Two Routes to Scalable Credit Assignment without Weight Symmetry

• URL: http://arxiv.org/abs/2003.01513v2
• Date: Thu, 25 Jun 2020 03:55:29 GMT
• Title: Two Routes to Scalable Credit Assignment without Weight Symmetry
• Authors: Daniel Kunin, Aran Nayebi, Javier Sagastuy-Brena, Surya Ganguli, Jonathan M. Bloom, Daniel L. K. Yamins
• Abstract summary: We investigate a recently proposed local learning rule that yields competitive performance with backpropagation. We find a performance and stability gap between this local rule and backpropagation that widens with increasing model depth. We then investigate several non-local learning rules that relax the need for instantaneous weight transport into a more biologically-plausible "weight estimation" process.
• Score: 25.485770386464186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The neural plausibility of backpropagation has long been disputed, primarily for its use of non-local weight transport $-$ the biologically dubious requirement that one neuron instantaneously measure the synaptic weights of another. Until recently, attempts to create local learning rules that avoid weight transport have typically failed in the large-scale learning scenarios where backpropagation shines, e.g. ImageNet categorization with deep convolutional networks. Here, we investigate a recently proposed local learning rule that yields competitive performance with backpropagation and find that it is highly sensitive to metaparameter choices, requiring laborious tuning that does not transfer across network architecture. Our analysis indicates the underlying mathematical reason for this instability, allowing us to identify a more robust local learning rule that better transfers without metaparameter tuning. Nonetheless, we find a performance and stability gap between this local rule and backpropagation that widens with increasing model depth. We then investigate several non-local learning rules that relax the need for instantaneous weight transport into a more biologically-plausible "weight estimation" process, showing that these rules match state-of-the-art performance on deep networks and operate effectively in the presence of noisy updates. Taken together, our results suggest two routes towards the discovery of neural implementations for credit assignment without weight symmetry: further improvement of local rules so that they perform consistently across architectures and the identification of biological implementations for non-local learning mechanisms.

Related papers

• Weight transport through spike timing for robust local gradients [0.5236468296934584]
  plasticity in functional neural networks is frequently expressed as gradient descent on a cost. This imposes symmetry constraints that are difficult to reconcile with local computation. We introduce spike-based alignment learning, which uses spike timing statistics to extract and correct the asymmetry between effective reciprocal connections.
  arXiv  Detail & Related papers  (2025-03-04T14:05:39Z)
• Oja's plasticity rule overcomes several challenges of training neural networks under biological constraints [0.0]
  We show that incorporating Oja's plasticity rule into error-driven training yields stable, efficient learning in feedforward and recurrent architectures. Our results show that Oja's rule preserves richer activation subspaces, mitigates exploding or vanishing signals, and improves short-term memory in recurrent networks.
  arXiv  Detail & Related papers  (2024-08-15T20:26:47Z)
• Deep Learning without Weight Symmetry [1.3812010983144802]
  Backpropagation (BP) is a foundational algorithm for training artificial neural networks. BP is often considered biologically implausible. Here we introduce the Product Feedback Alignment (PFA) algorithm.
  arXiv  Detail & Related papers  (2024-05-31T03:11:19Z)
• IF2Net: Innately Forgetting-Free Networks for Continual Learning [49.57495829364827]
  Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, we investigated how to design an Innately Forgetting-Free Network (IF2Net) IF2Net allows a single network to inherently learn unlimited mapping rules without telling task identities at test time.
  arXiv  Detail & Related papers  (2023-06-18T05:26:49Z)
• Minimizing Control for Credit Assignment with Strong Feedback [65.59995261310529]
  Current methods for gradient-based credit assignment in deep neural networks need infinitesimally small feedback signals. We combine strong feedback influences on neural activity with gradient-based learning and show that this naturally leads to a novel view on neural network optimization. We show that the use of strong feedback in DFC allows learning forward and feedback connections simultaneously, using a learning rule fully local in space and time.
  arXiv  Detail & Related papers  (2022-04-14T22:06:21Z)
• Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks [14.685237010856953]
  We develop biologically plausible training mechanisms for self-supervised learning (SSL) in deep networks. We show that learning can be performed with one of two more plausible alternatives to backpagation.
  arXiv  Detail & Related papers  (2021-09-30T12:56:57Z)
• Credit Assignment in Neural Networks through Deep Feedback Control [59.14935871979047]
  Deep Feedback Control (DFC) is a new learning method that uses a feedback controller to drive a deep neural network to match a desired output target and whose control signal can be used for credit assignment. The resulting learning rule is fully local in space and time and approximates Gauss-Newton optimization for a wide range of connectivity patterns. To further underline its biological plausibility, we relate DFC to a multi-compartment model of cortical pyramidal neurons with a local voltage-dependent synaptic plasticity rule, consistent with recent theories of dendritic processing.
  arXiv  Detail & Related papers  (2021-06-15T05:30:17Z)
• Neural Pruning via Growing Regularization [82.9322109208353]
  We extend regularization to tackle two central problems of pruning: pruning schedule and weight importance scoring. Specifically, we propose an L2 regularization variant with rising penalty factors and show it can bring significant accuracy gains. The proposed algorithms are easy to implement and scalable to large datasets and networks in both structured and unstructured pruning.
  arXiv  Detail & Related papers  (2020-12-16T20:16:28Z)
• Learning to Learn with Feedback and Local Plasticity [9.51828574518325]
  We employ meta-learning to discover networks that learn using feedback connections and local, biologically inspired learning rules. Our experiments show that meta-trained networks effectively use feedback connections to perform online credit assignment in multi-layer architectures.
  arXiv  Detail & Related papers  (2020-06-16T22:49:07Z)
• Distance-Based Regularisation of Deep Networks for Fine-Tuning [116.71288796019809]
  We develop an algorithm that constrains a hypothesis class to a small sphere centred on the initial pre-trained weights. Empirical evaluation shows that our algorithm works well, corroborating our theoretical results.
  arXiv  Detail & Related papers  (2020-02-19T16:00:47Z)
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment [84.57874289554839]
  Training deep neural networks on large-scale datasets requires significant hardware resources. Backpropagation, the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. We propose a neuro-biologically-plausible alternative to backprop that can be used to train deep networks.
  arXiv  Detail & Related papers  (2020-02-10T16:20:02Z)

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.