advances.<n>We introduce the concepts of dropin'' for neurogenesis and revisit dropout'' and structural pruning for neuroapoptosis.<n>We conclude by advocating for greater research efforts in this interdisciplinary domain.
• Score: 42.086960710257564
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI) has achieved new levels of performance and spread in public usage with the rise of deep neural networks (DNNs). Initially inspired by human neurons and their connections, NNs have become the foundation of AI models for many advanced architectures. However, some of the most integral processes in the human brain, particularly neurogenesis and neuroplasticity in addition to the more spread neuroapoptosis have largely been ignored in DNN architecture design. Instead, contemporary AI development predominantly focuses on constructing advanced frameworks, such as large language models, which retain a static structure of neural connections during training and inference. In this light, we explore how neurogenesis, neuroapoptosis, and neuroplasticity can inspire future AI advances. Specifically, we examine analogous activities in artificial NNs, introducing the concepts of ``dropin'' for neurogenesis and revisiting ``dropout'' and structural pruning for neuroapoptosis. We additionally suggest neuroplasticity combining the two for future large NNs in ``life-long learning'' settings following the biological inspiration. We conclude by advocating for greater research efforts in this interdisciplinary domain and identifying promising directions for future exploration.

Related papers

• Brain-like Functional Organization within Large Language Models [58.93629121400745]
  The human brain has long inspired the pursuit of artificial intelligence (AI) Recent neuroimaging studies provide compelling evidence of alignment between the computational representation of artificial neural networks (ANNs) and the neural responses of the human brain to stimuli. In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs) This framework links the AN sub-groups to FBNs, enabling the delineation of brain-like functional organization within large language models (LLMs)
  arXiv  Detail & Related papers  (2024-10-25T13:15:17Z)
• Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
  We propose a biologically-informed framework for enhancing artificial neural networks (ANNs) Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors. We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images. We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Towards Self-Assembling Artificial Neural Networks through Neural Developmental Programs [10.524752369156339]
  Biological nervous systems are created in a fundamentally different way than current artificial neural networks. By contrast, biological nervous systems are grown through a dynamic self-organizing process.
  arXiv  Detail & Related papers  (2023-07-17T01:58:52Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks [20.99799416963467]
  In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron.
  arXiv  Detail & Related papers  (2023-01-23T02:23:45Z)
• An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence [4.697611383288171]
  A kind of spiking neural network with biological interpretability is gradually receiving wide attention. This review hopes to attract different researchers and advance the development of brain-inspired intelligence and artificial intelligence.
  arXiv  Detail & Related papers  (2022-04-09T09:34:34Z)
• A brain basis of dynamical intelligence for AI and computational neuroscience [0.0]
  More brain-like capacities may demand new theories, models, and methods for designing artificial learning systems. This article was inspired by our symposium on dynamical neuroscience and machine learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.
  arXiv  Detail & Related papers  (2021-05-15T19:49:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.