Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective

• URL: http://arxiv.org/abs/2407.17305v2
• Date: Sun, 17 Nov 2024 07:13:40 GMT
• Title: Continual Learning with Hebbian Plasticity in Sparse and Predictive Coding Networks: A Survey and Perspective
• Authors: Ali Safa,
• Abstract summary: An emerging class of neuromorphic continual learning systems must learn to integrate new information on the fly. This survey covers a number of recent works in the field of neuromorphic continual learning based on state-of-the-art Sparse and Predictive Coding technology. It is hoped that this survey will contribute towards future research in the field of neuromorphic continual learning.
• Score: 1.3986052523534573
• License:
• Abstract: Recently, the use of bio-inspired learning techniques such as Hebbian learning and its closely-related Spike-Timing-Dependent Plasticity (STDP) variant have drawn significant attention for the design of compute-efficient AI systems that can continuously learn on-line at the edge. A key differentiating factor regarding this emerging class of neuromorphic continual learning system lies in the fact that learning must be carried using a data stream received in its natural order, as opposed to conventional gradient-based offline training, where a static training dataset is assumed available a priori and randomly shuffled to make the training set independent and identically distributed (i.i.d). In contrast, the emerging class of neuromorphic continual learning systems covered in this survey must learn to integrate new information on the fly in a non-i.i.d manner, which makes these systems subject to catastrophic forgetting. In order to build the next generation of neuromorphic AI systems that can continuously learn at the edge, a growing number of research groups are studying the use of Sparse and Predictive Coding-based Hebbian neural network architectures and the related Spiking Neural Networks (SNNs) equipped with STDP learning. However, since this research field is still emerging, there is a need for providing a holistic view of the different approaches proposed in the literature so far. To this end, this survey covers a number of recent works in the field of neuromorphic continual learning based on state-of-the-art Sparse and Predictive Coding technology; provides background theory to help interested researchers quickly learn the key concepts; and discusses important future research questions in light of the different works covered in this paper. It is hoped that this survey will contribute towards future research in the field of neuromorphic continual learning.

Related papers

• A Unified Framework for Neural Computation and Learning Over Time [56.44910327178975]
  Hamiltonian Learning is a novel unified framework for learning with neural networks "over time" It is based on differential equations that: (i) can be integrated without the need of external software solvers; (ii) generalize the well-established notion of gradient-based learning in feed-forward and recurrent networks; (iii) open to novel perspectives.
  arXiv  Detail & Related papers  (2024-09-18T14:57:13Z)
• DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks [4.041732967881764]
  Most rs-fMRI studies compute a single static functional connectivity matrix across brain regions of interest. These approaches are at risk of oversimplifying brain dynamics and lack proper consideration of the goal at hand. We propose a novel interpretable deep learning framework that learns goal-specific functional connectivity matrix directly from time series.
  arXiv  Detail & Related papers  (2024-05-19T23:35:06Z)
• When Meta-Learning Meets Online and Continual Learning: A Survey [39.53836535326121]
  meta-learning is a data-driven approach to optimize the learning algorithm. Continual learning and online learning, both of which involve incrementally updating a model with streaming data. This paper organizes various problem settings using consistent terminology and formal descriptions.
  arXiv  Detail & Related papers  (2023-11-09T09:49:50Z)
• Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computing [73.0977635031713]
  Neural-symbolic computing (NeSy) has been an active research area of Artificial Intelligence (AI) for many years. NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks.
  arXiv  Detail & Related papers  (2022-10-28T04:38:10Z)
• Initial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection [54.83707803301847]
  The research was conducted on a Formspring dataset provided in a Kaggle competition on automatic cyberbullying detection. The study confirmed the effectiveness of Neural Networks in cyberbullying detection and the correlation between classifier performance and Feature Density.
  arXiv  Detail & Related papers  (2022-06-04T03:17:15Z)
• aSTDP: A More Biologically Plausible Learning [0.0]
  We introduce approximate STDP, a new neural networks learning framework. It uses only STDP rules for supervised and unsupervised learning. It can make predictions or generate patterns in one model without additional configuration.
  arXiv  Detail & Related papers  (2022-05-22T08:12:50Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay [67.50637511633212]
  A lifelong learning agent is able to continually learn from potentially infinite streams of pattern sensory data. One major historic difficulty in building agents that adapt is that neural systems struggle to retain previously-acquired knowledge when learning from new samples. This problem is known as catastrophic forgetting (interference) and remains an unsolved problem in the domain of machine learning to this day.
  arXiv  Detail & Related papers  (2021-12-09T07:11:14Z)
• Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts [0.0]
  Two continual learning scenarios will be proposed to describe the potential challenges in this context. Deep neural networks have to learn new tasks and overcome forgetting the knowledge obtained from the old tasks as the amount of data keeps increasing in applications. Research topics are related but not limited to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc.
  arXiv  Detail & Related papers  (2021-08-24T14:59:10Z)
• Brain-Inspired Learning on Neuromorphic Substrates [5.279475826661643]
  This article provides a mathematical framework for the design of practical online learning algorithms for neuromorphic substrates. Specifically, we show a direct connection between Real-Time Recurrent Learning (RTRL) and biologically plausible learning rules for training Spiking Neural Networks (SNNs) We motivate a sparse approximation based on block-diagonal Jacobians, which reduces the algorithm's computational complexity.
  arXiv  Detail & Related papers  (2020-10-22T17:56:59Z)
• Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks [55.0627904986664]
  Spiking Neural Networks (SNNs) usetemporal spike patterns to represent and transmit information, which is not only biologically realistic but also suitable for ultra-low-power event-driven neuromorphic implementation. This paper investigates the contribution of spike timing dynamics to information encoding, synaptic plasticity and decision making, providing a new perspective to design of future DeepSNNs and neuromorphic hardware systems.
  arXiv  Detail & Related papers  (2020-03-26T11:13:07Z)

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.