Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworks

• URL: http://arxiv.org/abs/2107.01729v2
• Date: Mon, 1 Nov 2021 17:35:21 GMT
• Title: Hebbian learning with gradients: Hebbian convolutional neural networks with modern deep learning frameworks
• Authors: Thomas Miconi
• Abstract summary: We show that Hebbian learning in hierarchical, convolutional neural networks can be implemented almost trivially with modern deep learning frameworks. We build Hebbian convolutional multi-layer networks for object recognition.
• Score: 2.7666483899332643
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning networks generally use non-biological learning methods. By contrast, networks based on more biologically plausible learning, such as Hebbian learning, show comparatively poor performance and difficulties of implementation. Here we show that Hebbian learning in hierarchical, convolutional neural networks can be implemented almost trivially with modern deep learning frameworks, by using specific losses whose gradients produce exactly the desired Hebbian updates. We provide expressions whose gradients exactly implement a plain Hebbian rule (dw ~= xy), Grossberg's instar rule (dw ~= y(x-w)), and Oja's rule (dw ~= y(x-yw)). As an application, we build Hebbian convolutional multi-layer networks for object recognition. We observe that higher layers of such networks tend to learn large, simple features (Gabor-like filters and blobs), explaining the previously reported decrease in decoding performance over successive layers. To combat this tendency, we introduce interventions (denser activations with sparse plasticity, pruning of connections between layers) which result in sparser learned features, massively increase performance, and allow information to increase over successive layers. We hypothesize that more advanced techniques (dynamic stimuli, trace learning, feedback connections, etc.), together with the massive computational boost offered by modern deep learning frameworks, could greatly improve the performance and biological relevance of multi-layer Hebbian networks.

Related papers

• Network Degeneracy as an Indicator of Training Performance: Comparing Finite and Infinite Width Angle Predictions [3.04585143845864]
  We show that as networks get deeper and deeper, they are more susceptible to becoming degenerate. We use a simple algorithm that can accurately predict the level of degeneracy for any given fully connected ReLU network architecture.
  arXiv  Detail & Related papers  (2023-06-02T13:02:52Z)
• Improving the Trainability of Deep Neural Networks through Layerwise Batch-Entropy Regularization [1.3999481573773072]
  We introduce and evaluate the batch-entropy which quantifies the flow of information through each layer of a neural network. We show that we can train a "vanilla" fully connected network and convolutional neural network with 500 layers by simply adding the batch-entropy regularization term to the loss function.
  arXiv  Detail & Related papers  (2022-08-01T20:31:58Z)
• Hebbian Continual Representation Learning [9.54473759331265]
  Continual Learning aims to bring machine learning into a more realistic scenario. We investigate whether biologically inspired Hebbian learning is useful for tackling continual challenges.
  arXiv  Detail & Related papers  (2022-06-28T09:21:03Z)
• Leveraging Sparse Linear Layers for Debuggable Deep Networks [86.94586860037049]
  We show how fitting sparse linear models over learned deep feature representations can lead to more debuggable neural networks. The resulting sparse explanations can help to identify spurious correlations, explain misclassifications, and diagnose model biases in vision and language tasks.
  arXiv  Detail & Related papers  (2021-05-11T08:15:25Z)
• Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks [50.684661759340145]
  Firefly neural architecture descent is a general framework for progressively and dynamically growing neural networks. We show that firefly descent can flexibly grow networks both wider and deeper, and can be applied to learn accurate but resource-efficient neural architectures. In particular, it learns networks that are smaller in size but have higher average accuracy than those learned by the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-02-17T04:47:18Z)
• PyTorch-Hebbian: facilitating local learning in a deep learning framework [67.67299394613426]
  Hebbian local learning has shown potential as an alternative training mechanism to backpropagation. We propose a framework for thorough and systematic evaluation of local learning rules in existing deep learning pipelines. The framework is used to expand the Krotov-Hopfield learning rule to standard convolutional neural networks without sacrificing accuracy.
  arXiv  Detail & Related papers  (2021-01-31T10:53:08Z)
• A Layer-Wise Information Reinforcement Approach to Improve Learning in Deep Belief Networks [0.4893345190925178]
  This paper proposes the Residual Deep Belief Network, which considers the information reinforcement layer-by-layer to improve the feature extraction and knowledge retaining. Experiments conducted over three public datasets demonstrate its robustness concerning the task of binary image classification.
  arXiv  Detail & Related papers  (2021-01-17T18:53:18Z)
• LoCo: Local Contrastive Representation Learning [93.98029899866866]
  We show that by overlapping local blocks stacking on top of each other, we effectively increase the decoder depth and allow upper blocks to implicitly send feedbacks to lower blocks. This simple design closes the performance gap between local learning and end-to-end contrastive learning algorithms for the first time.
  arXiv  Detail & Related papers  (2020-08-04T05:41:29Z)
• From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks [71.23327876898816]
  Federated learning has emerged as a technique for training ML models at the network edge by leveraging processing capabilities across the nodes that collect the data. We advocate a new learning paradigm called fog learning which will intelligently distribute ML model training across the continuum of nodes from edge devices to cloud servers.
  arXiv  Detail & Related papers  (2020-06-07T05:11:18Z)
• The large learning rate phase of deep learning: the catapult mechanism [50.23041928811575]
  We present a class of neural networks with solvable training dynamics. We find good agreement between our model's predictions and training dynamics in realistic deep learning settings. We believe our results shed light on characteristics of models trained at different learning rates.
  arXiv  Detail & Related papers  (2020-03-04T17:52:48Z)
• Introducing Fuzzy Layers for Deep Learning [5.209583609264815]
  We introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies.
  arXiv  Detail & Related papers  (2020-02-21T19:33:30Z)

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.