A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptation

• URL: http://arxiv.org/abs/2510.18812v1
• Date: Tue, 21 Oct 2025 17:10:15 GMT
• Title: A Unified Perspective on Optimization in Machine Learning and Neuroscience: From Gradient Descent to Neural Adaptation
• Authors: Jesús García Fernández, Nasir Ahmad, Marcel van Gerven,
• Abstract summary: Iterative optimization is central to modern artificial intelligence (AI)<n>This review provides a unified perspective on this subject, bridging classic theory with neural network training and biological learning.
• Score: 1.0851051226732167
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Iterative optimization is central to modern artificial intelligence (AI) and provides a crucial framework for understanding adaptive systems. This review provides a unified perspective on this subject, bridging classic theory with neural network training and biological learning. Although gradient-based methods, powered by the efficient but biologically implausible backpropagation (BP), dominate machine learning, their computational demands can hinder scalability in high-dimensional settings. In contrast, derivative-free or zeroth-order (ZO) optimization feature computationally lighter approaches that rely only on function evaluations and randomness. While generally less sample efficient, recent breakthroughs demonstrate that modern ZO methods can effectively approximate gradients and achieve performance competitive with BP in neural network models. This ZO paradigm is also particularly relevant for biology. Its core principles of random exploration (probing) and feedback-guided adaptation (reinforcing) parallel key mechanisms of biological learning, offering a mathematically principled perspective on how the brain learns. In this review, we begin by categorizing optimization approaches based on the order of derivative information they utilize, ranging from first-, second-, and higher-order gradient-based to ZO methods. We then explore how these methods are adapted to the unique challenges of neural network training and the resulting learning dynamics. Finally, we build upon these insights to view biological learning through an optimization lens, arguing that a ZO paradigm leverages the brain's intrinsic noise as a computational resource. This framework not only illuminates our understanding of natural intelligence but also holds vast implications for neuromorphic hardware, helping us design fast and energy-efficient AI systems that exploit intrinsic hardware noise.

Related papers

• Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning [78.88684753303794]
  Deep learning has predominantly advanced through applications in computer vision and natural language processing.<n>Neural operators are a principled way to generalize neural networks to mappings between function spaces.<n>This paper identifies and distills the key principles for constructing practical implementations of mappings between infinite-dimensional function spaces.
  arXiv  Detail & Related papers  (2025-06-12T17:59:31Z)
• Evolution imposes an inductive bias that alters and accelerates learning dynamics [49.1574468325115]
  We investigate the effect of evolutionary optimization on the learning dynamics of neural networks.<n>We combined algorithms natural selection and online learning to produce a method for evolutionarily conditioning artificial neural networks.<n>Results suggest evolution constitutes an inductive bias that tunes neural systems to enable rapid learning.
  arXiv  Detail & Related papers  (2025-05-15T18:50:57Z)
• Noise-based reward-modulated learning [1.0851051226732167]
  Biological neural systems efficiently learn from delayed rewards despite relying on noisy synaptic transmission.<n>In this work, we derive a novel noise-based learning rule to address these challenges.<n>Results highlight the potential of noise-driven, brain-inspired learning for low-power adaptive systems.
  arXiv  Detail & Related papers  (2025-03-31T11:35:23Z)
• Frankenstein Optimizer: Harnessing the Potential by Revisiting Optimization Tricks [2.932254642052481]
  We propose Frankenstein, which combines various adaptive algorithms' mechanisms.<n>Experiments on computer vision, natural language processing, few-shot learning, and scientific simulations show that Frankenstein surpasses existing adaptive algorithms and gradient descent (SGD)<n>This research deepens our understanding of adaptive algorithms through centered kernel alignment analysis and loss landscape visualization during the learning process.
  arXiv  Detail & Related papers  (2025-03-04T00:25:54Z)
• Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation [49.44309457870649]
  Layer-wise Feedback feedback (LFP) is a novel training principle for neural network-like predictors.<n>LFP decomposes a reward to individual neurons based on their respective contributions.<n>Our method then implements a greedy reinforcing approach helpful parts of the network and weakening harmful ones.
  arXiv  Detail & Related papers  (2023-08-23T10:48:28Z)
• 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)
• Towards Theoretically Inspired Neural Initialization Optimization [66.04735385415427]
  We propose a differentiable quantity, named GradCosine, with theoretical insights to evaluate the initial state of a neural network. We show that both the training and test performance of a network can be improved by maximizing GradCosine under norm constraint. Generalized from the sample-wise analysis into the real batch setting, NIO is able to automatically look for a better initialization with negligible cost.
  arXiv  Detail & Related papers  (2022-10-12T06:49:16Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• 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)
• Gradient descent in materia through homodyne gradient extraction [2.012950941269354]
  We demonstrate a simple yet efficient gradient extraction method, based on the principle of homodyne detection.<n>By perturbing the parameters that need to be optimized we effectively obtain the gradient information in a highly robust and scalable manner.<n>Homodyne gradient extraction can in principle be fully implemented in materia, facilitating the development of autonomously learning material systems.
  arXiv  Detail & Related papers  (2021-05-15T12:18:31Z)
• Predictive Coding Can Do Exact Backpropagation on Any Neural Network [40.51949948934705]
  We generalize (IL and) Z-IL by directly defining them on computational graphs. This is the first biologically plausible algorithm that is shown to be equivalent to BP in the way of updating parameters on any neural network.
  arXiv  Detail & Related papers  (2021-03-08T11:52:51Z)

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.