Stochastic Engrams for Efficient Continual Learning with Binarized Neural Networks

• URL: http://arxiv.org/abs/2503.21436v1
• Date: Thu, 27 Mar 2025 12:21:00 GMT
• Title: Stochastic Engrams for Efficient Continual Learning with Binarized Neural Networks
• Authors: Isabelle Aguilar, Luis Fernando Herbozo Contreras, Omid Kavehei,
• Abstract summary: We propose a novel approach that integrates plasticityally-activated engrams as a gating mechanism for metaplastic binarized neural networks (mBNNs)<n>Our findings demonstrate (A) an improved stability vs. a trade-off, (B) a reduced memory intensiveness, and (C) an enhanced performance in binarized architectures.
• Score: 4.014396794141682
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to learn continuously in artificial neural networks (ANNs) is often limited by catastrophic forgetting, a phenomenon in which new knowledge becomes dominant. By taking mechanisms of memory encoding in neuroscience (aka. engrams) as inspiration, we propose a novel approach that integrates stochastically-activated engrams as a gating mechanism for metaplastic binarized neural networks (mBNNs). This method leverages the computational efficiency of mBNNs combined with the robustness of probabilistic memory traces to mitigate forgetting and maintain the model's reliability. Previously validated metaplastic optimization techniques have been incorporated to enhance synaptic stability further. Compared to baseline binarized models and benchmark fully connected continual learning approaches, our method is the only strategy capable of reaching average accuracies over 20% in class-incremental scenarios and achieving comparable domain-incremental results to full precision state-of-the-art methods. Furthermore, we achieve a significant reduction in peak GPU and RAM usage, under 5% and 20%, respectively. Our findings demonstrate (A) an improved stability vs. plasticity trade-off, (B) a reduced memory intensiveness, and (C) an enhanced performance in binarized architectures. By uniting principles of neuroscience and efficient computing, we offer new insights into the design of scalable and robust deep learning systems.

Related papers

• Optimizing Deep Neural Networks using Safety-Guided Self Compression [0.0]
  This study introduces a novel safety-driven quantization framework that prunes and quantizes neural network weights. The proposed methodology is rigorously evaluated on both a convolutional neural network (CNN) and an attention-based language model. Experimental results reveal that our framework achieves up to a 2.5% enhancement in test accuracy relative to the original unquantized models.
  arXiv  Detail & Related papers  (2025-05-01T06:50:30Z)
• Super Level Sets and Exponential Decay: A Synergistic Approach to Stable Neural Network Training [0.0]
  We develop a dynamic learning rate algorithm that integrates exponential decay and advanced anti-overfitting strategies. We prove that the superlevel sets of the loss function, as influenced by our adaptive learning rate, are always connected.
  arXiv  Detail & Related papers  (2024-09-25T09:27:17Z)
• Growing Deep Neural Network Considering with Similarity between Neurons [4.32776344138537]
  We explore a novel approach of progressively increasing neuron numbers in compact models during training phases. We propose a method that reduces feature extraction biases and neuronal redundancy by introducing constraints based on neuron similarity distributions. Results on CIFAR-10 and CIFAR-100 datasets demonstrated accuracy improvement.
  arXiv  Detail & Related papers  (2024-08-23T11:16:37Z)
• Trapezoidal Gradient Descent for Effective Reinforcement Learning in Spiking Networks [10.422381897413263]
  Spiking Neural Network (SNN) with their low energy consumption characteristics and performance have garnered widespread attention. To reduce the energy consumption of practical applications of reinforcement learning, researchers have successively proposed the Pop-SAN and MDC-SAN algorithms. We propose a trapezoidal approximation gradient method to replace the spike network, which not only preserves the original stable learning state but also enhances the model's adaptability and response sensitivity under various signal dynamics.
  arXiv  Detail & Related papers  (2024-06-19T13:56:22Z)
• Incorporating Neuro-Inspired Adaptability for Continual Learning in Artificial Intelligence [59.11038175596807]
  Continual learning aims to empower artificial intelligence with strong adaptability to the real world. Existing advances mainly focus on preserving memory stability to overcome catastrophic forgetting. We propose a generic approach that appropriately attenuates old memories in parameter distributions to improve learning plasticity.
  arXiv  Detail & Related papers  (2023-08-29T02:43:58Z)
• Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation [49.44309457870649]
  We present Layer-wise Feedback Propagation (LFP), a novel training principle for neural network-like predictors. LFP decomposes a reward to individual neurons based on their respective contributions to solving a given task. Our method then implements a greedy approach reinforcing helpful parts of the network and weakening harmful ones.
  arXiv  Detail & Related papers  (2023-08-23T10:48:28Z)
• Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures [4.2903672492917755]
  We develop a biologically inspired lightweight neural network architecture that incorporates synaptic plasticity mechanisms and neuromodulation. Our approach leads to superior online continual learning performance on Split-MNIST, Split-CIFAR-10, and Split-CIFAR-100 datasets. We further demonstrate the effectiveness of our approach by integrating key design concepts into other backpropagation-based continual learning algorithms.
  arXiv  Detail & Related papers  (2023-08-08T19:12:52Z)
• A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks [53.31941519245432]
  Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
  arXiv  Detail & Related papers  (2023-05-26T02:52:12Z)
• Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks [51.92362217307946]
  Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems. PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
  arXiv  Detail & Related papers  (2023-03-03T08:17:47Z)
• ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction [82.81767856234956]
  This paper proposes a new learning framework named ConCerNet to improve the trustworthiness of the DNN based dynamics modeling. We show that our method consistently outperforms the baseline neural networks in both coordinate error and conservation metrics.
  arXiv  Detail & Related papers  (2023-02-11T21:07:30Z)
• FOSTER: Feature Boosting and Compression for Class-Incremental Learning [52.603520403933985]
  Deep neural networks suffer from catastrophic forgetting when learning new categories. We propose a novel two-stage learning paradigm FOSTER, empowering the model to learn new categories adaptively.
  arXiv  Detail & Related papers  (2022-04-10T11:38:33Z)
• Schematic Memory Persistence and Transience for Efficient and Robust Continual Learning [8.030924531643532]
  Continual learning is considered a promising step towards next-generation Artificial Intelligence (AI) It is still quite primitive, with existing works focusing primarily on avoiding (catastrophic) forgetting. We propose a novel framework for continual learning with external memory that builds on recent advances in neuroscience.
  arXiv  Detail & Related papers  (2021-05-05T14:32:47Z)
• Stochastic Markov Gradient Descent and Training Low-Bit Neural Networks [77.34726150561087]
  We introduce Gradient Markov Descent (SMGD), a discrete optimization method applicable to training quantized neural networks. We provide theoretical guarantees of algorithm performance as well as encouraging numerical results.
  arXiv  Detail & Related papers  (2020-08-25T15:48:15Z)

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.